conclusion for covid 19 research

• Research article
• Open access
• Published: 04 June 2021

Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews

• Israel Júnior Borges do Nascimento 1 , 2 ,
• Dónal P. O’Mathúna 3 , 4 ,
• Thilo Caspar von Groote 5 ,
• Hebatullah Mohamed Abdulazeem 6 ,
• Ishanka Weerasekara 7 , 8 ,
• Ana Marusic 9 ,
• Livia Puljak   ORCID: orcid.org/0000-0002-8467-6061 10 ,
• Vinicius Tassoni Civile 11 ,
• Irena Zakarija-Grkovic 9 ,
• Tina Poklepovic Pericic 9 ,
• Alvaro Nagib Atallah 11 ,
• Santino Filoso 12 ,
• Nicola Luigi Bragazzi 13 &
• Milena Soriano Marcolino 1

On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)

BMC Infectious Diseases volume  21 , Article number:  525 ( 2021 ) Cite this article

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Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.

Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.

Conclusions

In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.

Peer Review reports

The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].

The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].

Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].

Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.

In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.

Methodology

Research question.

This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.

Study design

We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.

Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].

We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].

A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].

Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.

Eligibility criteria

Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.

No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.

No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].

Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.

Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.

Information sources

Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.

The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.

Study selection

All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.

Data collection process

We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.

We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).

We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.

The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).

We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.

Quality assessment in individual reviews

Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .

Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.

One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.

Synthesis of results

For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.

For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.

Managing overlapping systematic reviews

Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.

Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.

PRISMA flow diagram

Characteristics of included reviews

Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).

All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.

Population and study designs

Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.

Systematic review findings

The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).

A meta-analysis of the prevalence of mortality

Clinical symptoms

Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).

A meta-analysis of the prevalence of fever

A meta-analysis of the prevalence of cough

A meta-analysis of the prevalence of dyspnea

A meta-analysis of the prevalence of fatigue or myalgia

A meta-analysis of the prevalence of headache

A meta-analysis of the prevalence of gastrointestinal disorders

A meta-analysis of the prevalence of sore throat

Diagnostic aspects

Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].

Therapeutic possibilities

Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].

Laboratory and radiological findings

Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].

Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.

Quality of evidence in individual systematic reviews

Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .

Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).

Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].

This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.

Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].

The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.

Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.

All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.

We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.

The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].

Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].

Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.

Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].

Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.

Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.

Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.

Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.

Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.

Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].

In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.

Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.

In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.

Availability of data and materials

All data collected and analyzed within this study are available from the corresponding author on reasonable request.

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Acknowledgments

We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.

This research received no external funding.

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Israel Júnior Borges do Nascimento & Milena Soriano Marcolino

Medical College of Wisconsin, Milwaukee, WI, USA

Israel Júnior Borges do Nascimento

Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA

Dónal P. O’Mathúna

School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany

Thilo Caspar von Groote

Department of Sport and Health Science, Technische Universität München, Munich, Germany

Hebatullah Mohamed Abdulazeem

School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, Australia

Ishanka Weerasekara

Department of Physiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka

Cochrane Croatia, University of Split, School of Medicine, Split, Croatia

Ana Marusic, Irena Zakarija-Grkovic & Tina Poklepovic Pericic

Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000, Zagreb, Croatia

Livia Puljak

Cochrane Brazil, Evidence-Based Health Program, Universidade Federal de São Paulo, São Paulo, Brazil

Vinicius Tassoni Civile & Alvaro Nagib Atallah

Yorkville University, Fredericton, New Brunswick, Canada

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Contributions

IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.

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Correspondence to Livia Puljak .

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Supplementary Information

Additional file 1: appendix 1..

Search strategies used in the study.

Additional file 2: Appendix 2.

Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.

Additional file 3: Appendix 3.

List of excluded studies, with reasons.

Additional file 4: Appendix 4.

Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.

Additional file 5: Appendix 5.

A detailed explanation of AMSTAR scoring for each item in each review.

Additional file 6: Appendix 6.

List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).

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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4

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DOI : https://doi.org/10.1186/s12879-021-06214-4

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Our COVID-19 Research Summary - 2021         

The published literature on COVID now exceeds 211,000 papers, books, and documents, which include: 22,866 observational studies, 19,591 reviews, 1496 meta-analyses and 781 randomized control trials. These publications comprise the backdrop for our research and writing. The project began in the spring of 2020 based on a limited source of cumulative COVID-19 data and has broadened considerably. Here is what we have learned.

Our objectives remain to

• Describe trends and the geographic extent of the pandemic, including associated predictors
• Evaluate the effectiveness of vaccinations and exposure limitations.
• Provide public health perspectives.

“We know that people’s behavior, the mode of transmission, and the virus‘s characteristics all play a role but we don’t have a clear quantitative understanding of how all of these forces interact. With COVID, the biggest wild card has been human behavior.”

Dr. R. Rosenfeld , Head, Department of Machine Learning, Carnegie Mellon

Our findings along with other COVID studies are observational , focusing on numbers rather than people. Since the annual COVID mortality rate is about is only about 1 in 700, achieving reliable statistics by state would require a cohort approaching a million subjects, each of which would have to be tracked over time in order to estimate exposures. Observational studies are thus the only practical option and uncertainties about causality vs. association are inevitable.

COVID-19 trends continue to defy analysis in large part because of unpredictable variants, the latest of which is still unfolding. However, some aspects remain and will continue to dominate:

• Daily cases, deaths, and incidence of long-haul COVID can be reduced more than 10-fold by vaccination, notwithstanding deterioration over time that requires boosting. 
• Acceptance of vaccination remains a personal choice - a choice that may be associated with personal characteristics including income, education, and political perspective.
• COVID-19 comprises a major cause of death in the United States and may continue to do so.

Findings In 2021 .

January . We showed that COVID-19 cases increased 10-fold, by 30% per month, during the 2020-21 winter; deaths generally followed suit, while case fatality rates (CFRs) decreased up to 10-fold. The Northeast region had shifted from worst to best, so that urban predictor variables like population density were no longer important. Regional rates coalesced in January.

February . The regional trend analyses showed declining cases but not deaths, with steady CFRs. We reported that “No plausible hypotheses have been advanced for the order-of-magnitude increases in cases and deaths since September”, now referred to as the “winter surge”. We compared urban and rural rates and noted the shift towards higher cases, deaths, and CFRs in rural areas.

March . We found that cases and deaths declined while CFRs increased 5-fold. Regional trends, which had ranged 4-fold for cases and deaths now coalesced. We analyzed short-term deaths and found a strong day-of-week effect, probably due to reporting error, but no evidence of important holiday surges.

April . We tried explaining the cyclical behavior of cases in terms of “susceptibles;” predicting an underlying trend of 5000 new cases per million per month. By contrast, the average case rate is now about 8000 cases per million per month - 53 million cases in total. However, after vaccinations got underway and prior to the Delta variant, new cases dropped to levels similar to those at the beginning of the pandemic. CFR’s ranged from about 0.015 in northern regions to 0.05 in the Southwest by April. We showed that Caucasian and mixed-race subjects had far lower COVID death rates than persons of color, and that COVID death rates increased with age at the same rate as non-COVID deaths.

May . COVID rates remained low in May. Comparing states, we reported significant relationships between COVID rates and political preference along with situational factors like household crowding. An increase in Republican voters of 60 percentage points, used as a marker of political perspective, was associated with a doubling of cumulative cases.

June . We revisited our previous consideration of airborne virus transmission, which had been espoused by CDC and the epidemiological community. We estimated ventilation rates and concluded that exposures in a small apartment were likely worse than in subways or aircraft. We also revisited urban-rural differences in more detail and showed that regional COVID rates had continued to coalesce.

July . We did a detailed analysis of vaccination rates and benefits. Daily vaccination rates peaked in April, at about 50% higher in the Northeast than elsewhere. We showed a strong significant decrease in daily state-level cases associated with full vaccination. We estimated unvaccinated case rates to be hundred-folds higher than with full vaccinations. We compared vaccination effects with education, and air pollution concluding that such personal characteristics could also be important. We also showed a negative state-level relationship between voting Republican and cumulative vaccination rates. Interestingly, vaccination rates correlated with COVID rates in 2020  before  mass vaccinations began, and vaccinations at this time, also apparently, reduced mortality not associated with COVID.  “ Could the decision to vaccinate have been more critical than the vaccination itself? ”

August . We reported that COVID case rates showed a sharp upturn, followed by death, likely due to the arrival of the Delta variant. Death rates and cases had decreased steadily until July to about 30 per million or 10,000 per day – a level the CDC considered as a “tolerable” endemic. We have not had these low levels since then. CFRs peaked in July, growing six-fold with substantial geographic variability.

September . We examined cyclical variations in daily infection rates and found substantial heterogeneity. State-level vaccination rates predicted both cases and death; and complete vaccination decreased case and death rates about 100-fold, even in the presence of the Delta variant.

October . We compared October’s COVID rates with those of the 2018-19 influenza to obtain a public health perspective. Total COVID-19 and influenza cases were similar at about 30 million and both were controlled by vaccination.  Compare to influenza, COVID hospitalizations were 4-fold higher and deaths were 20-fold higher - COVID is clearly the more serious disease. We concluded that 178,000 lives may have been saved by COVID-19 vaccination.

November . We continued examining vaccine effects and found no difference in the real-world effectiveness of Pfizer or Moderna vaccines. We found that COVID vaccinations were associated with reduced non-COVID deaths by 3-fold. We built an empirical mathematical model of the temporal variation of cases that fit the existing data very well but grossly underestimated the current situation. We predicted that full vaccinations for the U.S. might reach 72% in the next year, but with a range of 50-90% among states.

December . Cases began a sharp upward trend at years end, with deaths lagging behind. Regional gradients shifted, with Northwest highest and Northeast lowest. Vaccination rates continued to increase slowly, led by the Northeast. Previous beneficial effects of vaccination had been overshadowed by the severity of the Delta variant. We reported that vaccine effectiveness appears to decrease substantially over time. Long-haul COVID, neglected by the epidemiology community, was inversely associated with  vaccination rates and the socioeconomic factors underlying vaccine reluctance or refusal. We estimated trends and the contributions of immunity acquired from previous infection, which we found to be statistically modest.

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By Fred Lipfert, PhD

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MINI REVIEW article

Covid-19: emergence, spread, possible treatments, and global burden.

• 1 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India
• 2 Department of Health Sciences, School of Education and Health, Cape Breton University, Sydney, NS, Canada

The Coronavirus (CoV) is a large family of viruses known to cause illnesses ranging from the common cold to acute respiratory tract infection. The severity of the infection may be visible as pneumonia, acute respiratory syndrome, and even death. Until the outbreak of SARS, this group of viruses was greatly overlooked. However, since the SARS and MERS outbreaks, these viruses have been studied in greater detail, propelling the vaccine research. On December 31, 2019, mysterious cases of pneumonia were detected in the city of Wuhan in China's Hubei Province. On January 7, 2020, the causative agent was identified as a new coronavirus (2019-nCoV), and the disease was later named as COVID-19 by the WHO. The virus spread extensively in the Wuhan region of China and has gained entry to over 210 countries and territories. Though experts suspected that the virus is transmitted from animals to humans, there are mixed reports on the origin of the virus. There are no treatment options available for the virus as such, limited to the use of anti-HIV drugs and/or other antivirals such as Remdesivir and Galidesivir. For the containment of the virus, it is recommended to quarantine the infected and to follow good hygiene practices. The virus has had a significant socio-economic impact globally. Economically, China is likely to experience a greater setback than other countries from the pandemic due to added trade war pressure, which have been discussed in this paper.

Introduction

Coronaviridae is a family of viruses with a positive-sense RNA that possess an outer viral coat. When looked at with the help of an electron microscope, there appears to be a unique corona around it. This family of viruses mainly cause respiratory diseases in humans, in the forms of common cold or pneumonia as well as respiratory infections. These viruses can infect animals as well ( 1 , 2 ). Up until the year 2003, coronavirus (CoV) had attracted limited interest from researchers. However, after the SARS (severe acute respiratory syndrome) outbreak caused by the SARS-CoV, the coronavirus was looked at with renewed interest ( 3 , 4 ). This also happened to be the first epidemic of the 21st century originating in the Guangdong province of China. Almost 10 years later, there was a MERS (Middle East respiratory syndrome) outbreak in 2012, which was caused by the MERS-CoV ( 5 , 6 ). Both SARS and MERS have a zoonotic origin and originated from bats. A unique feature of these viruses is the ability to mutate rapidly and adapt to a new host. The zoonotic origin of these viruses allows them to jump from host to host. Coronaviruses are known to use the angiotensin-converting enzyme-2 (ACE-2) receptor or the dipeptidyl peptidase IV (DPP-4) protein to gain entry into cells for replication ( 7 – 10 ).

In December 2019, almost seven years after the MERS 2012 outbreak, a novel Coronavirus (2019-nCoV) surfaced in Wuhan in the Hubei region of China. The outbreak rapidly grew and spread to neighboring countries. However, rapid communication of information and the increasing scale of events led to quick quarantine and screening of travelers, thus containing the spread of the infection. The major part of the infection was restricted to China, and a second cluster was found on a cruise ship called the Diamond Princess docked in Japan ( 11 , 12 ).

The new virus was identified to be a novel Coronavirus and was thus initially named 2019-nCoV; later, it was renamed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ( 13 ), and the disease it causes is now referred to as Coronavirus Disease-2019 (COVID-19) by the WHO. The virus was suspected to have begun its spread in the Huanan seafood wholesale market in the Wuhan region. It is possible that an animal that was carrying the virus was brought into or sold in the market, causing the spread of the virus in the crowded marketplace. One of the first claims made was in an article published in the Journal of Medical Virology ( 14 ), which identified snakes as the possible host. A second possibility was that pangolins could be the wild host of SARS-CoV-2 ( 15 ), though the most likely possibility is that the virus originated from bats ( 13 , 16 – 19 ). Increasing evidence and experts are now collectively concluding the virus had a natural origin in bats, as with previous such respiratory viruses ( 2 , 20 – 24 ).

Similarly, SARS and MERS were also suspected to originate from bats. In the case of MERS, the dromedary camel is an intermediate host ( 5 , 10 ). Bats have been known to harbor coronaviruses for quite some time now. Just as in the case of avian flu, SARS, MERS, and possibly even HIV, with increasing selection and ecological pressure due to human activities, the virus made the jump from animal to man. Humans have been encroaching increasingly into forests, and this is true over much of China, as in Africa. Combined with additional ecological pressure due to climate change, such zoonotic spillovers are now more common than ever. It is likely that the next disease X will also have such an origin ( 25 ). We have learned the importance of identification of the source organism due to the Ebola virus pandemic. Viruses are unstable organisms genetically, constantly mutating by genetic shift or drift. It is not possible to predict when a cross-species jump may occur and when a seemingly harmless variant form of the virus may turn into a deadly strain. Such an incident occurred in Reston, USA, with the Reston virus ( 26 ), an alarming reminder of this possibility. The identification of the original host helps us to contain future spreads as well as to learn about the mechanism of transmission of viruses. Until the virus is isolated from a wild animal host, in this case, mostly bats, the zoonotic origin will remain hypothetical, though likely. It should further be noted that the virus has acquired several mutations, as noted by a group in China, indicating that there are more than two strains of the virus, which may have had an impact on its pathogenicity. However, this claim remains unproven, and many experts have argued otherwise; data proving this are not yet available ( 27 ). A similar finding was reported from Italy and India independently, where they found two strains ( 28 , 29 ). These findings need to be further cross-verified by similar analyses globally. If true, this finding could effectively explain why some nations are more affected than others.

Transmission

When the spread of COVID-19 began ( Figure 1 ), the virus appeared to be contained within China and the cruise ship “Diamond Princess,” which formed the major clusters of the virus. However, as of April 2020, over 210 countries and territories are affected by the virus, with Europe, the USA, and Iran forming the new cluster of the virus. The USA ( Figure 2 ) has the highest number of confirmed COVID-19 cases, whereas India and China, despite being among the most population-dense countries in the world, have managed to constrain the infection rate by the implementation of a complete lockdown with arrangements in place to manage the confirmed cases. Similarly, the UK has also managed to maintain a low curve of the graph by implementing similar measures, though it was not strictly enforced. Reports have indicated that the presence of different strains or strands of the virus may have had an effect on the management of the infection rate of the virus ( 27 – 29 ). The disease is spread by droplet transmission. As of April 2020, the total number of infected individuals stands at around 3 million, with ~200,000 deaths and more than 1 million recoveries globally ( 30 , 34 ). The virus thus has a fatality rate of around 2% and an R 0 of 3 based on current data. However, a more recent report from the CDC, Atlanta, USA, claims that the R 0 could be as high as 5.7 ( 35 ). It has also been observed from data available from China and India that individuals likely to be infected by the virus from both these countries belong to the age groups of 20–50 years ( 36 , 37 ). In both of these countries, the working class mostly belongs to this age group, making exposure more likely. Germany and Singapore are great examples of countries with a high number of cases but low fatalities as compared to their immediate neighbors. Singapore is one of the few countries that had developed a detailed plan of action after the previous SARS outbreak to deal with a similar situation in the future, and this worked in their favor during this outbreak. Both countries took swift action after the outbreak began, with Singapore banning Chinese travelers and implementing screening and quarantine measures at a time when the WHO recommended none. They ordered the elderly and the vulnerable to strictly stay at home, and they ensured that lifesaving equipment and large-scale testing facilities were available immediately ( 38 , 39 ). Germany took similar measures by ramping up testing capacity quite early and by ensuring that all individuals had equal opportunity to get tested. This meant that young, old, and at-risk people all got tested, thus ensuring positive results early during disease progression and that most cases were mild like in Singapore, thus maintaining a lower death percentage ( 40 ). It allowed infected individuals to be identified and quarantined before they even had symptoms. Testing was carried out at multiple labs, reducing the load and providing massive scale, something which countries such as the USA did quite late and India restricted to select government and private labs. The German government also banned large gatherings and advocated social distancing to further reduce the spread, though unlike India and the USA, this was done quite late. South Korea is another example of how a nation has managed to contain the spread and transmission of the infection. South Korea and the USA both reported their first COVID-19 cases on the same day; however, the US administration downplayed the risks of the disease, unlike South Korean officials, who constantly informed their citizens about the developments of the disease using the media and a centralized messaging system. They also employed the Trace, Test, and Treat protocol to identify and isolate patients fast, whereas the USA restricted this to patients with severe infection and only later broadened this criterion, like many European countries as well as India. Unlike the USA, South Korea also has universal healthcare, ensuring free diagnostic testing.

Figure 1 . Timeline of COVID-19 progression ( 30 – 32 ).

Figure 2 . Total confirmed COVID 19 cases as of May 2020 ( 33 ).

The main mode of transmission of 2019-nCoV is human to human. As of now, animal-to-human transfer has not yet been confirmed. Asymptomatic carriers of the virus are at major risk of being superinfectors with this disease, as all those infected may not develop the disease ( 41 ). This is a concern that has been raised by nations globally, with the Indian government raising concerns on how to identify and contain asymptomatic carriers, who could account for 80% of those infected ( 42 ). Since current resources are directed towards understanding the hospitalized individuals showing symptoms, there is still a vast amount of information about asymptomatic individuals that has yet to be studied. For example, some questions that need to be answered include: Do asymptomatic individuals develop the disease at any point in time at all? Do they eventually develop antibodies? How long do they shed the virus for? Can any tissue of these individuals store the virus in a dormant state? Asymptomatic transmission is a gray area that encompasses major unknowns in COVID-19.

The main route of human-to-human transmission is by droplets, which are generated during coughing, talking, or sneezing and are then inhaled by a healthy individual. They can also be indirectly transmitted to a person when they land on surfaces that are touched by a healthy individual who may then touch their nose, mouth, or eyes, allowing the virus entry into the body. Fomites are also a common issue in such diseases ( 43 ).

Aerosol-based transmission of the virus has not yet been confirmed ( 43 ). Stool-based transmission via the fecal-oral route may also be possible since the SARS-CoV-2 has been found in patient feces ( 44 , 45 ). Some patients with COVID-19 tend to develop diarrhea, which can become a major route of transmission if proper sanitation and personal hygiene needs are not met. There is no evidence currently available to suggest intrauterine vertical transmission of the disease in pregnant women ( 46 ).

More investigation is necessary of whether climate has played any role in the containment of the infection in countries such as India, Singapore, China, and Israel, as these are significantly warmer countries as compared with the UK, the USA, and Canada ( Figure 2 ). Ideally, a warm climate should prevent the virus from surviving for longer periods of time on surfaces, reducing transmissibility.

Pathophysiology

On gaining entry via any of the mucus membranes, the single-stranded RNA-based virus enters the host cell using type 2 transmembrane serine protease (TMPRSS2) and ACE2 receptor protein, leading to fusion and endocytosis with the host cell ( 47 – 49 ). The uncoated RNA is then translated, and viral proteins are synthesized. With the help of RNA-dependant RNA polymerase, new RNA is produced for the new virions. The cell then undergoes lysis, releasing a load of new virions into the patients' body. The resultant infection causes a massive release of pro-inflammatory cytokines that causes a cytokine storm.

Clinical Presentation

The clinical presentation of the disease resembles beta coronavirus infections. The virus has an incubation time of 2–14 days, which is the reason why most patients suspected to have the illness or contact with an individual having the illness remain in quarantine for the said amount of time. Infection with SARS-CoV-2 causes severe pneumonia, intermittent fever, and cough ( 50 , 51 ). Symptoms of rhinorrhoea, pharyngitis, and sneezing have been less commonly seen. Patients often develop acute respiratory distress syndrome within 2 days of hospital admission, requiring ventilatory support. It has been observed that during this phase, the mortality tends to be high. Chest CT will show indicators of pneumonia and ground-glass opacity, a feature that has helped to improve the preliminary diagnosis ( 51 ). The primary method of diagnosis for SARS-CoV-2 is with the help of PCR. For the PCR testing, the US CDC recommends testing for the N gene, whereas the Chinese CDC recommends the use of ORF lab and N gene of the viral genome for testing. Some also rely on the radiological findings for preliminary screening ( 52 ). Additionally, immunodiagnostic tests based on the presence of antibodies can also play a role in testing. While the WHO recommends the use of these tests for research use, many countries have pre-emptively deployed the use of these tests in the hope of ramping up the rate and speed of testing ( 52 – 54 ). Later, they noticed variations among the results, causing them to stop the use of such kits; there was also debate among the experts about the sensitivity and specificity of the tests. For immunological tests, it is beneficial to test for antibodies against the virus produced by the body rather than to test for the presence of the viral proteins, since the antibodies can be present in larger titers for a longer span of time. However, the cross-reactivity of these tests with other coronavirus antibodies is something that needs verification. Biochemical parameters such as D-dimer, C-reactive protein, and variations in neutrophil and lymphocyte counts are some other parameters that can be used to make a preliminary diagnosis; however, these parameters vary in a number of diseases and thus cannot be relied upon conclusively ( 51 ). Patients with pre-existing diseases such as asthma or similar lung disorder are at higher risk, requiring life support, as are those with other diseases such as diabetes, hypertension, or obesity. Those above the age of 60 have displayed the highest mortality rate in China, a finding that is mirrored in other nations as well ( Figure 3 ) ( 55 ). If we cross-verify these findings with the population share that is above the age of 70, we find that Italy, the United Kingdom, Canada, and the USA have one of the highest elderly populations as compared to countries such as India and China ( Figure 4 ), and this also reflects the case fatality rates accordingly ( Figure 5 ) ( 33 ). This is a clear indicator that aside from comorbidities, age is also an independent risk factor for death in those infected by COVID-19. Also, in the US, it was seen that the rates of African American deaths were higher. This is probably due to the fact that the prevalence of hypertension and obesity in this community is higher than in Caucasians ( 56 , 57 ). In late April 2020, there are also claims in the US media that young patients in the US with COVID-19 may be at increased risk of stroke; however, this is yet to be proven. We know that coagulopathy is a feature of COVID-19, and thus stroke is likely in this condition ( 58 , 59 ). The main cause of death in COVID-19 patients was acute respiratory distress due to the inflammation in the linings of the lungs caused by the cytokine storm, which is seen in all non-survival cases and in respiratory failure. The resultant inflammation in the lungs, served as an entry point of further infection, associated with coagulopathy end-organ failure, septic shock, and secondary infections leading to death ( 60 – 63 ).

Figure 3 . Case fatality rate by age in selected countries as of April 2020 ( 33 ).

Figure 4 . Case fatality rate in selected countries ( 33 ).

Figure 5 . Population share above 70 years of age ( 33 ).

For COVID-19, there is no specific treatment available. The WHO announced the organization of a trial dubbed the “Solidarity” clinical trial for COVID-19 treatments ( 64 ). This is an international collaborative study that investigates the use of a few prime candidate drugs for use against COVID-19, which are discussed below. The study is designed to reduce the time taken for an RCT by over 80%. There are over 1087 studies ( Supplementary Data 1 ) for COVID-19 registered at clinicaltrials.gov , of which 657 are interventional studies ( Supplementary Data 2 ) ( 65 ). The primary focus of the interventional studies for COVID-19 has been on antimalarial drugs and antiviral agents ( Table 1 ), while over 200 studies deal with the use of different forms of oxygen therapy. Most trials focus on improvement of clinical status, reduction of viral load, time to improvement, and reduction of mortality rates. These studies cover both severe and mild cases.

Table 1 . List of therapeutic drugs under study for COVID-19 as per clinical trials registered under clinicaltrials.gov .

Use of Antimalarial Drugs Against SARS-CoV-2

The use of chloroquine for the treatment of corona virus-based infection has shown some benefit in the prevention of viral replication in the cases of SARS and MERS. However, it was not validated on a large scale in the form of a randomized control trial ( 50 , 66 – 68 ). The drugs of choice among antimalarials are Chloroquine (CQ) and Hydroxychloroquine (HCQ). The use of CQ for COVID-19 was brought to light by the Chinese, especially by the publication of a letter to the editor of Bioscience Trends by Gao et al. ( 69 ). The letter claimed that several studies found CQ to be effective against COVID-19; however, the letter did not provide many details. Immediately, over a short span of time, interest in these two agents grew globally. Early in vitro data have revealed that chloroquine can inhibit the viral replication ( 70 , 71 ).

HCQ and CQ work by raising the pH of the lysosome, the cellular organelle that is responsible for phagocytic degradation. Its function is to combine with cell contents that have been phagocytosed and break them down eventually, in some immune cells, as a downstream process to display some of the broken proteins as antigens, thus further enhancing the immune recruitment against an antigen/pathogen. The drug was to be administered alone or with azithromycin. The use of azithromycin may be advocated by the fact that it has been seen previously to have some immunomodulatory role in airway-related disease. It appears to reduce the release of pro-inflammatory cytokines in respiratory illnesses ( 72 ). However, HCQ and azithromycin are known to have a major drug interaction when co-administered, which increases the risk of QT interval prolongation ( 73 ). Quinine-based drugs are known to have adverse effects such as QT prolongation, retinal damage, hypoglycemia, and hemolysis of blood in patients with G-6-PD deficiency ( 66 ). Several preprints, including, a metanalysis now indicate that HCQ may have no benefit for severe or critically ill patients who have COVID-19 where the outcome is need for ventilation or death ( 74 , 75 ). As of April 21, 2020, after having pre-emptively recommended their use for SARS-CoV-2 infection, the US now advocates against the use of these two drugs based on the new data that has become available.

Use of Antiviral Drugs Against SARS-CoV-2

The antiviral agents are mainly those used in the case of HIV/AIDS, these being Lopinavir and Ritonavir. Other agents such as nucleoside analogs like Favipiravir, Ribavirin, Remdesivir, and Galidesivir have been tested for possible activity in the prevention of viral RNA synthesis ( 76 ). Among these drugs, Lopinavir, Ritonavir, and Remdesivir are listed in the Solidarity trial by the WHO.

Remdesivir is a nucleotide analog for adenosine that gets incorporated into the viral RNA, hindering its replication and causing chain termination. This agent was originally developed for Ebola Virus Disease ( 77 ). A study was conducted with rhesus macaques infected with SARS-CoV-2 ( 78 ). In that study, after 12 h of infection, the monkeys were treated with either Remdesivir or vehicle. The drug showed good distribution in the lungs, and the animals treated with the drug showed a better clinical score than the vehicle group. The radiological findings of the study also indicated that the animals treated with Remdesivir have less lung damage. There was a reduction in viral replication but not in virus shedding. Furthermore, there were no mutations found in the RNA polymerase sequences. A randomized clinical control study that became available in late April 2020 ( 79 ), having 158 on the Remdesivir arm and 79 on the placebo arm, found that Remdesivir reduced the time to recovery in the Remdesivir-treated arm to 11 days, while the placebo-arm recovery time was 15 days. Though this was not found to be statistically significant, the agent provided a basis for further studies. The 28-days mortality was found to be similar for both groups. This has now provided us with a basis on which to develop future molecules. The study has been supported by the National Institute of Health, USA. The authors of the study advocated for more clinical trials with Remdesivir with a larger population. Such larger studies are already in progress, and their results are awaited. Remdesivir is currently one of the drugs that hold most promise against COVID-19.

An early trial in China with Lopinavir and Ritonavir showed no benefit compared with standard clinical care ( 80 ). More studies with this drug are currently underway, including one in India ( 81 , 82 ).

Use of Convalescent Patient Plasma

Another possible option would be the use of serum from convalescent individuals, as this is known to contain antibodies that can neutralize the virus and aid in its elimination. This has been tried previously for other coronavirus infections ( 83 ). Early emerging case reports in this aspect look promising compared to other therapies that have been tried ( 84 – 87 ). A report from China indicates that five patients treated with plasma recovered and were eventually weaned off ventilators ( 84 ). They exhibited reductions in fever and viral load and improved oxygenation. The virus was not detected in the patients after 12 days of plasma transfusion. The US FDA has provided detailed recommendations for investigational COVID-19 Convalescent Plasma use ( 88 ). One of the benefits of this approach is that it can also be used for post-exposure prophylaxis. This approach is now beginning to be increasingly adopted in other countries, with over 95 trials registered on clinicaltrials.gov alone, of which at least 75 are interventional ( 89 ). The use of convalescent patient plasma, though mostly for research purposes, appears to be the best and, so far, the only successful option for treatment available.

From a future perspective, the use of monoclonal antibodies for the inhibition of the attachment of the virus to the ACE-2 receptor may be the best bet. Aside from this, ACE-2-like molecules could also be utilized to attach and inactivate the viral proteins, since inhibition of the ACE-2 receptor would not be advisable due to its negative repercussions physiologically. In the absence of drug regimens and a vaccine, the treatment is symptomatic and involves the use of non-invasive ventilation or intubation where necessary for respiratory failure patients. Patients that may go into septic shock should be managed as per existing guidelines with hemodynamic support as well as antibiotics where necessary.

The WHO has recommended that simple personal hygiene practices can be sufficient for the prevention of spread and containment of the disease ( 90 ). Practices such as frequent washing of soiled hands or the use of sanitizer for unsoiled hands help reduce transmission. Covering of mouth while sneezing and coughing, and disinfection of surfaces that are frequently touched, such as tabletops, doorknobs, and switches with 70% isopropyl alcohol or other disinfectants are broadly recommended. It is recommended that all individuals afflicted by the disease, as well as those caring for the infected, wear a mask to avoid transmission. Healthcare works are advised to wear a complete set of personal protective equipment as per WHO-provided guidelines. Fumigation of dormitories, quarantine rooms, and washing of clothes and other fomites with detergent and warm water can help get rid of the virus. Parcels and goods are not known to transmit the virus, as per information provided by the WHO, since the virus is not able to survive sufficiently in an open, exposed environment. Quarantine of infected individuals and those who have come into contact with an infected individual is necessary to further prevent transmission of the virus ( 91 ). Quarantine is an age-old archaic practice that continues to hold relevance even today for disease containment. With the quarantine being implemented on such a large scale in some countries, taking the form of a national lockdown, the question arises of its impact on the mental health of all individuals. This topic needs to be addressed, especially in countries such as India and China, where it is still a matter of partial taboo to talk about it openly within the society.

In India, the Ministry of Ayurveda, Yoga, and Naturopathy, Unani, Siddha and Homeopathy (AYUSH), which deals with the alternative forms of medicine, issued a press release that the homeopathic, drug Arsenicum album 30, can be taken on an empty stomach for 3 days to provide protection against the infection ( 92 ). It also provided a list of herbal drugs in the same press release as per Ayurvedic and Unani systems of medicine that can boost the immune system to deal with the virus. However, there is currently no evidence to support the use of these systems of medicine against COVID-19, and they need to be tested.

The prevention of the disease with the use of a vaccine would provide a more viable solution. There are no vaccines available for any of the coronaviruses, which includes SARS and MERS. The development of a vaccine, however, is in progress at a rapid pace, though it could take about a year or two. As of April 2020, no vaccine has completed the development and testing process. A popular approach has been with the use of mRNA-based vaccine ( 93 – 96 ). mRNA vaccines have the advantage over conventional vaccines in terms of production, since they can be manufactured easily and do not have to be cultured, as a virus would need to be. Alternative conventional approaches to making a vaccine against SARS-CoV-2 would include the use of live attenuated virus as well as using the isolated spike proteins of the virus. Both of these approaches are in progress for vaccine development ( 97 ). Governments across the world have poured in resources and made changes in their legislation to ensure rapid development, testing, and deployment of a vaccine.

Barriers to Treatment

Lack of transparency and poor media relations.

The lack of government transparency and poor reporting by the media have hampered the measures that could have been taken by healthcare systems globally to deal with the COVID-19 threat. The CDC, as well as the US administration, downplayed the threat and thus failed to stock up on essential supplies, ventilators, and test kits. An early warning system, if implemented, would have caused borders to be shut and early lockdowns. The WHO also delayed its response in sounding the alarm regarding the severity of the outbreak to allow nations globally to prepare for a pandemic. Singapore is a prime example where, despite the WHO not raising concerns and banning travel to and from China, a country banned travelers and took early measures, thus managing the outbreak quite well. South Korea is another example of how things may have played out had those measures by agencies been taken with transparency. Increased transparency would have allowed the healthcare sector to better prepare and reduced the load of patients they had to deal with, helping flatten the curve. The increased patient load and confusion among citizens arising from not following these practices has proved to be a barrier to providing effective treatments to patients with the disease elsewhere in the world.

Lack of Preparedness and Protocols

Despite the previous SARS outbreak teaching us important lessons and providing us with data on a potential outbreak, many nations did not take the important measures needed for a future outbreak. There was no allocation of sufficient funds for such an event. Many countries experienced severe lack of PPE, and the lockdown precautions hampered the logistics of supply and manufacturing of such essential equipment. Singapore and South Korea had protocols in place and were able to implement them at a moment's notice. The spurt of cases that Korea experienced was managed well, providing evidence to this effect. The lack of preparedness and lack of protocol in other nations has resulted in confusion as to how the treatment may be administered safely to the large volume of patients while dealing with diagnostics. Both of these factors have limited the accessibility to healthcare services due to sheer volume.

Socio-Economic Impact

During the SARS epidemic, China faced an economic setback, and experts were unsure if any recovery would be made. However, the global and domestic situation was then in China's favor, as it had a lower debt, allowing it to make a speedy recovery. This is not the case now. Global experts have a pessimistic outlook on the outcome of this outbreak ( 98 ). The fear of COVID-19 disease, lack of proper understanding of the dangers of the virus, and the misinformation spread on the social media ( 99 ) have caused a breakdown of the economic flow globally ( 100 ). An example of this is Indonesia, where a great amount of fear was expressed in responses to a survey when the nation was still free of COVID-19 ( 101 ). The pandemic has resulted in over 2.6 billion people being put under lockdown. This lockdown and the cancellation of the lunar year celebration has affected business at the local level. Hundreds of flights have been canceled, and tourism globally has been affected. Japan and Indonesia are estimated to lose over 2.44 billion dollars due to this ( 102 , 103 ). Workers are not able to work in factories, transportation in all forms is restricted, and goods are not produced or moved. The transport of finished products and raw materials out of China is low. The Economist has published US stock market details indicating that companies in the US that have Chinese roots fell, on average, 5 points on the stock market as compared to the S&P 500 index ( 104 ). Companies such as Starbucks have had to close over 4,000 outlets due to the outbreak as a precaution. Tech and pharma companies are at higher risk since they rely on China for the supply of raw materials and active pharmaceutical ingredients. Paracetamol, for one, has reported a price increase of over 40% in India ( 104 – 106 ). Mass hysteria in the market has caused selling of shares of these companies, causing a tumble in the Indian stock market. Though long-term investors will not be significantly affected, short-term traders will find themselves in soup. Politically, however, this has further bolstered support for world leaders in countries such as India, Germany, and the UK, who are achieving good approval ratings, with citizens being satisfied with the government's approach. In contrast, the ratings of US President Donald Trump have dropped due to the manner in which the COVID-19 pandemic was handled. These minor impacts may be of temporary significance, and the worst and direct impact will be on China itself ( 107 – 109 ), as the looming trade war with the USA had a negative impact on the Chinese and Asian markets. The longer production of goods continues to remain suspended, the more adversely it will affect the Chinese economy and the global markets dependent on it ( 110 ). If this disease is not contained, more and more lockdowns by multiple nations will severely affect the economy and lead to many social complications.

The appearance of the 2019 Novel Coronavirus has added and will continue to add to our understanding of viruses. The pandemic has once again tested the world's preparedness for dealing with such outbreaks. It has provided an outlook on how a massive-scale biological event can cause a socio-economic disturbance through misinformation and social media. In the coming months and years, we can expect to gain further insights into SARS-CoV-2 and COVID-19.

Author Contributions

KN: conceptualization. RK, AA, JM, and KN: investigation. RK and AA: writing—original draft preparation. KN, PN, and JM: writing—review and editing. KN: supervision.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to acknowledge the contributions made by Dr. Piya Paul Mudgal, Assistant Professor, Manipal Institute of Virology, Manipal Academy of Higher Education towards inputs provided by her during the drafting of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2020.00216/full#supplementary-material

Supplementary Data 1, 2. List of all studies registered for COVID-19 on clinicaltrials.gov .

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Keywords: 2019-nCoV, COVID-19, SARS-CoV-2, coronavirus, pandemic, SARS

Citation: Keni R, Alexander A, Nayak PG, Mudgal J and Nandakumar K (2020) COVID-19: Emergence, Spread, Possible Treatments, and Global Burden. Front. Public Health 8:216. doi: 10.3389/fpubh.2020.00216

Received: 21 February 2020; Accepted: 11 May 2020; Published: 28 May 2020.

Reviewed by:

Copyright © 2020 Keni, Alexander, Nayak, Mudgal and Nandakumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Krishnadas Nandakumar, mailnandakumar77@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Experience, Perceptions, and Recommendations Concerning COVID-19-Related Clinical Research Adjustments

Affiliations.

• 1 1Department of Internal Medicine, Division of Hematology-Oncology.
• 2 2Harold C. Simmons Comprehensive Cancer Center, and.
• 3 3Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
• PMID: 33027755
• PMCID: PMC8173586
• DOI: 10.6004/jnccn.2020.7643

Background: During the COVID-19 public health emergency, the FDA and NIH altered clinical trial requirements to protect participants and manage study conduct. Given their detailed knowledge of research protocols and regular contact with patients, clinicians, and sponsors, clinical research professionals offer important perspectives on these changes.

Methods: We developed and distributed an anonymous survey assessing COVID-19-related clinical trial adjustment experiences, perceptions, and recommendations to Clinical Research Office personnel at the Harold C. Simmons Comprehensive Cancer Center. Responses were compared using the Fisher exact test.

Results: A total of 94 of 109 contacted research personnel (87%) responded. Among these individuals, 58% had >5 years' professional experience in clinical research, and 56% had personal experience with a COVID-19-related change. Respondents perceived that these changes had a positive impact on patient safety; treatment efficacy; patient and staff experience; and communication with patients, investigators, and sponsors. More than 90% felt that positive changes should be continued after COVID-19. For remote consent, telehealth, therapy shipment, off-site diagnostics, and remote monitoring, individuals with personal experience with the specific change and individuals with >5 years' professional experience were numerically more likely to recommend continuing the adjustment, and these differences were significant for telehealth (P=.04) and therapy shipment (P=.02).

Conclusions: Clinical research professionals perceive that COVID-19-related clinical trial adjustments positively impact multiple aspects of study conduct. Those with greatest experience-both specific to COVID-19-related changes and more generally-are more likely to recommend that these adjustments continue in the future.

Publication types

• Research Support, N.I.H., Extramural
• Biomedical Research / standards*
• COVID-19 / prevention & control*
• COVID-19 / virology
• Delivery of Health Care / standards*
• Interdisciplinary Communication*
• Practice Guidelines as Topic / standards*
• SARS-CoV-2 / isolation & purification*
• Surveys and Questionnaires
• Telemedicine / methods*

Grants and funding

• UL1 TR001105/TR/NCATS NIH HHS/United States
• UG1 CA233302/CA/NCI NIH HHS/United States
• P30 CA142543/CA/NCI NIH HHS/United States
• K24 CA201543/CA/NCI NIH HHS/United States
• T32 CA124334/CA/NCI NIH HHS/United States

COVID-19 (2019 Novel Coronavirus) Research Guide

COVID-19 Research Guide Home

• Research Articles Downloadable Database
• COVID-19 Science Updates
• Databases and Journals
• Secondary Data and Statistics

From the CDC’s COVID-19 (2019 Novel Coronavirus) website :

“COVID-19 (coronavirus disease 2019) is a disease caused by a virus named SARS-CoV-2. It can be very contagious and spreads quickly. Over one million people have died from COVID-19 in the United States.

COVID-19 most often causes respiratory symptoms that can feel much like a cold, the flu, or pneumonia. COVID-19 may attack more than your lungs and respiratory system. Other parts of your body may also be affected by the disease. Most people with COVID-19 have mild symptoms, but some people become severely ill.

Some people including those with minor or no symptoms will develop Post-COVID Conditions – also called “Long COVID.”

May 11, 2023, marks the end of the federal COVID-19 PHE declaration . After this date, CDC’s authorizations to collect certain types of public health data will expire.

The latest situation summary updates are available on CDC’s web page for COVID-19 . “

This guide provides resources for researching COVID-19. In this guide you can find the following:

• The CDC Database of COVID-19 Research Articles became a collaboration with the WHO to create the WHO COVID-19 database during the pandemic to make it easier for results to be searched, downloaded, and used by researchers worldwide.
• The last version of the CDC COVID-19 database was archived and remain available on this website.  Please note that it has stopped updating as of October 9, 2020 and all new articles were integrated into the WHO COVID-19 database .  The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.
• COVID-19 Science Updates : To help inform CDC’s COVID-19 Response, as well as to help CDC staff stay up to date on the latest COVID-19 research, the Response’s Office of the Chief Medical Officer has collaborated with the CDC Office of Library Science to create a series called COVID-19 Science Update . This series, the first of its kind for a CDC emergency response, provides brief summaries of new COVID-19-related studies on many topics, including epidemiology, clinical treatment and management, laboratory science, and modeling. As of December 18, 2021, CDC has stopped production of the weekly COVID-19 Science Update.
• Selected scholarly literature databases and journals available to help you find research about COVID-19.
• Search alerts notify you when new research is published on COVID-19.
• Search alerts available for Ovid , PubMed , Scopus , and News sources .
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Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings. HHS, PHS, and CDC assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by HHS, PHS, and CDC. Opinion, findings, and conclusions expressed by the original authors of items included in these materials, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of HHS, PHS, or CDC. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by HHS, PHS, or CDC.

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Coronavirus (COVID-19) and society: what matters to people in Scotland?

Findings from an open free text survey taken to understand in greater detail how the pandemic has changed Scotland.

• This research has captured the diversity and complexity of people’s experiences.
• People’s experiences of the pandemic and their ability to stay safe has been impacted by a range of factors, including: their geographical environment, their financial situation, profession, their living situation and if they have any physical or mental health conditions.
• Even though the direct level of threat from COVID-19 has reduced (for some people), there is still concern about the longer term harm and disruption that COVID-19 has caused to people and communities, and worry about the threat of future waves of infection.
• This report captures a number of specific suggestions for support. For example, support for key workers, creating safer public environments, wide-scale financial support, greater awareness around the experiences of those who are at higher risk to COVID-19 and putting in place robust processes for learning and reflection on the impact of the pandemic.
• Public engagement in this open and unfiltered format is an essential part of making sense of people’s attitudes and behaviours within the context of their life.

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‘Landmark in survey research’: How the COVID States Project analyzed the pandemic with objectivity

Four years ago David Lazer formed the Northeastern-led effort — resulting in more than 100 cutting-edge reports and national media coverage.

David Lazer ran into a fellow Northeastern University professor Alessandro Vespignani . It was February 2020. One month before the COVID-19 shutdowns.

“I said, ‘Tell me: How bad is it going to be?’” says Lazer, University Distinguished Professor of Political Science and Computer Sciences at Northeastern. “And he laid out how bad it would be.”

They were facing a life-changing event, warned Vespignani, director of the Network Science Institute and Sternberg Family Distinguished Professor at Northeastern. SARS-CoV-2, the virus that causes COVID-19, was spreading fast throughout the U.S. and beyond just three months after its emergence in Wuhan, China.

“He talked about how things were going to shut down over the following month and how there was going to be an indefinite time of having to modify our lives in order to protect ourselves individually and collectively,” Lazer recalls of that conversation. “He really got the broad parameters spot on.

“I obviously was quite distressed. I was thinking, ‘What can I do to contribute to the moment?’”

The answer would become known as the COVID States Project , a Northeastern-led effort by four universities that would analyze newly collected data in order to make sense of the evolving and volatile COVID-19 pandemic. 

Over the next four years the project would put out more than 100 reports — all relevant to urgent issues — that were reflected by media coverage across the country. 

Sharing their expertise across a variety of fields — computational social science, network science, public opinion polling, epidemiology, public health, psychiatry, communication and political science — the researchers framed and conducted surveys that enabled them to identify national and regional trends that influenced (and were influenced by) the spread of the virus.

“It was an act of improvisation — we didn’t know exactly what we were going to do,” Lazer says. “But we felt quite committed to having a positive impact and using our tools, our skill set, to do something during this horrible moment.”Built into their real-time research was an understanding that social behaviors would play a large role in a pandemic that has claimed close to 1.2 million lives nationally, according to the Centers for Disease Control and Prevention (though there is reason to believe many more people have died ).

The project’s surveys and reports reflected national moods and trends while also providing reliable information for policymakers at a time when the future was difficult to predict.

“David, being a political scientist, told me that he had this idea that a survey would be helpful,” says Mauricio Santillana , an original member of the COVID States Project who has since joined Northeastern as director of the Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE) at the Network Science Institute. “I told him it was very appropriate because rather than seeing a population reaction to a public health crisis, the pandemic was evolving into a sociological problem — one where people were reacting more from their political views rather than scientific evidence.

“He had this idea of having a project where we could monitor people’s feelings, emotions and their changing behaviors in response to pronounced increases in COVID-19 infections and we could record their political affiliations,” adds Santillana, who was focused on mathematically modeling the pandemic. “The project became a really important tool for me to understand why things were getting worse and worse.”

Their work was based in objectivity — the need to respect all points of view while prioritizing understanding and dismissing judgment.

“By shedding light on things in a way that has visibility,” Lazer says, “one hopes that you are informing individual people who are reading about our stories in the media as well as policy elites about what decisions should be made.”

‘The best data out there’

It began with Lazer contacting colleagues at other universities. The COVID States Project became an effort coordinated by Lazer, Santillana, Matthew Baum and Roy Perlis of Harvard, Katherine Ognyanova of Rutgers and James Druckman of Northwestern. Weekly meetings were held at 10 a.m. on Fridays as the project grew to include undergraduate and postdoctoral students — all contributing on a volunteer basis.

“We went out into the field in April and we started collecting data,” Lazer says. “We realized that we could get useful results for all 50 states. We could see the numbers pile up and that was an exciting moment, like, maybe this thing can actually work.”

Northeastern provided the startup funding (and many of the volunteers, and much of the person power, as authors of project reports included three postdoctoral fellows and six students from Northeastern). Additional financial support would come from the National Science Foundation, the National Institutes of Health and other supporters that enabled the project to grow and expand. The project’s work on COVID-19 is continuing even now.

“We’re still putting out data on vaccination rates and infection rates,” says Lazer, whose team relied on a third-party vendor for online surveys that represent a new frontier for public polling. “It turns out that our data are better than the official data, because the official data are seriously flawed in important ways.”

Those official numbers can be faulty because individual states have difficulty linking residents with the number of vaccinations they’ve received, says Lazer.

The COVID States Project team has learned how to not only frame questions with the precision to deal with relevant issues, but also to re-weigh the answers to provide representative analysis.

“If you want to know the vaccination rates of a given state, I think our data are the best data out there,” Lazer says. “It’s pretty mind-blowing that we have done 1,400 to 1,500 state-level surveys.”

Initial efforts were focused on understanding the basics of the pandemic. While all 50 states were developing plans to reopen for business in June 2020, the project found that most people preferred a more cautious approach, with only 15% of respondents favoring an immediate reopening.

“The project is a landmark in survey research,” says Alexi Quintana Mathé , a fourth-year Ph.D. student working with Lazer at Northeastern. “We surveyed more than 20,000 respondents roughly every month, with viable samples in every U.S. state and good representativity of the general population. This allowed us to closely monitor behaviors, opinions and consequences of the COVID-19 pandemic across the country with a special focus on differences by state, which were particularly relevant during the pandemic.”

Their work was able to show that Black people waited longer for test results than other people in the U.S.

“It’s important to illuminate and create accountability,” Lazer says.

The project’s tracking of social distance behaviors in October 2020 helped predict which states would experience surges the following month. 

A survey in summer 2020 accurately predicted the rates of people who would submit to vaccinations when the shots became available that December. Another survey was able to show which demographic groups would be reluctant to be vaccinated.

“The team found that concerns over vaccine safety, as well as distrust, were key reasons [for reluctance],” says Kristin Lunz Trujillo, now a University of South Carolina assistant professor of political science who worked on the COVID States Project as a Northeastern postdoctoral fellow. “This report sparked a lot of other ongoing work on the project and gave a fuller picture of COVID vaccine hesitancy than what our typical survey measures provided.”

“People still needed to be convinced, and I think that was a very natural response,” says Santillana, a Northeastern professor of physics and electrical and computer engineering. “The fact that people were concerned about their health when being exposed to a vaccine is a natural thing. But that was being interpreted as, ‘Oh, then you are a denier.’ There was no room to be a normal person who wants to learn as we experience things. For me, being a mathematician and physicist and hearing my political-scientist colleagues discussing issues of trust in medical research and medical professionals, it became a multidisciplinary learning experience.”

A constructive role by academia

In the midst of their COVID-19 work, the researchers delved into other major U.S. events. They were able to identify the demographics of the widespread Black Lives Matter protests that followed the May 2020 murder of George Floyd. And they were able to show that those outdoor protests did not result in upsurges of pandemic-related illness.

“The diverse expertise of scientists on the project meant that we could investigate public health issues both broadly and deeply,” says Alauna Safarpour, a Northeastern postdoctoral contributor to the project who now serves as assistant professor of political science at Gettysburg College. “We not only analyzed misinformation related to the pandemic, vaccine skepticism and depression/mental health concerns, but also abortion attitudes, support for political violence and even racism as a public health concern.”

In anticipation of the role that mail-in ballots would play in the 2020 election, the project anticipated which state results would change as the late-arriving votes were counted.

“We had a piece predicting the shift after Election Day,” Lazer says. “We said there’s going to be a shift towards Biden in some states and it will be a very large shift — and we got the states right, we got the estimates right. 

“We were trying to prepare people that there was nothing fishy going on here. That this is what is expected.”

After the insurrection of Jan. 6, 2021, the project predicted accurately that Donald Trump would retain his influence as leader of the Republic Party.

“There were a lot of people right after Jan. 6 who said Trump is finished,” Lazer says. “We went into the field a couple of days later, did a survey and we said, ‘The [typical] Republican believes the election was stolen and says Trump’s endorsement would still matter a lot.’” 

The Supreme Court’s overturning of Roe v. Wade in June 2022 was followed by a COVID States Project report accurately forecasting a Democratic backlash .

“There’s a story here around the constructive role that academia can play in moments of crisis — the tools that we have are really quite practical,” Lazer says. “As the information ecosystem of our country has diminished — we see the news media firing people left and right — there is a role for universities to take some of that capacity for creating knowledge and translating that to help with the crises of the day.”

Next up: CHIP50

“It uncovered the impact of the social and political changes that Americans went through over the last four years at the national level, but more importantly it broke down the findings to demographic and regional groups,” says Ata Aydin Uslu , a third-year Ph.D. student at the Lazer Lab at Northeastern. “I see CSP as a successful attempt to mic up the American public. We enabled Americans to make their point to the local and federal decision-makers, and the decision-makers to make informed decisions and resource allocations — something that was of utmost importance during a once-in-a-century crisis.”

Entering its fifth year, the project is taking on an identity to reflect the changing times. The newly named Civic Health and Institutions Project, a 50 States Survey (CHIP50) is building on the lessons learned by the COVID States Project team during the pandemic.

“The idea is to institutionalize the notion of doing 50 state surveys in a federal country,” Lazer says. “We have this perspective on states that no other research ever has.”

Their ongoing work will include competitions to add questions from outside scholars, Lazer says. “We’re still going to issue reports, but less often, and we’re going to be turning more to scholarship while still trying to get that translational element of what does this mean, what people should think, what policymakers should do and so on.”

During a recent interview, as Lazer is recounting the work of the past four years via a Zoom call, his head is bobbing back and forth. When the pandemic forced him to isolate, he explains, he made a habit of working while walking a treadmill in his attic. At times he was responding to the pressures of the pandemic by working 16 hours while logging 40,000 steps daily — and developing plantar fasciitis along the way.

“All of this has made me think much more about the underlying sociological and psychological realities of how people process information — and the role that trust in particular plays,” Lazer says. “It has really shaped my thinking about what is core in understanding politics.”

Ian Thomsen is a Northeastern Global News reporter. Email him at [email protected] . Follow him on X/Twitter @IanatNU .

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• New research strengthens case to treat COVID-19 with metformin, not ivermectin

MINNEAPOLIS — Patients with COVID-19 had lower viral loads if treated with metformin, according to new University of Minnesota research that argues for broader use of the cheap anti-diabetes drug and against the controversial use of ivermectin.

Thursday’s published findings helped connect the dots and explain why metformin in a U-led clinical trial reduced the likelihood of COVID-related hospitalizations or the development of long COVID illness . The amount of virus in patients is often associated with the severity of illnesses and complications, and it was found to be almost fourfold lower in patients in the trial who took metformin vs. non-medicating placebo pills.

The results “could be a tipping point” that convinces doctors to prescribe metformin to treat COVID, said Dr. Carolyn Bramante, the lead U researcher of the drug trial. “But people don’t want to be wrong” so she predicted many will wait for results of a larger federally funded drug trial called ACTIV-6 .

The U study results also showed that metformin users were less likely to see a rebound in 10 days of their viral loads, which also can be a proxy for the development of post-COVID complications, or long COVID.

Researchers of the U-led trial, named COVID-OUT, found no statistically significant evidence of lower viral loads in participants who took ivermectin, an anti-parasitic drug that has been championed by some doctors, politicians and vaccine skeptics. A third drug, fluvoxamine, also showed no benefit.

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All three drugs had been identified early in the pandemic as promising targets, but a U computer simulation singled out metformin for its potential to disrupt the life cycle of the coronavirus that causes COVID-19.

Metformin’s benefits appeared statistically stronger in unvaccinated participants, but the drug also appeared to work for vaccinated participants. It also reduced viral loads in those infected by the alpha, delta or omicron coronavirus variants that caused distinct COVID-19 waves over the three years of the pandemic.

COVID has become something of an afterthought in 2024. Hospitalizations related to the infectious disease have plummeted since December, according to Thursday’s state update . Signs of the coronavirus in Minnesota wastewater samples were at their lowest since August.

COVID-19 related deaths have declined from 113 in February in Minnesota to 62 in March to 40 so far in April — almost all among senior citizens. Health officials warned that this is still an elevated mortality rate that has just been normalized by the earlier severity of the pandemic.

Long COVID also remains a concern: federal survey data showed that more than 7% of Minnesota adults were dealing with the lingering condition last month.

Federal health officials earlier this year urged people 65 and older at greatest risk of severe COVID to seek additional vaccine boosters . Uptake has been slow; the most recent state data showed only 3% of seniors were up to date on COVID vaccinations since the latest recommendations.

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Who do Americans feel comfortable talking to about their mental health?

Half of Americans or more say they are extremely or very comfortable talking about their mental health with a close friend (57%), an immediate family member (52%) or a mental health therapist (50%), according to a new Pew Research Center survey.

Pew Research Center conducted this analysis to understand who Americans feel comfortable talking to about their mental health and emotional well-being. For this analysis, we surveyed 10,133 U.S. adults from Feb. 7 to Feb. 11, 2024.

Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

For this study, respondents who are not married nor living with a partner were not asked about their comfort talking with a spouse or partner about their mental health and emotional well-being. Those who are not working for pay were not asked about someone they work with. Some of the items were asked of half the sample. Refer to the topline questionnaire below for details about the survey administration.

Here are the questions used for this analysis , along with responses, and the survey methodology .

In the United States, the importance of mental health and emotional well-being has grown increasingly visible, particularly in light of the mental health challenges many Americans faced during the COVID-19 pandemic . Health professionals encourage people to turn to a trusted support network to weather life’s difficult moments, so we asked U.S. adults about who they feel they can open up to about their mental health.

Still, not all Americans are comfortable talking about their mental health with people close to them or with professionals. About three-in-ten U.S. adults (31%) say they would be only somewhat comfortable talking with a close friend about their mental health, and an additional 12% would be not too or not at all comfortable with this. Similar shares say this about discussing mental health with an immediate family member or a therapist.

When it comes to other people Americans might open up to about their mental health, comfort levels vary:

Significant others are seen as a source of support for most people who are married or living with a partner. A large majority of these Americans (79%) are extremely or very comfortable talking about their mental health with their spouse or partner. This is the highest level of comfort across the types of people we asked about.

However, this source of support is not available to all adults. About four-in-ten Americans say they are not married nor living with a partner, while roughly six-in-ten say they are.

Americans who frequently attend religious services are largely comfortable discussing their mental health with faith leaders . Overall, 31% of U.S. adults say they would feel extremely or very comfortable talking about this with a spiritual or religious leader. But comfort is much higher among adults who report attending religious services at least once a week: 58% of regular attenders would be comfortable talking about their mental health with a religious leader.

Americans largely feel uncomfortable talking about their mental health with colleagues or neighbors . Nearly half of working Americans (48%) say they would feel not too or not at all comfortable talking about this with a co-worker. And roughly two-thirds of Americans overall (68%) would be uncomfortable talking about their mental health with a neighbor.

Modest differences by gender and age

We did not find large differences in comfort talking about mental health by gender or age, even though these factors are related to the likelihood of experiencing certain mental health conditions .

For example, similar shares of women (53%) and men (47%) say they are extremely or very comfortable talking to a therapist about their mental health. And women and men rank the seven sources of support included in the survey in the same order.

Differences are also modest across age groups. Adults ages 18 to 29 and those ages 65 and older express similar levels of comfort talking about their mental health with a close friend, immediate family member or therapist.

Networks of social support

Health experts say having diverse, supportive relationships can enhance emotional well-being . Our survey offers a mixed picture of the networks Americans turn to for talking about mental health.

On the one hand, nearly half of Americans (47%) say they are extremely or very comfortable having mental health conversations with three or more types of people included in the survey.

On the other hand, 15% of Americans are not extremely or very comfortable talking about their mental health with any of the types of people we asked about.

Between these two ends of the spectrum, 17% of Americans are comfortable talking about their mental health with only one of the types of people included in the survey, and 21% are comfortable doing so with two types of people.

The survey did not ask about the total number of people respondents are comfortable talking with about their mental health. Some respondents may only be comfortable with one type of contact – like a close friend – but could have more than one close friend they’d feel comfortable turning to.

Unpartnered U.S. adults are more likely than partnered U.S. adults to not be highly comfortable talking about their mental health with any of the types of people we asked about. Among Americans who are not married and don’t live with a partner, about a quarter (23%) are not extremely or very comfortable talking about their mental health with any of the people asked about in the survey. That’s larger than the share of married or partnered Americans who give this response (10%).

Note: Here are the questions used for this analysis , along with responses, and the survey methodology .

• Family & Relationships
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• Happiness & Life Satisfaction
• Medicine & Health

Giancarlo Pasquini is a research associate focusing on science and society research at Pew Research Center .

Emma Kikuchi is is a research assistant focusing on science and society research at Pew Research Center .

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Assessment and mapping of noise pollution in recreation spaces using geostatistic method after COVID-19 lockdown in Turkey

• Research Article
• Open access
• Published: 29 April 2024

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• Rifat Olgun   ORCID: orcid.org/0000-0002-5396-057X 1 , 2 ,
• Nihat Karakuş 1 , 4 ,
• Serdar Selim 3 &
• Buket Eyileten 4  

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Increased use of recreational areas after the lifting of COVID-19 pandemic restrictions has led to increased noise levels. This study aims to determine the level of noise pollution experienced in recreational areas with the increasing domestic and international tourism activities after the lifting of pandemic lockdowns, to produce spatial distribution maps of noise pollution, and to develop strategic planning suggestions for reducing noise pollution in line with the results obtained. Antalya-Konyaaltı Beach Recreation Area, the most important international tourism destination of Turkey, is determined as the study area. To determine the existing noise pollution, 31 measurement points were marked at 100 m intervals within the study area. Noise measurements were taken during the daytime (07:00–19:00), evening (19:00–23:00), and nighttime (23:00–07:00) on weekdays (Monday, Wednesday, Friday) and weekends (Sunday) over 2 months in the summer when the lockdown was lifted. In addition, the sound level at each measurement point was recorded for 15 min, while the number of vehicles passing through the area during the same period was determined. The database created as a result of measurements and observations was analyzed using statistical and geostatistical methods. After the analysis of the data, it was found that the co-kriging-stable model showed superior performance in noise mapping. Additionally, it was revealed that there is a high correlation between traffic density and noise intensity, with the highest equivalent noise level (Leq) on weekdays and weekend evenings due to traffic and user density. In conclusion, regions exposed to intense noise pollution were identified and strategic planning recommendations were developed to prevent/reduce noise sources in these identified regions.

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Noise Pollution and Urban Planning

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Introduction

The escalation of the global population, rapid urbanization, and advancements in industry and transportation contribute to the emergence of various forms of pollution, encompassing air, water, soil, and noise (Alimohammadi et al. 2013 ; Oyedepo 2013 ; Munir et al. 2021 ). These pollutants that tend to be more effective in urban areas demonstrate a gradual increase over time attributable to anthropogenic influences (Margaritis and Kang 2017a ; Rocha et al. 2017 ; Lagonigro et al. 2018 ; Hien et al. 2020 ). Recent study findings suggest that noise pollution will become one of the prominent issues among urban challenges. Highlighted in the study conducted by the European Noise Directive ( 2017 ), the adverse impact of noise pollution on the quality of life in both advanced and developing nations is underscored. Additionally, it is stated that this phenomenon negatively impacts human health, thereby constituting a significant health concern such as headaches, hypertension, cardiac issues, hearing difficulties, attentional disturbances, restlessness, and cognitive impairments (Arana et al. 2010 ; Oyedepo and Saadu 2010 ; Dal and Yugruk Akdag 2011 ; Oyedepo et al. 2019 ; Kalawapudi et al. 2020 ; Sonaviya and Tandel 2020 ; de Lima Andrade et al. 2021 ). The World Health Organization (WHO), taking into consideration the health implications of noise, has formally acknowledged ambient acoustic disturbances as an adverse pollutant impacting human health (WHO 2011 ; Begou and Kassomenos 2021 ). According to the European Environment Agency, approximately 82 million people are exposed to sound levels above 55 dB, which has a negative impact on human health (Khan et al. 2021 ).

In today’s world, the development of environmental noise in urban areas is primarily driven by transport vehicles (such as highway noise, railway noise, and airport noise), along with construction and industrial activities (Licitra et al. 2011 , 2012 ; Bozkurt 2021 ; Salem 2021 ). Another significant noise source in urban areas is recreational activities (Jakovljević et al. 2006 ). Upon examining noise pollution resulting from recreational activities, it is observed that urban tourism activities amplify noise levels, thus it is assessed as a factor augmenting the residents’ discomfort levels. Over the past years, particularly in nations with coastal proximity, problems related to this issue have been escalating. Therefore, study endeavors are being undertaken in the field of recreational noise pollution (Victoria State Government 2016 ; Akbulut Çoban et al. 2018 ; Ottoz et al. 2018 ; Petri et al. 2021 ).

Researchers conduct scientific studies to mitigate or prevent noise pollution, and various institutions and organizations analyze noise maps and formulate comprehensive action plans. Collaborative initiatives between researchers and institutions form a platform, that leads to the development of various strategies in response to study findings (Iglesias-Merchan et al. 2015 ; Bunn and Zannin 2016 ; Licitra et al. 2016 , 2017 ; Ruiz-Padillo et al. 2016 ; Gagliardi et al. 2017 ; Ozkurt et al. 2018 ; Tezel et al. 2019 ). Nevertheless, the acoustic environment in a city or region can vary temporally and spatially depending on factors such as the city’s architectural configuration, the structure of the city’s road network, weather conditions, and existing vegetation (Torija et al. 2011 ; Maruyama et al. 2013 ; Prieto Gajardo and Barrigón Morillas 2015 ). Hence, diverse methodologies and tools are available to detect and map noise pollution (Lee et al. 2014 ; Vogiatzis and Remy 2014 ; Gulliver et al. 2015 ). Noise mapping studies are conducted utilizing Geographic Information System (GIS) based software, notably widely used in the majority of European Union (EU) countries as well as in nations like Turkey, Japan, and the USA (Cai et al. 2015 ; Harman et al. 2016 ). In conjunction with the mentioned GIS-based software, interpolation techniques such as kriging, inverse distance weighted, natural neighbor, radial basis function, and spline are used for the acquisition of noise maps (Harman et al. 2016 ; Gheibi et al 2022 ; Nasser et al. 2023 ; Princess Okimiji et al. 2023 ). Noise maps are graphical representations of the spatial distribution of sound levels in a given area and provide an effective method for evaluating urban noise. Noise maps serve as a crucial resource for planning strategies aimed at reducing noise pollution (Pandya 2003 ; Licitra and Ascari 2014 ; Oyedepo et al. 2019 ; Arani et al. 2022 ; Kumari et al. 2023 ).

Widespread infectious diseases and events like natural disasters or large-scale emergencies result in restrictions on people’s pursuits and recreational activities. One example includes the “Coronavirus (COVID-19)” pandemic, which is recognized as the most severe global health crisis of the current century. The disease was first observed in the city of Wuhan, China, in December 2019 (Chen et al. 2020 ; Jones 2020 ; Wang et al. 2020 ). Subsequently, the WHO officially declared it a pandemic (global epidemic) on March 11, 2020 (Cucinotta and Vanelli 2020 ; Sülkü et al. 2021 ). While the global spread of the coronavirus continued, the initial case in Turkey was recorded on March 10, 2020 (Cakir 2020 ; Çalışkan et al. 2022 ). In alignment with global strategies, this situation revealed the need to implement some extensive measures and impose restrictions at various scales in Turkey to prevent the spread of the pandemic. Consequently, limitations were imposed on various sectors, particularly in areas such as travel, and as a natural outcome of this process, the tourism sector was directly affected (Rivas Ortega et al. 2021 ; Urfa et al. 2021 ; Rita et al. 2023 ). In this context, the city of Antalya, a prominent tourism hub in Turkey with its natural and cultural endowments, has also been negatively affected by this situation. During the summer months of June, July, and August, the number of visitors to Antalya reached 6,178,419 in 2018 and 7,291,753 in 2019. However, due to the impacts and restrictions of the COVID-19 pandemic, there was a significant decrease in 2020, and the number of visitors dropped to 1,046,559 (Türob 2020 ). However, the lifting of COVID-19 lockdowns in Turkey has resulted in an increase in tourism activities, as of July 1, 2021. Thus, Antalya’s Konyaaltı Beach, recognized among the world’s important shores, became intensively used again. Additionally, the negative developments between Russia and Ukraine have particularly caused a significant influx of people and vehicles from the relevant regions to Antalya. Within the scope of tourism activities, the increasing number of vehicles in Antalya has also contributed to the escalation of noise pollution. In this context, stemming from events that manifest periodically, it becomes crucial to thoroughly address the potential impacts of noise pollution on the quality of urban life. Additionally, it becomes a necessity to evaluate the compliance of sound with legal regulations and the implementation of effective preventative measures to ensure that sound levels remain within the boundaries of legal parameters.

This study aims to determine the level of noise pollution experienced in recreational areas with the increasing domestic and international tourism activities after the COVID-19 pandemic, to produce spatial distribution maps of noise pollution, and to develop strategic planning suggestions for reducing noise pollution in line with the results obtained. In this context, the renowned Antalya-Konyaaltı Beach Recreation Area, one of Turkey’s prominent international tourism destinations, was determined as the study area. Noise models were produced based on national and international noise legislation, and prevention/reduction suggestions were developed for the detected noise pollution and its sources.

Materials and methods

Turkey is surrounded by seas on three sides, possessing coastal cities with distinctive historical and geographical features. One such city is Antalya, situated in the south of Turkey, within the boundaries of the Mediterranean Region, and stands out as an important center for both agriculture and tourism. According to the data of the Turkish Statistical Institute for the year 2022 , the population of Antalya province is recorded as 2,688,004. Furthermore, owing to its roughly 657 km long coastlines, Antalya experiences a serious increase in population density as a result of increasing tourism activities, particularly in the summer months (Ozcelik and Sarp 2018 ; Tan et al. 2021 ). Nevertheless, in the summer of 2020, there was a 77.7% decrease in Antalya’s tourism density due to the restrictive measures imposed during the COVID-19 pandemic (Association of Turkish Travel Agencies 2020 ). Following the removal of pandemic restrictions in July 2021, the previous tourism intensity was reached again by the region. The coastal areas of Antalya have a Mediterranean climate type, characterized by hot and arid summers and warm and rainy winters. The average temperature in summer is between 30 and 34 °C, while in January, the average temperature varies between 9 and 15 °C. The average relative humidity in the province is around 64% annually. On average, 40–50 days of the year are overcast and rainy. Antalya is one of the rare regions where tourism activities can be carried out 12 months of the year with its meteorological features (Antalya Metropolitan Municipality 2021 ; The Turkish State Meteorological Service 2021 ).

Konyaaltı Beach Recreation Area, which was designated within the scope of the study area (Fig.  1 ), is located within the boundaries of Konyaaltı district in the west of Antalya. Konyaaltı’s coast is approximately 7.5 km long (Dipova 2016 ), and ranks among the important tourism and recreation milieu of the region and nation (Yiğit et al. 2022 ).

Location of study area and distribution of noise measurement points

ArcGIS basemap image was used as the base map of the study. Then, the devices used for noise measurement were calibrated and made ready for use. Two identical PCE-322A devices were used in tandem. The devices have a measurement range of 30 dB to 130 dB and their sound measurement sensitivity is ± 1.4 dB (Table  1 ). The other dataset used for analysis was the coordinate values of the measurement points. A database was created by transferring the coordinate values taken from the noise measurement points with high precision to GIS software.

The number of vehicles is another variable in the data set. The number of vehicles passing by was also monitored to determine the fluctuation in sound levels in the study area that changed based on the traffic density level at different times of the day. In this context, while the sound level at each measurement point was recorded, the number of vehicles traversing the area during the same period was determined.

Sampling point experimental procedure for noise measurement

After the evaluations made as a result of natural, cultural, and socio-economic structure features, interviews with local governments, and field observations, 31 different noise measurement points were determined at 100 m intervals. Care was taken to ensure that these points were distributed approximately equally throughout the study area. Then, the sound measuring devices were mounted on tripods 1.5 m above the ground, according to the norms determined by the “Central Pollution Control Board (CPCB)” and the “Regulation on Assessment and Management of Environmental Noise” (Koushki 1999 ; WHO 2000 ; Baaj et al. 2001 ; Öden and Bilgin 2019 ; Kalawapudi et al. 2020 ). These devices are positioned at points at least 3–3.5 m away from reflective and blocking surfaces, in accordance with the relevant legal legislation. Since meteorological factors have an impact on the values ​given by the sound meter (Miškinytė and Dėdelė 2014 ), measurements were taken on days when the wind speed was below 5 m/s and there was no rain, and the wind noise suppressing sponge that came with the device was attached to the microphone of the devices. In the measurement, “a weighted” frequency was used to evaluate the relative loudness perceived by the human ear, and the data was recorded in the system with a sample rate of 1 s. Measurements were transferred to the computer via Sound Level Meter software, where they were monitored and evaluated in real-time (Tripathy 2008 ; Munir et al. 2021 ). Measurements were carried out in the summer (01.07.2021/31.08.2021) after the lifting of COVID-19 lockdowns. Noise measurements were made during the daytime (07:00–19:00), evening (19:00–23:00), and nighttime (23:00–07:00) periods on weekdays (Monday, Wednesday, Friday) and weekends (Sunday) under the “Regulation on Evaluation and Management of Environmental Noise”. Since a set of three 15-min measurements represents daily noise levels in urban environments (Rey Gozalo et al. 2013 ; Romeu et al. 2011 ; Morillas et al. 2021 ), 15 min at each point was measured by researchers. Simultaneous measurements were made with 2 sound measuring devices of the same brand throughout. With the measurements performed, minimum noise levels ( L min ), maximum noise levels ( L max ), and equivalent noise levels ( L eq ) (Eq.  1 ) parameters for the L day , L evening , L night time periods were obtained (Princess Okimiji et al. 2023 ).

where N is the number of samples in the reference time interval, ( L eq ,T ) is the rating level specific sound level plus any adjustment for the characteristic features of the sound.

Noise pollution mapping using geostatistical models

To determine the best spatial distribution model that can be used in mapping noise pollution, the noise data measured during weekday and weekend L day , L evening , and L night time periods were digitized using the Leq parameter according to their geographical coordinates in the database created in the GIS software. The number of vehicles counted was also digitized, taking into account the locations where they were counted. Then, employing the Leq parameter data, the data of the new points were subjected to kriging interpolation, a geostatistical method that allows a more impartial and minimally variant estimation compared to other methods (Isaaks and Srivastava 1989 ; Tercan and Saraç 1998 ; Tunçay et al. 2017 ). Kriging is a minimum-variance, spatial interpolation method that makes predictions with the weighted values of neighboring data of the point or area to be predicted, utilizing spatial dependence models obtained from covariance or semi-variogram functions (Krivoruchko 2005 ). In this specific context, ordinary kriging, which is a simple and widely used approach to estimate the study variable, was preferred due to its capability to provide both prediction values and associated prediction errors (Webster and Oliver 2007 ; Oliver and Webster 2015 ; Khan et al. 2023 ; Vedurmudi et al. 2023 ). Ordinary kriging is calculated with the following Eq.  3 :

where at point \({{x}_{0}, Z}^{*}\left({x}_{0}\right)\) signifies the non-sampled value, while \(Z\left({x}_{i}\right)\) represents the value of the sample for the point \({x}_{i}\) , \({\lambda }_{i}\) is the weight of point i , and n indicates the total number of samples (Webster and Oliver 2007 ; Vedurmudi et al. 2023 ; Nasser et al. 2023 ).

In this study, co-kriging interpolation was applied to analyze the noise data along with the number of vehicles. While estimating the values of unobserved points through co-kriging interpolation, semivariogram models (Stable, Circular, and Spherical) that compute the spatial variation of regional variables were utilized (Liu et al. 2024 ). The co-kriging method is based on the ability to use non-detailed or incomplete secondary data and to take into account spatial cross-correlation between primary and secondary variables (Goovaerts 1997 ). Co-kriging was preferred within the scope of this study because it is used to estimate values at points where no observations have been made in multivariate data sets. Kriging and co-kriging interpolations were applied separately for weekday and weekend L day , L evening , and L night time periods. The mean absolute percentage error (MAPE) was used to compare the prediction accuracy of the models obtained as a result of the analyses. MAPE is expressed in statistics as a measure of the success of a prediction method in predicting (Ahlburg 1995 ; de Myttenaere et al. 2016 ; Baykal et al. 2022 ). MAPE is obtained by dividing the mean of absolute errors by the actual observation values (Ahlburg 1995 ; de Myttenaere et al. 2016 ; Molla et al. 2022 ). The equality is expressed in Eq.  4 :

where x i and y i are measured and correspond to the predicted values at location i , respectively, and n signifies the total number of observations.

MAPE ranges from 0 to positive infinity; MAPE = 0% indicates an excellent model, while MAPE > 100% indicates an inferior model. However, the flaw of MAPE is that even a small quantitative error makes the calculated value enormous when the observed value is small. Therefore, the closer the value for MAPE is to 0, the higher the accuracy of the interpolation model (Molla et al. 2022 ). Models with a MAPE value below 10% are considered “excellent,” models between 10 and 20% are considered “good,” models with a MAPE value between 20 and 50% are considered “acceptable,” and models above 50% are considered “incorrect” (Yang and Xing 2021 ; Baykal et al. 2022 ).

Statistical analysis

IBM SPSS and JMP software were used to determine the statistical significance levels and analyze the data obtained from both noise measurements and vehicle counts. In this context, descriptive statistics values for each variable, whether the data showed a normal distribution, skewness, and kurtosis values of the variables were computed and graphs were created. In the analyses, values between − 2 and + 2 for skewness and kurtosis were considered statistically significant to prove normal univariate distribution (Gravetter and Wallnau 2014 ; George and Mallery 2019 ). Furthermore, regression analysis was performed to determine the existence of a relationship between the traffic density, and noise intensity of the study area, and in cases where a correlation was identified, it aimed to measure the magnitude and direction of this relationship (Eq.  2 ).

where Y i denotes the i th response, β 0 is the intercept, β1 represents the regression coefficient, X i is the i th predictor, and ε i stands for the i th random error.

The last stage of the study involves assessing the map creation in alignment with the spatial distribution model deemed optimal based on the acquired findings. Subsequently, strategic planning recommendations are formulated, incorporating measures and planning aligned with both national and international noise pollution regulations, as well as insights gleaned from academic literature.

Results and discussion

Urbanization and noise levels.

Legal regulations in Turkey target noise control either directly or indirectly (Official Gazette of the Republic of Turkey 2010 ). However, the current legal legislation concerning noise pollution in the country can be considered inadequate on its own to effectively improve the acoustic quality of cities, as the latter depends on a multitude of factors. The main reason for the poor acoustic quality of cities in developing countries, such as Turkey, is the various confusions regarding zoning and the resulting irregular and unplanned growth. This situation causes an increase in the number of sound sources due to the lack of adequate urban planning (Zannin et al. 2001 ; da Paz and Zannin 2010 ) and leads to the concentration of construction around transportation infrastructures such as highways, train stations, and airports. Consequently, a significant portion of the urban population is exposed to prolonged periods of noise (Lee 2018 ; Traoré 2019 ; Gilani and Mir 2021 ). In this context, studies on strategic planning are important in improving the acoustic quality of cities (Munir et al. 2021 ). Studies on this subject focus on different areas. While some studies concentrate on urban traffic planning (Oiamo et al. 2018 ; Khomenko et al. 2020 ), others emphasize the design of architectural structures or facades in cities (Memoli et al. 2008 ; Wang et al. 2015 ; Montes González et al. 2018 ). However, it is not enough to examine only these features of urban layers in improving the acoustic quality of cities. In addition, the strategic planning of urban open green areas plays a crucial role in mitigating noise pollution and enhancing the welfare and health of urban residents (Kogan et al. 2018 ; Rey Gozalo et al. 2013 ; Morillas et al. 2021 ). For these reasons, Konyaaltı Beach Recreation Area was chosen as the study area, not only serving as a significant open green space for recreational activities but also as the focal point of the city where the sea and green elements seamlessly integrate.

Noise during and after the COVID-19 lockdown

Due to the restrictions imposed on indoor spaces during the COVID-19 pandemic period, the use of urban open green spaces for recreational purposes by residents has increased (Douglas et al. 2020 ; Samuelsson et al. 2020 ; Venter et al. 2020 ). However, this surge was not as pronounced as during periods of heightened domestic and international tourism activities. Analyzing data from The World Tourism Organization (UNWTO) on the global impact of the COVID-19 pandemic on the tourism sector reveals a 73% decrease in the number of international tourists in 2020 (January–December). This decline emerged as a significant factor in reducing noise pollution, particularly in cities characterized by intense tourism. In this context, a study conducted in various cities around the world, such as Barcelona (Bonet-Solà et al. 2021 ), Dublin (Basu et al. 2021 ), Buenos Aires (Said et al. 2020 ), Madrid (Asensio et al. 2020 ), and Milan (Pagès et al. 2020 ), reveals a decrease in equivalent noise levels ( L eq ). Throughout Turkey, there was a very high decrease in the number of tourists in April (99%), May (99%), and June (96%), when there were pandemic restrictions (UNWTO 2021 ). Antalya, which is described as the capital of tourism, was also affected by the decrease in the number of tourists coming to Turkey. During this period when the density was not high, residents of the region used both the sea, the beach, and the open green areas for recreational purposes without being exposed to noise pollution and in accordance with social distance rules. However, with the start of the 2021 tourism season, many local, national, and international tourists came to the region. Especially with the lifting of COVID-19 lockdowns in Turkey on July 1, 2021, tourism activities have become more intense. These intense human activities have also caused noise pollution to increase.

The relationship between noise pollution and the number of vehicles

This study, which was conducted specifically for the city of Antalya, one of the most important tourism spots in Turkey and Europe, aimed to develop strategic planning decisions within the scope of the results obtained by focusing on the detection, modeling, and mapping of noise pollution. According to the measurement results, the normality test of the measured noise data and vehicle numbers for the weekday and weekend L day , L evening , and L night time periods is shown in Fig.  2 .

Normality test performed on the measured noise data and vehicle numbers

Figure  2 shows that the skewness coefficient is between − 0.928 and 0.475, and the kurtosis coefficient is between − 1.200 and 0.162. Since the skewness coefficient of the number of vehicles is between − 0.036 and 0.713 and the kurtosis coefficient is between − 1.063 and − 0.288, these data show a normal distribution. This implies that if the kurtosis coefficient has a positive value, the data shows a steeper distribution than the normal distribution, and if it has a negative value, it shows a flatter distribution (Field 2013 ; Tabachnick and Fidell 2013 ). Since the noise measurement data and vehicle number data used in the study showed normal distribution, data normalization was not performed in the analyses.

Regression analysis was performed to determine the amount and direction of the relationship between traffic density and noise intensity of the study area, and the results are given in Fig.  3 . Accordingly, it was determined that there was a high correlation between traffic density and noise intensity between 0.79 and 0.90. Although Aletta et al. ( 2020 ) and Hemker et al. ( 2023 ) emphasize that there are potentially many factors affecting noise intensity, many studies have stated that traffic density is the main factor affecting noise intensity (Čurović et al. 2021 ; Steele and Guastavino 2021 ; Asensio et al. 2020 ). In addition, it can be said that the correlation between weekend and night data is even higher.

Regression analysis results of measured noise data and number of vehicles

Optimal geostatistical model for noise mapping

In comparing predicted values with measured values (Webster and Oliver 2007 ), MAPE was used to evaluate all models in terms of performance prediction accuracy, and the performance of the resulting models was evaluated (Fig.  4 ).

MAPE values of the models used in the analysis

When the data obtained as a result of MAPE analysis is examined, the performance of all models tested using both kriging and co-kriging analysis is excellent in terms of prediction accuracy (Fig.  4 ). However, the interpolation created with the co-kriging-stable model showed better performance than the interpolations created with other models. Because models with lower MAPE values perform better (Webster and Oliver 2007 ; Sajjadi et al. 2017 ; Yang and Xing 2021 ; Baykal et al. 2022 ; Molla et al. 2022 ). Therefore, the co-kriging-stable model, which shows the best performance in data estimation, was used for noise mapping. The noise map created according to this model is given in Fig.  5 .

Noise map of the study area created with the co-kriging-stable model

Temporal and spatial assessment

The study area has heavy road traffic, especially during the summer season, which is the tourist season. However, today, the noise level caused by this highway traffic is not as high as in previous years. Because, with the winning project implemented as a result of the Konyaaltı Coast, Architectural and Coastal Landscaping Idea Project Competition, held in 2014 for the Konyaaltı coast, the wide double-lane vehicle road was narrowed in order to obtain more green areas and pedestrian paths. Del Pizzo et al. ( 2020 ), de León et al. ( 2020 ), and Gilani and Mir ( 2021 ) stated in their studies, in addition to reducing the flow speed of vehicles, the pavement texture and material of the road are important parameters that should be taken into consideration in reducing noise pollution. In this context, speed bumps and traffic lights were positioned along the road in the project to keep the speed of vehicles under control. This caused the vehicles to reduce their speed and thus helped reduce noise pollution caused by the road. Additionally, vehicles were tried to slow down with the help of road geometry and different road pavement materials.

Noise level measurements in the study area were made at different time periods on weekdays and weekends, as the noise level in an area varies depending on vehicle traffic density and human activities (Basu et al. 2021 ). The findings show that the highest equivalent noise level ( L eq ) in the study area is during weekdays and weekend evenings (19:00 to 23:00). Because, as stated in the study conducted by Miškinytė and Dėdelė ( 2014 ) in the city of Kaunas (Lithuania), the times of the highest traffic and pedestrian density are the times when noise pollution is the highest. Especially in this time period, the extreme temperature that is the effect of the season is less, which causes people to generally choose this region as a recreational activity and increases the rate of intensive use. Due to this intense use, the noise level is high. In addition, during this time period, the music playing in cafes and clubs along the beach, loud music coming from vehicles moving on the highway, sounds coming from children’s playgrounds and sports fields, and sounds from motorcycles used in the area increase noise pollution. The renovations carried out in the businesses along the beach, the loud communication of young people in the skateboarding and skating area, and the loud entertainments of people swimming in the sea are among the noise sources of the area.

Since weekdays (07:00–19:00) are working hours, it was observed that there were fewer users and traffic density in the study area compared to weekends (07:00–19:00). For this reason, the overall noise pollution is less during the daytime on weekdays. However, noise pollution caused by traffic can sometimes be more severe during off-peak hours than during peak hours, depending on the traffic flow speed. Because during rush hours, traffic flow speed and traffic volume decrease. Thus, engine noise and road noise caused by vehicles during rush hours are reduced and traffic noise is reduced. As seen in the data obtained from the study, the time periods with the lowest equivalent noise levels ( L eq ) are weekdays and weekend night hours. On the other hand, it was observed that the Lmax value was high in the measurements made during these time intervals, especially at the roadside measurement points.

According to studies on reducing noise pollution, vegetation, regardless of its type or form, is an important material in reducing the noise level and ensuring noise control (Erol 1993 ; Mutlu 2010 ; Margaritis and Kang 2017b ). Studies have found that the leaves and green biomass of plants absorb acoustic energy and thus reduce noise (Samara and Tsitsoni 2011 ; Attenborough 2002 ; Van Renterghem et al. 2012 ; Tashakor and Chamani 2021 ). In reducing noise pollution, Pathak et al. ( 2011 ), it is more appropriate to use plants that do not shed their leaves and have dense green mass. In addition, this will make a significant contribution to the design in terms of aesthetics and functionality (Tashakor and Chamani 2021 ). When the vegetation of the study area is evaluated, it is seen that the plant population density is modest. As a result of the landscape design of the “Konyaaltı Beach, Architectural and Coastal Landscaping Idea Project Competition” implemented in the area, young and small plants were implemented in the project area. Similarly, the fact that the plants used along the driveway and walking path are young reduces the effect of the plants on reducing acoustic energy. Preferring tall and broad-leaved plant species in the revisions to be made in the work area in the future will both increase acoustic energy absorption and provide shade to the users on hot summer days. In addition, the highway and its surroundings in the study area connect many ecological focal points (city parks, urban forests, stream beds) along the east–west direction. This route should be considered the ecological corridor of the green infrastructure system on a city scale. The European Commission defines Green Infrastructure as a strategically planned network of natural and semi-natural areas with other environmental elements. This structure, designed to provide and develop a wide range of ecosystem services, is supportive of human well-being and contributes to the protection of biodiversity and climate change adaptation (Nieuwenhuijsen 2021 ). In addition to all these, this study area should be considered an ecological corridor within the scope of increasing human welfare, protecting biological diversity, improving mobility by providing connections between habitats, and producing ecosystem services, and should be planned as a part of the green infrastructure system (Liu and Russo 2021 ). This corridor, which will be defined as a result of such a planning approach, would be able to achieve many ecological benefits in addition to the essential role it plays in reducing noise pollution.

Conclusions

After the lifting of the COVID-19 lockdowns, noise pollution has become an important problem in recreation areas due to increased user and traffic density. The findings of the study show that there is a strong positive correlation between traffic density and noise level in Konyaaltı Beach Recreation Area and that these hours constitute the noisiest time intervals since traffic and user density are highest between 19:00 and 23:00 on weekdays and weekends. In addition, it was determined that the co-kriging-stable model is the best model used in the creation of spatial distribution maps of noise and the spatial distribution maps of the noise in the study area were created with this model.

There are some limitations to this study though. One of them is that it could not be monitored the seasonal variation in noise levels since noise levels were measured during the summer period after the lifting of pandemic lockdowns in Turkey. Another limitation is that the noise measurement could not be carried out during the whole day and with more noise monitoring stations since the study was conducted with limited resources. Also, for this reason, all instantaneous variables affecting the noise level could not be included in the study. In future studies, increasing the sample size and the number of variables affecting the noise level, monitoring noise levels seasonally or periodically, and creating noise maps of certain time intervals to determine the spatial distribution of noise are recommended for a better understanding of noise dynamics in developing cities.

Overall, incentives should be provided to relevant institutions/organizations to close down motor vehicle traffic causing noise pollution at Konyaaltı Beach Recreation Area, which is an important recreational activity area of the region, and transportation systems and facilities should be built for this purpose. The recreational value of the study area will be enhanced by converting the existing vehicle road into bicycle and walking paths, increasing green areas, and planning vegetation belts between road traffic and recreation areas. In conclusion, this study is expected to contribute to researchers and decision-makers for studies to be carried out in tackling noise pollution, which varies dynamically according to urbanization and demographic changes.

Data availability

The datasets used and/or analyzed during the current study are available upon reasonable request.

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Republicans Step Up Attacks on Scientist at Heart of Lab Leak Theory

A heated hearing produced no new evidence that Peter Daszak or his nonprofit, EcoHealth Alliance, were implicated in the Covid outbreak.

By Benjamin Mueller

House Republicans demanded on Wednesday that the president of a virus-hunting nonprofit group be criminally investigated and barred from federal research funds, a sharp escalation in their campaign against scientists in the United States who Republicans have suggested either had links to the origin of the Covid-19 pandemic or obscured its true beginnings.

During a heated three-hour hearing of the Select Subcommittee on the Coronavirus Pandemic, Republican lawmakers at times raised their voices at the nonprofit’s leader, Peter Daszak, and said that they believed that he would fare poorly as a defendant in criminal court.

The nonprofit, EcoHealth Alliance, which receives federal funding to study global threats from wild animal viruses, has faced suspicion over a proposal that it made in 2018 to team up with Chinese scientists on novel coronavirus experiments that Republicans believe could have led to the pandemic, despite that project’s never receiving funding.

But in a report and in extensive questioning on Wednesday, the Republicans offered no new information suggesting that EcoHealth Alliance or Dr. Daszak were involved in the coronavirus outbreak. And they did not produce any evidence pointing directly to a coronavirus leak from a lab in China, with or without EcoHealth’s involvement, a hitch in their yearslong effort to implicate Chinese and American scientists in the beginnings of the pandemic.

Democrats on the subcommittee seized on the dearth of new evidence, even as they echoed Republican concerns that Dr. Daszak had not been forthcoming about his collaborations with Chinese scientists.

The subcommittee, which is led by Republicans, has reviewed nearly a half-million pages of documents and conducted over 100 hours of private interviews in the course of investigating the origin of Covid, Representative Raul Ruiz of California, the panel’s top Democrat, said on Wednesday.

But, Mr. Ruiz said, the subcommittee has found “no evidence” linking the pandemic to EcoHealth’s research. And he added that the investigation had not “meaningfully advanced our understanding of the pandemic’s origins.” Rather, he said, the subcommittee’s work now “appeared to be an effort to weaponize concerns about a lab-related origin to fuel sentiment against our nation’s scientists and public health officials for partisan gain.”

Republicans, for their part, pledged that their investigation would continue. They have called Dr. Anthony S. Fauci, the former director of the National Institute of Allergy and Infectious Diseases, to testify next month. A Republican member of the subcommittee pushed in January for Dr. Fauci to be jailed .

“I support work that will make the world safer,” the chairman of the subcommittee, Representative Brad Wenstrup of Ohio, a Republican, said as he opened the hearing on Wednesday. “Our concern is that this research and research similar does the opposite,” he said, referring to EcoHealth’s collaborations with Chinese scientists. “It puts the world at the risk of a pandemic.”

Republicans and Democrats alike hammered Dr. Daszak on Wednesday for what they described as a long list of misrepresentations and evasions. They suggested, among other things, that he was being untruthful about why he had been late to submit a required report to health officials before the pandemic and that he had cast research proposals in ways that helped his chances of receiving funding but misled reviewers. Dr. Daszak strongly denied those charges.

Much of the suspicion centered on an EcoHealth request made in 2018 for Defense Department funding to collect and experiment on coronaviruses with novel traits that would make them highly transmissible in humans.

EcoHealth’s proposed partners included the Wuhan Institute of Virology, a premier site for coronavirus research in China. That institute’s presence in the city where the Covid outbreak began was at the heart of theories that the virus had first infected people as a result of research activities.

Republicans said that notes on a draft of the proposal from 2018 suggested Dr. Daszak was trying to mislead American officials about the extent of Chinese involvement in the project. Dr. Daszak denied that, saying that he had sought — and received — approval from American defense officials for Chinese scientists to participate. Dr. Daszak said the research had never been carried out.

“It’s utterly irrelevant to the origins of Covid,” Dr. Daszak said of the proposal. He acknowledged that Wuhan scientists could have been conducting research independently that he was not aware of.

The Wuhan institute’s biosafety practices have intensified concerns about the possibility of a lab leak. On Wednesday, Republicans doubled down on worries about its lab’s protocols, citing private emails and comments in a closed-door congressional interview by Ralph Baric, a virologist at the University of North Carolina who has collaborated with EcoHealth and the Wuhan institute.

In an email to Dr. Daszak in 2021 , Dr. Baric said the Wuhan institute had failed to conduct virus experiments under appropriately safe conditions, and called the idea that they were taking proper precautions a “load of BS.” Still, Dr. Baric told Dr. Daszak in a separate email that year that the coronavirus had most likely jumped from animals to people outside of a lab. He cited the absence of any evidence that the Wuhan institute possessed a virus related closely enough to the one that caused the pandemic.

There remains no evidence that the Wuhan institute stored any virus that could have become the coronavirus and caused Covid, with or without scientific tinkering, researchers have said.

Scientists who specialize in tracing outbreaks have published analyses of early cases and viral genomes that they say point to the pandemic’s starting at an illegal wild-animal market in Wuhan. The presence of the coronavirus in samples from the market containing genetic material linked to raccoon dogs , they have said, is consistent with that scenario.

Dr. Daszak cited animal-borne disease threats on Wednesday in defending projects by EcoHealth, which works with collaborators around the world to identify and study dangerous viruses circulating in nature.

In April 2020, as EcoHealth came under attack from President Donald J. Trump, the group had a grant terminated by the National Institutes of Health. The grant was reinstated last year, but it does not provide funding for any research in China. The Biden administration has also taken steps to impose a 10-year ban on funding for the Wuhan Institute of Virology.

Republicans on Wednesday cited concerns about EcoHealth’s compliance with grant reporting requirements in arguing that EcoHealth, too, should be barred from receiving federal funds. The N.I.H. faulted the group in 2021 for failing to promptly report findings from studies on how well bat coronaviruses grow in mice. An internal federal watchdog agency chided the N.I.H. last year for failing to demand a progress report from EcoHealth that was two years late.

In an interview after the hearing, Mr. Wenstrup, the Republican subcommittee chair, said that oversight by health agencies was too weak. He called for the creation of criminal penalties for violating health agency protocols — “anything from a misdemeanor to a felony,” he said — as a way of better policing certain scientific research.

Benjamin Mueller reports on health and medicine. He was previously a U.K. correspondent in London and a police reporter in New York. More about Benjamin Mueller

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• Published: 15 February 2023

Coronavirus research: knowledge gaps and research priorities

• Stanley Perlman   ORCID: orcid.org/0000-0003-4213-2354 1 &
• Malik Peiris   ORCID: orcid.org/0000-0001-8217-5995 2 , 3  

Nature Reviews Microbiology volume  21 ,  pages 125–126 ( 2023 ) Cite this article

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• Policy and public health in microbiology

Decades of coronavirus research and intense studies of SARS-CoV-2 since the beginning of the COVID-19 pandemic have led to an unprecedented level of knowledge of coronavirus biology and pathogenesis, yet many outstanding questions remain. Here, we discuss knowledge gaps and research priorities in the field.

Introduction

The COVID-19 pandemic showed that, based on previous research efforts, we understood many aspects of coronavirus biology and pathogenesis, but also that there was much we did not know. In 2019, the worldwide number of coronavirus investigators was small, having increased after the severe acute respiratory syndrome coronavirus (SARS-CoV) outbreak in 2003 but decreasing thereafter. The influx of scientists with diverse expertise into the field after the pandemic onset contributed to an increased understanding of coronavirus replication, epidemiology, SARS-CoV-2 pathogenesis and immune responses in humans, to the development and characterization of experimentally infected animal models for COVID-19, and to SARS-CoV-2 vaccine and antiviral drug development. Here, as investigators who have studied coronaviruses for decades, we outline some of the outstanding research questions that we think need to be addressed.

SARS-CoV-2 emergence

Where did SARS-CoV-2 originate and how did it evolve to infect humans? The emergence of SARS-CoV-2 continues to be an area of controversy and has been, and is being, investigated by many national and international organizations, including the WHO (World Health Organization). It is almost certain that the virus originated in bats and crossed species to humans either directly or indirectly via intermediary hosts. There remains debate on whether the virus first infected humans from a zoonotic source or from a research laboratory, but, no matter what the answer to this question is, it is clear to us that in order to be prepared for the next pandemic, we need to further delineate the panoply of coronaviruses present in bats and possible intermediary hosts 1 . We need to better understand coronavirus circulation in hotspots, such as parts of China and Southeast Asia, where humans, wildlife gathered for food or medicinal purposes and bats are in close proximity. These investigations should include surveillance (virological and serological) of humans in close contact with bats and the game animal trade, with or without respiratory disease, for evidence of coronavirus infection. A related question, discussed below, is why coronaviruses are especially good at jumping species, to humans and other animals.

Zoonotic risk

Once coronaviruses in animal reservoirs are identified, can they be better risk assessed for threats for human spillover? Surveillance of bat reservoirs of sarbecoviruses (Sarbecovirus is the subgenus to which SARS-CoV-2 belongs) had previously found evidence of viruses with a capacity for infecting human cells using the angiotensin converting enzyme 2 (ACE2) receptor (reminiscent of SARS-CoV) 2 . Serological evidence of viral spillover to humans was demonstrated before the emergence of SARS-CoV-2 (ref. 3 ). Arguably, these signals together should have been triggers for action to develop countermeasures with greater urgency. The availability of human organoid cultures and ex vivo cultures of human respiratory tissue may enable the use of physiologically relevant systems for a more systematic risk assessment of animal coronaviruses in the future, analogous to ongoing risk assessments being carried out for animal influenza viruses 4 .

SARS-CoV-2 transmissibility

What explains the high transmissibility of SARS-CoV-2 compared with SARS-CoV or Middle East respiratory syndrome coronavirus (MERS-CoV)? A critical factor leading to the COVID-19 pandemic was the ability of SARS-CoV-2 to grow to high levels in the upper respiratory tract and therefore to readily transmit to other humans. Titres of SARS-CoV and MERS-CoV in the upper respiratory tract peak at later times after infection 5 , consistent with the ability to interrupt transmission with relevant public health infection-prevention methods. A second, related question is why SARS-CoV and a common cold coronavirus, HCoV-NL63, which both use the same receptor as SARS-CoV-2 (ACE2) 6 , have such different patterns of infection within the human respiratory tract. HCoV-NL63 rarely infects the lower respiratory tract, whereas SARS-CoV preferentially causes pneumonia. These different patterns of infection most likely relate to differences in cell entry, including differences in co-receptor usage, host protease usage or fusogenicity of the spike protein, but there are other possibilities. Understanding these differences will provide information on which coronavirus might be expected to be transmissible and to identify additional targets for therapeutic interventions. Further elucidation of the factors that contribute to virus spread will require additional experimental animal models of coronavirus transmission.

The SARS-CoV-2 outbreak also highlighted the lack of evidence-based data on the transmission of coronaviruses, or indeed respiratory viruses in in general, and on which non-pharmaceutical countermeasures (for example, social distancing and masks (surgical versus N99/FFP3 masks)) are effective or not. The SARS-CoV-2 outbreak demonstrated that the only effective control options available in the first months of the pandemic were non-pharmaceutical, but our understanding of the efficacy of specific measures is limited.

Coronavirus genome complexity

Why do coronavirus genomes encode so many more proteins than other RNA viruses? Coronavirus genomes are bigger than those of any other RNA virus, apart from those of related members of the Nidovirales order. The genomes are so large that they require genomic proofreading activity to avoid error catastrophe 7 . A large genome size may contribute to enhanced cross-species transmission, but, at present, this notion is speculative. In any case, an important question is to understand the function of the many non-structural proteins involved in virus replication. Development of a cell-free or entirely in vitro replication system would facilitate detailed probing of the role of individual proteins in replication and transmission. Efforts to develop such cell-free systems were initiated 40 years ago, but it is only in the past few years with the advent of cryo-electron microscopy and new biochemical approaches that progress has been made. These efforts are expected to complement studies in intact cells, which use high-resolution microscopy and related techniques to analyse macromolecular interactions and function at the subcellular level.

Related to the previous question, why do coronaviruses encode so many proteins with apparent immunoevasive function? Coronaviruses encode a variable number of accessory proteins, the genes of which are intermingled within the structural protein genes located at the 3′ end of the genome. For example, SARS-CoV-2 encodes at least six such proteins, with several other putative open reading frames in the genome hypothesized to be expressed and have immunoevasive properties 8 . Confusingly, these genes are often deleted in viruses isolated from infected animals, without apparent loss of virulence. This was shown most clearly in the case of MERS-CoV, in which diverse deletions and insertions in accessory genes were detected in some isolates obtained in Africa from camels, the primary host of the virus 9 . These genetic changes may have unpredictable consequences for virus transmissibility or pathogenesis. Deletion of these genes occasionally leads to increased virulence 10 . The variable and sometimes unexpectedly high numbers of these proteins suggest that they have redundant and, perhaps, additional functions. Such redundancy could contribute to cross-species transmission. The genetic instability of MERS-CoV camels in Africa therefore needs to be monitored and evidence for human spillover needs to be continually assessed.

Predictive evolution

Can coronavirus evolution in infected human or other animal hosts be predicted? Coronaviruses readily mutate and recombine as they adapt to a new host. This is well illustrated by the COVID-19 pandemic, in which ancestral strains of SARS-CoV-2 initially mutated to better infect humans, and later evolved to evade the human immune response, generating a series of variants of concern. Several studies have modelled SARS-CoV-2 evolution but so far it has not been possible to predict how the virus will evolve in the future. Such predictive modelling is recognized to be difficult, but would be very useful in the present pandemic as well as in future coronavirus outbreaks or pandemics for vaccine development, for anticipating clinical disease and pathogenesis, and for risk assessment of animal viruses with zoonotic potential.

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Department of Microbiology and Immunology, University of Iowa, Iowa City, IA, USA

Stanley Perlman

HKU-Pasteur Research Pole, The University of Hong Kong (HKU), Hong Kong Special Administrative Region, People’s Republic of China

Malik Peiris

School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong (HKU), Hong Kong Special Administrative Region, People’s Republic of China

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Perlman, S., Peiris, M. Coronavirus research: knowledge gaps and research priorities. Nat Rev Microbiol 21 , 125–126 (2023). https://doi.org/10.1038/s41579-022-00837-3

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DOI : https://doi.org/10.1038/s41579-022-00837-3

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Sars-cov-2 and innate immunity: the good, the bad, and the “goldilocks”.

• Benjamin L. Sievers
• Mark T. K. Cheng
• Ravindra K. Gupta

Cellular & Molecular Immunology (2023)

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