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1.
PLoS Biol ; 20(2): e3001285, 2022 02.
Article in English | MEDLINE | ID: covidwho-1662437

ABSTRACT

Amid the Coronavirus Disease 2019 (COVID-19) pandemic, preprints in the biomedical sciences are being posted and accessed at unprecedented rates, drawing widespread attention from the general public, press, and policymakers for the first time. This phenomenon has sharpened long-standing questions about the reliability of information shared prior to journal peer review. Does the information shared in preprints typically withstand the scrutiny of peer review, or are conclusions likely to change in the version of record? We assessed preprints from bioRxiv and medRxiv that had been posted and subsequently published in a journal through April 30, 2020, representing the initial phase of the pandemic response. We utilised a combination of automatic and manual annotations to quantify how an article changed between the preprinted and published version. We found that the total number of figure panels and tables changed little between preprint and published articles. Moreover, the conclusions of 7.2% of non-COVID-19-related and 17.2% of COVID-19-related abstracts undergo a discrete change by the time of publication, but the majority of these changes do not qualitatively change the conclusions of the paper.


Subject(s)
COVID-19/prevention & control , Information Dissemination/methods , Peer Review, Research/trends , Periodicals as Topic/trends , Publications/trends , COVID-19/epidemiology , COVID-19/virology , Humans , Pandemics/prevention & control , Peer Review, Research/methods , Peer Review, Research/standards , Periodicals as Topic/standards , Periodicals as Topic/statistics & numerical data , Publications/standards , Publications/statistics & numerical data , Publishing/standards , Publishing/statistics & numerical data , Publishing/trends , SARS-CoV-2/isolation & purification , SARS-CoV-2/physiology
2.
Nat Microbiol ; 6(12): 1483-1492, 2021 12.
Article in English | MEDLINE | ID: covidwho-1550288

ABSTRACT

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.


Subject(s)
Host-Pathogen Interactions , Virus Diseases/virology , Virus Physiological Phenomena , Animals , Humans , Virus Diseases/physiopathology , Viruses/genetics , Zoonoses/physiopathology , Zoonoses/virology
3.
PLoS Pathog ; 17(4): e1009149, 2021 04.
Article in English | MEDLINE | ID: covidwho-1194504

ABSTRACT

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.


Subject(s)
Biological Evolution , Coronaviridae Infections/diagnosis , Coronaviridae Infections/virology , Coronaviridae/pathogenicity , Genome, Viral , Machine Learning , Spike Glycoprotein, Coronavirus/metabolism , Animals , Coronaviridae Infections/genetics , Coronaviridae Infections/metabolism , Phylogeny , Spike Glycoprotein, Coronavirus/genetics
4.
PLoS Biol ; 19(4): e3000959, 2021 04.
Article in English | MEDLINE | ID: covidwho-1166988

ABSTRACT

The world continues to face a life-threatening viral pandemic. The virus underlying the Coronavirus Disease 2019 (COVID-19), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has caused over 98 million confirmed cases and 2.2 million deaths since January 2020. Although the most recent respiratory viral pandemic swept the globe only a decade ago, the way science operates and responds to current events has experienced a cultural shift in the interim. The scientific community has responded rapidly to the COVID-19 pandemic, releasing over 125,000 COVID-19-related scientific articles within 10 months of the first confirmed case, of which more than 30,000 were hosted by preprint servers. We focused our analysis on bioRxiv and medRxiv, 2 growing preprint servers for biomedical research, investigating the attributes of COVID-19 preprints, their access and usage rates, as well as characteristics of their propagation on online platforms. Our data provide evidence for increased scientific and public engagement with preprints related to COVID-19 (COVID-19 preprints are accessed more, cited more, and shared more on various online platforms than non-COVID-19 preprints), as well as changes in the use of preprints by journalists and policymakers. We also find evidence for changes in preprinting and publishing behaviour: COVID-19 preprints are shorter and reviewed faster. Our results highlight the unprecedented role of preprints and preprint servers in the dissemination of COVID-19 science and the impact of the pandemic on the scientific communication landscape.


Subject(s)
COVID-19 , Information Dissemination/methods , Publishing/trends , SARS-CoV-2 , Biomedical Research/trends , COVID-19/epidemiology , Communication , Humans , Open Access Publishing/trends , Pandemics , Peer Review, Research/trends , Preprints as Topic , SARS-CoV-2/pathogenicity
5.
Lancet Planet Health ; 5(3): e115-e117, 2021 03.
Article in English | MEDLINE | ID: covidwho-1131942
6.
One Health ; 12: 100221, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1062535

ABSTRACT

Approximately a year into the COVID-19 pandemic caused by the SARS-CoV-2 virus, many countries have seen additional "waves" of infections, especially in the temperate northern hemisphere. Other vulnerable regions, such as South Africa and several parts of South America have also seen cases rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate observed for other common respiratory viruses such as seasonal influenza. Here, we look for empirical evidence of seasonality using a robust estimation framework. For 359 large cities across the world, we estimated the basic reproduction number (R0) using logistic growth curves fitted to cumulative case data. We then assess evidence for association with climatic variables through ordinary least squares (OLS) regression. We find evidence of seasonality, with lower R0 within cities experiencing greater surface radiation (coefficient = -0.005, p < 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R0 and temperature during the early phase of the epidemic in China. However, climatic variables had much weaker explanatory power compared to socioeconomic and disease control factors. Rates of transmission and health burden of the continuing pandemic will be ultimately determined by population factors and disease control policies.

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