Timing the SARS-CoV-2 index case in Hubei province

Pekar, Jonathan E.; Worobey, Michael; Moshiri, Niema; Scheffler, Konrad; Wertheim, Joel O.

doi:10.1126/science.abf8003

Cited by 136 publications

(136 citation statements)

References 53 publications

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“…To contextualize the viral sequences recovered from the deleted project, I first analyze early SARS-CoV-2 sequences already available in the GISAID database (Shu and McCauley 2017). The analyses described in this section are not entirely novel, but syn-thesize observations from multiple prior studies (Kumar et al 2021;Pekar et al 2021;Rambaut et al 2020;Forster et al 2020;Pipes et al 2021) to provide key background.…”

Section: Analysis Of Existing Sars-cov-2 Sequences Emphasizes the Perplexing Discordance Between Collection Date And Distance To Bat Coromentioning

confidence: 99%

Recovery of deleted deep sequencing data sheds more light on the early Wuhan SARS-CoV-2 epidemic

Bloom

2021

Preprint

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The origin and early spread of SARS-CoV-2 remains shrouded in mystery. Here I identify a data set containing SARS-CoV-2 sequences from early in the Wuhan epidemic that has been deleted from the NIH's Sequence Read Archive. I recover the deleted files from the Google Cloud, and reconstruct partial sequences of 13 early epidemic viruses. Phylogenetic analysis of these sequences in the context of carefully annotated existing data suggests that the Huanan Seafood Market sequences that are the focus of the joint WHO-China report are not fully representative of the viruses in Wuhan early in the epidemic. Instead, the progenitor of known SARS-CoV-2 sequences likely contained three mutations relative to the market viruses that made it more similar to SARS-CoV-2's bat coronavirus relatives.

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Section: Analysis Of Existing Sars-cov-2 Sequences Emphasizes the Perplexing Discordance Between Collection Date And Distance To Bat Coromentioning

confidence: 99%

Recovery of deleted deep sequencing data sheds more light on the early Wuhan SARS-CoV-2 epidemic

Bloom

2021

Preprint

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“…October/November 2019 [7,11], [12,13] animal source non-human primates primary host: bats, intermediate hosts:…”

Section: Hiv-1 Sars-cov-2 Refs Demographic Features Geographic Originmentioning

confidence: 99%

SARS-CoV-2 Portrayed Against HIV: Contrary Viral Strategies in Similar Disguise

Duerr¹,

Jimenez²,

Dittmann³

2021

Preprint

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SARS-CoV-2 and HIV are zoonotic viruses that rapidly reached pandemic scale causing global losses and fear. The COVID-19 and AIDS pandemics ignited massive efforts worldwide to develop antiviral strategies and characterize viral architectures, biological and immunological properties, and clinical outcomes. Although both viruses have a comparable appearance as enveloped, positive-stranded RNA viruses with envelope spikes mediating cellular entry, the entry process, downstream biological and immunological pathways, clinical outcomes, and disease courses are strikingly different. This review provides a systemic comparison of both viruses’ structural and functional characteristics delineating their distinct strategies for efficient spread.

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“…15 The higher than expected number of ARI cases in January 2020 across multiple counties is unlikely due to COVID-19, as global evidence indicates that there was not sustained transmission of SARS-CoV-2 outside China during this time. 16 However, in the case of Maryland County, which had a large spike in ARI cases from January through March, it is possible that the containment measures taken for COVID-19 following the national health emergency contributed to a reduction in ARI cases in the months of April through August. 17…”

Section: Application Of Ari Syndromic Surveillance In Liberiamentioning

confidence: 99%

Syndromic surveillance using monthly aggregate health systems information data: methods with application to COVID-19 in Liberia

Fulcher

Boley²,

Gopaluni

et al. 2021

International Journal of Epidemiology

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Background Early detection of SARS-CoV-2 circulation is imperative to inform local public health response. However, it has been hindered by limited access to SARS-CoV-2 diagnostic tests and testing infrastructure. In regions with limited testing capacity, routinely collected health data might be leveraged to identify geographical locales experiencing higher than expected rates of COVID-19-associated symptoms for more specific testing activities. Methods We developed syndromic surveillance tools to analyse aggregated health facility data on COVID-19-related indicators in seven low- and middle-income countries (LMICs), including Liberia. We used time series models to estimate the expected monthly counts and 95% prediction intervals based on 4 years of previous data. Here, we detail and provide resources for our data preparation procedures, modelling approach and data visualisation tools with application to Liberia. Results To demonstrate the utility of these methods, we present syndromic surveillance results for acute respiratory infections (ARI) at health facilities in Liberia during the initial months of the COVID-19 pandemic (January through August 2020). For each month, we estimated the deviation between the expected and observed number of ARI cases for 325 health facilities and 15 counties to identify potential areas of SARS-CoV-2 circulation. Conclusions Syndromic surveillance can be used to monitor health facility catchment areas for spikes in specific symptoms which may indicate SARS-CoV-2 circulation. The developed methods coupled with the existing infrastructure for routine health data systems can be leveraged to monitor a variety of indicators and other infectious diseases with epidemic potential.

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Timing the SARS-CoV-2 index case in Hubei province

Cited by 136 publications

References 53 publications

Recovery of deleted deep sequencing data sheds more light on the early Wuhan SARS-CoV-2 epidemic

Recovery of deleted deep sequencing data sheds more light on the early Wuhan SARS-CoV-2 epidemic

SARS-CoV-2 Portrayed Against HIV: Contrary Viral Strategies in Similar Disguise

Syndromic surveillance using monthly aggregate health systems information data: methods with application to COVID-19 in Liberia

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