Evidence map›Paper›PMID 33269353›Full record

ArticlebioRxiv : the preprint server for biology2020

Timing the SARS-CoV-2 Index Case in Hubei Province.

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

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Jonathan PekarBioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA.
Michael WorobeyDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA.
Niema MoshiriDepartment Computer Science & Engineering, University of California San Diego, La Jolla, CA 92093, USA.
Konrad SchefflerIllumina, Inc., San Diego, CA 92122, USA.
Joel O WertheimDepartment of Medicine, University of California San Diego, La Jolla, CA 92093, USA.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
Next-Generation Phylodynamics-targeted Partner Service Models for Combined HIV PreventionR01AI136056 · NIAID · UNIVERSITY OF CHICAGO · PI BENBOW, NANETTE DIOR, D'AQUILA, RICHARD · 2018 to 2022
$4.0M
Automation and Evaluation of Real-Time Transmission Network-Based HIV Prevention Services in New York CityR01AI135992 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WERTHEIM, JOEL OKRENT · 2018 to 2022
$3.6M
NIAID NIH HHS R01 AI135992NIAID NIH HHS R01 AI136056NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Understanding when SARS-CoV-2 emerged is critical to evaluating our current approach to monitoring novel zoonotic pathogens and understanding the failure of early containment and mitigation efforts for COVID-19. We employed a coalescent framework to combine retrospective molecular clock inference with forward epidemiological simulations to determine how long SARS-CoV-2 could have circulated prior to the time of the most recent common ancestor. Our results define the period between mid-October and mid-November 2019 as the plausible interval when the first case of SARS-CoV-2 emerged in Hubei province. By characterizing the likely dynamics of the virus before it was discovered, we show that over two-thirds of SARS-CoV-2-like zoonotic events would be self-limited, dying out without igniting a pandemic. Our findings highlight the shortcomings of zoonosis surveillance approaches for detecting highly contagious pathogens with moderate mortality rates.

Identifiers

PMID33269353
PMCPMC7709179

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.