Evidence map›Paper›PMID 36040963›Full record

ArticlePLoS computational biology2022

Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.

Melanie H Chitwood, Marcus Russi, Kenneth Gunasekera, Joshua Havumaki, Fayette Klaassen, Virginia E Pitzer, Joshua A Salomon, Nicole A Swartwood, Joshua L Warren, Daniel M Weinberger and 2 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

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  15. Population Immunity to Pre-Omicron and Omicron Severe Acute Respiratory Syndrome Coronavirus 2 Variants in US States and Counties Through 1 December 2021.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Melanie H ChitwoodDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.ORCID 0000-0002-9289-5694
Marcus RussiDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.ORCID 0000-0002-7106-068X
Kenneth GunasekeraDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.ORCID 0000-0003-2098-2358
Joshua HavumakiDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.ORCID 0000-0002-6749-1217
Fayette KlaassenDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts United States of America.ORCID 0000-0002-9488-2571
Virginia E PitzerDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.ORCID 0000-0003-1015-2289
Joshua A SalomonDepartment of Health Policy, Stanford University, Stanford, California United States of America.
Nicole A SwartwoodDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts United States of America.ORCID 0000-0003-4116-9243
Joshua L WarrenDepartment of Biostatistics and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.
Daniel M WeinbergerDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.ORCID 0000-0003-1178-8086
Ted CohenDepartment of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut United States of America.
Nicolas A MenziesDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts United States of America.ORCID 0000-0002-2571-016X

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM007205 · NIGMS · YALE UNIVERSITY · PI KAZMIERCZAK, BARBARA I · 1985 to 2019
$42.9M
Research Mentoring and Building Capacity of underrepresented Minority Research Scientists in India.D43TW010540 · FIC · YALE UNIVERSITY · PI MICHELE BARRY, Eva Harris · 2017 to 2026
$15.5M
Predicting and monitoring variations in the effects of vaccines against RSVR01AI137093 · NIAID · YALE UNIVERSITY · PI Virginia E Pitzer, Daniel Martin Weinberger · 2018 to 2026
$3.4M
Optimal targeting for individual and population-level TB preventionR01AI146555 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI MENZIES, NICOLAS A · 2020 to 2024
$2.5M
Evaluating health and economic effects of targeted strategies in TB/HIVR01AI112438 · NIAID · YALE UNIVERSITY · PI COHEN, THEODORE H, SALOMON, JOSHUA A · 2014 to 2017
$2.4M
FIC NIH HHS D43 TW010540NCATS NIH HHS UL1 TR001863NIAID NIH HHS R01 AI112438NIAID NIH HHS R01 AI137093NIAID NIH HHS R01 AI146555NIGMS NIH HHS T32 GM007205
6 · The paper itself

Abstract

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Indexed as

COVID-19EpidemicsBayes TheoremHumansSARS-CoV-2Seroepidemiologic StudiesUnited States

Identifiers

PMID36040963
PMCPMC9467347

What OpenQuestion holds

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Registered trials

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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.