Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
07 Aug 2020
Historique:
pubmed: 27 6 2020
medline: 27 6 2020
entrez: 27 6 2020
Statut: epublish

Résumé

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the useful-ness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.2 to 4.9 million, with possibly as many as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 10.3 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.

Identifiants

pubmed: 32587997
doi: 10.1101/2020.04.18.20070821
pmc: PMC7310656
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM130668
Pays : United States

Commentaires et corrections

Type : UpdateIn

Déclaration de conflit d'intérêts

Conflicts of Interest No conflicts of interest.

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Auteurs

Fred S Lu (FS)

Computational Health Informatics Program, Boston Children's Hospital, Boston, MA.
Department of Statistics, Stanford University, Stanford, CA.

Andre T Nguyen (AT)

Computational Health Informatics Program, Boston Children's Hospital, Boston, MA.
University of Maryland, Baltimore County, Baltimore, MD.
Booz Allen Hamilton, Columbia, MD.

Nicholas B Link (NB)

Computational Health Informatics Program, Boston Children's Hospital, Boston, MA.

Jessica T Davis (JT)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.

Matteo Chinazzi (M)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.

Xinyue Xiong (X)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.

Alessandro Vespignani (A)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA USA.

Marc Lipsitch (M)

Department of Epidemiology, Harvard T.H. Chan School of Public Health.

Mauricio Santillana (M)

Computational Health Informatics Program, Boston Children's Hospital, Boston, MA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health.
Department of Pediatrics, Harvard Medical School, Boston, MA.

Classifications MeSH