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Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.
Lu, Fred S; Nguyen, Andre T; Link, Nicholas B; Molina, Mathieu; Davis, Jessica T; Chinazzi, Matteo; Xiong, Xinyue; Vespignani, Alessandro; Lipsitch, Marc; Santillana, Mauricio.
  • Lu FS; Department of Statistics, Stanford University, Stanford, California, United States of America.
  • Nguyen AT; University of Maryland, Baltimore County, Baltimore, Maryland, United States of America.
  • Link NB; Booz Allen Hamilton, Columbia, Maryland, United States of America.
  • Molina M; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
  • Davis JT; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
  • Chinazzi M; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Xiong X; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Vespignani A; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Lipsitch M; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
  • Santillana M; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
PLoS Comput Biol ; 17(6): e1008994, 2021 06.
Article in English | MEDLINE | ID: covidwho-1278164
ABSTRACT
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 usefulness 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 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Population Surveillance / Models, Statistical / Influenza, Human / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Journal.pcbi.1008994

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Population Surveillance / Models, Statistical / Influenza, Human / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Journal.pcbi.1008994