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Estimating the undetected emergence of COVID-19 in the US.
Javan, Emily M; Fox, Spencer J; Meyers, Lauren Ancel.
  • Javan EM; Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States of America.
  • Fox SJ; Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States of America.
  • Meyers LA; Department of Epidemiology & Biostatistics and Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America.
PLoS One ; 18(4): e0284025, 2023.
Artículo en Inglés | MEDLINE | ID: covidwho-2264513
ABSTRACT
As SARS-CoV-2 emerged as a global threat in early 2020, China enacted rapid and strict lockdown orders to prevent introductions and suppress transmission. In contrast, the United States federal government did not enact national orders. State and local authorities were left to make rapid decisions based on limited case data and scientific information to protect their communities. To support local decision making in early 2020, we developed a model for estimating the probability of an undetected COVID-19 epidemic (epidemic risk) in each US county based on the epidemiological characteristics of the virus and the number of confirmed and suspected cases. As a retrospective analysis we included county-specific reproduction numbers and found that counties with only a single reported case by March 16, 2020 had a mean epidemic risk of 71% (95% CI 52-83%), implying COVID-19 was already spreading widely by the first detected case. By that date, 15% of US counties covering 63% of the population had reported at least one case and had epidemic risk greater than 50%. We find that a 10% increase in model estimated epidemic risk for March 16 yields a 0.53 (95% CI 0.49-0.58) increase in the log odds that the county reported at least two additional cases in the following week. The original epidemic risk estimates made on March 16, 2020 that assumed all counties had an effective reproduction number of 3.0 are highly correlated with our retrospective estimates (r = 0.99; p<0.001) but are less predictive of subsequent case increases (AIC difference of 93.3 and 100% weight in favor of the retrospective risk estimates). Given the low rates of testing and reporting early in the pandemic, taking action upon the detection of just one or a few cases may be prudent.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Estudio observacional / Estudio pronóstico Límite: Humanos País/Región como asunto: America del Norte Idioma: Inglés Revista: PLoS One Asunto de la revista: Ciencia / Medicina Año: 2023 Tipo del documento: Artículo País de afiliación: Journal.pone.0284025

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Estudio observacional / Estudio pronóstico Límite: Humanos País/Región como asunto: America del Norte Idioma: Inglés Revista: PLoS One Asunto de la revista: Ciencia / Medicina Año: 2023 Tipo del documento: Artículo País de afiliación: Journal.pone.0284025