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Accounting for incomplete testing in the estimation of epidemic parameters

As the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling tim...

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Detalles Bibliográficos
Autores principales: Betensky, R.A., Feng, Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273303/
https://www.ncbi.nlm.nih.gov/pubmed/32511535
http://dx.doi.org/10.1101/2020.04.08.20058313
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author Betensky, R.A.
Feng, Y.
author_facet Betensky, R.A.
Feng, Y.
author_sort Betensky, R.A.
collection PubMed
description As the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling time of the epidemic and various other representations of the numbers of cases identified over time. Crude estimates of these quantities suffer from dependence on the underlying testing strategies within communities. We clarify the functional relationship between testing and the epidemic parameters, and thereby derive sensitivity analyses that explore the range of possible truths under various testing dynamics. We derive the required adjustment to the estimates of interest for New York City. We demonstrate that crude estimates that assume stable testing or complete testing can be biased.
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spelling pubmed-72733032020-06-07 Accounting for incomplete testing in the estimation of epidemic parameters Betensky, R.A. Feng, Y. medRxiv Article As the COVID-19 pandemic spreads across the world, it is important to understand its features and responses to public health interventions in real-time. The field of infectious diseases epidemiology has highly advanced modeling strategies that yield relevant estimates. These include the doubling time of the epidemic and various other representations of the numbers of cases identified over time. Crude estimates of these quantities suffer from dependence on the underlying testing strategies within communities. We clarify the functional relationship between testing and the epidemic parameters, and thereby derive sensitivity analyses that explore the range of possible truths under various testing dynamics. We derive the required adjustment to the estimates of interest for New York City. We demonstrate that crude estimates that assume stable testing or complete testing can be biased. Cold Spring Harbor Laboratory 2020-05-10 /pmc/articles/PMC7273303/ /pubmed/32511535 http://dx.doi.org/10.1101/2020.04.08.20058313 Text en http://creativecommons.org/licenses/by-nc-nd/4.0/It is made available under a CC-BY-NC-ND 4.0 International license (http://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Article
Betensky, R.A.
Feng, Y.
Accounting for incomplete testing in the estimation of epidemic parameters
title Accounting for incomplete testing in the estimation of epidemic parameters
title_full Accounting for incomplete testing in the estimation of epidemic parameters
title_fullStr Accounting for incomplete testing in the estimation of epidemic parameters
title_full_unstemmed Accounting for incomplete testing in the estimation of epidemic parameters
title_short Accounting for incomplete testing in the estimation of epidemic parameters
title_sort accounting for incomplete testing in the estimation of epidemic parameters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273303/
https://www.ncbi.nlm.nih.gov/pubmed/32511535
http://dx.doi.org/10.1101/2020.04.08.20058313
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