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Estimation of local time-varying reproduction numbers in noisy surveillance data

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission...

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Autores principales: Li, Wenrui, Bulekova, Katia, Gregor, Brian, White, Laura F., Kolaczyk, Eric D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095231/
https://www.ncbi.nlm.nih.gov/pubmed/33948612
http://dx.doi.org/10.1101/2021.04.23.21255958
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author Li, Wenrui
Bulekova, Katia
Gregor, Brian
White, Laura F.
Kolaczyk, Eric D.
author_facet Li, Wenrui
Bulekova, Katia
Gregor, Brian
White, Laura F.
Kolaczyk, Eric D.
author_sort Li, Wenrui
collection PubMed
description A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.
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spelling pubmed-80952312021-05-05 Estimation of local time-varying reproduction numbers in noisy surveillance data Li, Wenrui Bulekova, Katia Gregor, Brian White, Laura F. Kolaczyk, Eric D. medRxiv Article A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. Cold Spring Harbor Laboratory 2022-04-28 /pmc/articles/PMC8095231/ /pubmed/33948612 http://dx.doi.org/10.1101/2021.04.23.21255958 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Li, Wenrui
Bulekova, Katia
Gregor, Brian
White, Laura F.
Kolaczyk, Eric D.
Estimation of local time-varying reproduction numbers in noisy surveillance data
title Estimation of local time-varying reproduction numbers in noisy surveillance data
title_full Estimation of local time-varying reproduction numbers in noisy surveillance data
title_fullStr Estimation of local time-varying reproduction numbers in noisy surveillance data
title_full_unstemmed Estimation of local time-varying reproduction numbers in noisy surveillance data
title_short Estimation of local time-varying reproduction numbers in noisy surveillance data
title_sort estimation of local time-varying reproduction numbers in noisy surveillance data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095231/
https://www.ncbi.nlm.nih.gov/pubmed/33948612
http://dx.doi.org/10.1101/2021.04.23.21255958
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