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Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic
Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297945/ https://www.ncbi.nlm.nih.gov/pubmed/34252078 http://dx.doi.org/10.1371/journal.pcbi.1009210 |
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author | Li, Tenglong White, Laura F. |
author_facet | Li, Tenglong White, Laura F. |
author_sort | Li, Tenglong |
collection | PubMed |
description | Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve. |
format | Online Article Text |
id | pubmed-8297945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82979452021-07-31 Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic Li, Tenglong White, Laura F. PLoS Comput Biol Research Article Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve. Public Library of Science 2021-07-12 /pmc/articles/PMC8297945/ /pubmed/34252078 http://dx.doi.org/10.1371/journal.pcbi.1009210 Text en © 2021 Li, White https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Tenglong White, Laura F. Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic |
title | Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic |
title_full | Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic |
title_fullStr | Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic |
title_full_unstemmed | Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic |
title_short | Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic |
title_sort | bayesian back-calculation and nowcasting for line list data during the covid-19 pandemic |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297945/ https://www.ncbi.nlm.nih.gov/pubmed/34252078 http://dx.doi.org/10.1371/journal.pcbi.1009210 |
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