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Inference on the dynamics of COVID-19 in the United States
The evolution of the COVID-19 pandemic is described through a time-dependent stochastic dynamic model in discrete time. The proposed multi-compartment model is expressed through a system of difference equations. Information on the social distancing measures and diagnostic testing rates are incorpora...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831615/ https://www.ncbi.nlm.nih.gov/pubmed/35145115 http://dx.doi.org/10.1038/s41598-021-04494-z |
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author | Bhattacharjee, Satarupa Liao, Shuting Paul, Debashis Chaudhuri, Sanjay |
author_facet | Bhattacharjee, Satarupa Liao, Shuting Paul, Debashis Chaudhuri, Sanjay |
author_sort | Bhattacharjee, Satarupa |
collection | PubMed |
description | The evolution of the COVID-19 pandemic is described through a time-dependent stochastic dynamic model in discrete time. The proposed multi-compartment model is expressed through a system of difference equations. Information on the social distancing measures and diagnostic testing rates are incorporated to characterize the dynamics of the various compartments of the model. In contrast with conventional epidemiological models, the proposed model involves interpretable temporally static and dynamic epidemiological rate parameters. A model fitting strategy built upon nonparametric smoothing is employed for estimating the time-varying parameters, while profiling over the time-independent parameters. Confidence bands of the parameters are obtained through a residual bootstrap procedure. A key feature of the methodology is its ability to estimate latent unobservable compartments such as the number of asymptomatic but infected individuals who are known to be the key vectors of COVID-19 spread. The nature of the disease dynamics is further quantified by relevant epidemiological markers that make use of the estimates of latent compartments. The methodology is applied to understand the true extent and dynamics of the pandemic in various states within the United States (US). |
format | Online Article Text |
id | pubmed-8831615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88316152022-02-14 Inference on the dynamics of COVID-19 in the United States Bhattacharjee, Satarupa Liao, Shuting Paul, Debashis Chaudhuri, Sanjay Sci Rep Article The evolution of the COVID-19 pandemic is described through a time-dependent stochastic dynamic model in discrete time. The proposed multi-compartment model is expressed through a system of difference equations. Information on the social distancing measures and diagnostic testing rates are incorporated to characterize the dynamics of the various compartments of the model. In contrast with conventional epidemiological models, the proposed model involves interpretable temporally static and dynamic epidemiological rate parameters. A model fitting strategy built upon nonparametric smoothing is employed for estimating the time-varying parameters, while profiling over the time-independent parameters. Confidence bands of the parameters are obtained through a residual bootstrap procedure. A key feature of the methodology is its ability to estimate latent unobservable compartments such as the number of asymptomatic but infected individuals who are known to be the key vectors of COVID-19 spread. The nature of the disease dynamics is further quantified by relevant epidemiological markers that make use of the estimates of latent compartments. The methodology is applied to understand the true extent and dynamics of the pandemic in various states within the United States (US). Nature Publishing Group UK 2022-02-10 /pmc/articles/PMC8831615/ /pubmed/35145115 http://dx.doi.org/10.1038/s41598-021-04494-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Bhattacharjee, Satarupa Liao, Shuting Paul, Debashis Chaudhuri, Sanjay Inference on the dynamics of COVID-19 in the United States |
title | Inference on the dynamics of COVID-19 in the United States |
title_full | Inference on the dynamics of COVID-19 in the United States |
title_fullStr | Inference on the dynamics of COVID-19 in the United States |
title_full_unstemmed | Inference on the dynamics of COVID-19 in the United States |
title_short | Inference on the dynamics of COVID-19 in the United States |
title_sort | inference on the dynamics of covid-19 in the united states |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831615/ https://www.ncbi.nlm.nih.gov/pubmed/35145115 http://dx.doi.org/10.1038/s41598-021-04494-z |
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