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Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand

Quantifying the effects of control measures during the emergence and recurrence of SARS-CoV-2 poses a challenge to understanding the dynamic responses in terms of effectiveness and the population’s reaction. This study aims to estimate and compare the non-pharmaceutical interventions applied in the...

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Autores principales: Patanarapeelert, Klot, Songprasert, Wuttinant, Patanarapeelert, Nichaphat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612314/
https://www.ncbi.nlm.nih.gov/pubmed/36288044
http://dx.doi.org/10.3390/tropicalmed7100303
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author Patanarapeelert, Klot
Songprasert, Wuttinant
Patanarapeelert, Nichaphat
author_facet Patanarapeelert, Klot
Songprasert, Wuttinant
Patanarapeelert, Nichaphat
author_sort Patanarapeelert, Klot
collection PubMed
description Quantifying the effects of control measures during the emergence and recurrence of SARS-CoV-2 poses a challenge to understanding the dynamic responses in terms of effectiveness and the population’s reaction. This study aims to estimate and compare the non-pharmaceutical interventions applied in the first and second outbreaks of COVID-19 in Thailand. We formulated a dynamic model of transmission and control. For each outbreak, the time interval was divided into subintervals characterized by epidemic events. We used daily case report data to estimate the transmission rates, the quarantine rate, and its efficiency by the maximum likelihood method. The duration-specific control reproduction numbers were calculated. The model predicts that the reproduction number dropped by about 91% after the nationwide lockdown in the first wave. In the second wave, after a high number of cases had been reported, the reproduction number decreased to about 80% in the next phase, but the spread continued. The estimated value was below the threshold in the last phase. For both waves, successful control was mainly induced by decreased transmission rate, while the explicit quarantine measure showed less effectiveness. The relatively weak control measure estimated by the model may have implications for economic impact and the adaptation of people.
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spelling pubmed-96123142022-10-28 Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand Patanarapeelert, Klot Songprasert, Wuttinant Patanarapeelert, Nichaphat Trop Med Infect Dis Article Quantifying the effects of control measures during the emergence and recurrence of SARS-CoV-2 poses a challenge to understanding the dynamic responses in terms of effectiveness and the population’s reaction. This study aims to estimate and compare the non-pharmaceutical interventions applied in the first and second outbreaks of COVID-19 in Thailand. We formulated a dynamic model of transmission and control. For each outbreak, the time interval was divided into subintervals characterized by epidemic events. We used daily case report data to estimate the transmission rates, the quarantine rate, and its efficiency by the maximum likelihood method. The duration-specific control reproduction numbers were calculated. The model predicts that the reproduction number dropped by about 91% after the nationwide lockdown in the first wave. In the second wave, after a high number of cases had been reported, the reproduction number decreased to about 80% in the next phase, but the spread continued. The estimated value was below the threshold in the last phase. For both waves, successful control was mainly induced by decreased transmission rate, while the explicit quarantine measure showed less effectiveness. The relatively weak control measure estimated by the model may have implications for economic impact and the adaptation of people. MDPI 2022-10-16 /pmc/articles/PMC9612314/ /pubmed/36288044 http://dx.doi.org/10.3390/tropicalmed7100303 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Patanarapeelert, Klot
Songprasert, Wuttinant
Patanarapeelert, Nichaphat
Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand
title Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand
title_full Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand
title_fullStr Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand
title_full_unstemmed Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand
title_short Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand
title_sort modeling dynamic responses to covid-19 epidemics: a case study in thailand
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612314/
https://www.ncbi.nlm.nih.gov/pubmed/36288044
http://dx.doi.org/10.3390/tropicalmed7100303
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