Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1784819744230080512 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9612314 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT patanarapeelertklot modelingdynamicresponsestocovid19epidemicsacasestudyinthailand AT songprasertwuttinant modelingdynamicresponsestocovid19epidemicsacasestudyinthailand AT patanarapeelertnichaphat modelingdynamicresponsestocovid19epidemicsacasestudyinthailand |