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Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand
BACKGROUND: Thailand experienced the first wave of Coronavirus Disease 2019 (COVID-19) during March–May 2020 and has been facing the second wave since December 2020. The area facing the greatest impact was Samut Sakhon, a main migrant-receiving province in the country. The Department of Disease Cont...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349215/ https://www.ncbi.nlm.nih.gov/pubmed/34377040 http://dx.doi.org/10.2147/RMHP.S318012 |
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author | Suphanchaimat, Rapeepong Nittayasoot, Natthaprang Thammawijaya, Panithee Teekasap, Pard Ungchusak, Kumnuan |
author_facet | Suphanchaimat, Rapeepong Nittayasoot, Natthaprang Thammawijaya, Panithee Teekasap, Pard Ungchusak, Kumnuan |
author_sort | Suphanchaimat, Rapeepong |
collection | PubMed |
description | BACKGROUND: Thailand experienced the first wave of Coronavirus Disease 2019 (COVID-19) during March–May 2020 and has been facing the second wave since December 2020. The area facing the greatest impact was Samut Sakhon, a main migrant-receiving province in the country. The Department of Disease Control (DDC) of the Thai Ministry of Public Health (MOPH) considered initiating a vaccination strategy in combination with active case finding (ACF) in the epidemic area. The DDC commissioned a research team to predict the impact of various vaccination and ACF policy scenarios in terms of case reduction and deaths averted, which is the objective of this study. METHODS: The design of this study was a secondary analysis of quantitative data. Most of the data were obtained from the DDC, MOPH. Deterministic system dynamics and compartmental models were exercised. A basic reproductive number (R(0)) was estimated at 3 from the beginning. Vaccine efficacy against disease transmission was assumed to be 50%. A total of 10,000 people were estimated as an initial population size. RESULTS: The findings showed that the greater the vaccination coverage, the smaller the size of incident and cumulative cases. Compared with a no-vaccination and no-ACF scenario, the 90%-vaccination coverage combined with 90%-ACF coverage contributed to a reduction of cumulative cases by 33%. The case reduction benefit would be greater when R(0) was smaller (~53% and ~51% when R(0) equated 2 and 1.5, respectively). CONCLUSION: This study reaffirmed the idea that a combination of vaccination and ACF measures contributed to favourable results in reducing the number of COVID-19 cases and deaths, relative to the implementation of only a single measure. The greater the vaccination and ACF coverage, the greater the volume of cases saved. Though we demonstrated the benefit of vaccination strategies in this setting, actual implementation should consider many more policy angles, such as social acceptability, cost-effectiveness and operational feasibility. Further studies that address these topics based on empirical evidence are of great value. |
format | Online Article Text |
id | pubmed-8349215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-83492152021-08-09 Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand Suphanchaimat, Rapeepong Nittayasoot, Natthaprang Thammawijaya, Panithee Teekasap, Pard Ungchusak, Kumnuan Risk Manag Healthc Policy Original Research BACKGROUND: Thailand experienced the first wave of Coronavirus Disease 2019 (COVID-19) during March–May 2020 and has been facing the second wave since December 2020. The area facing the greatest impact was Samut Sakhon, a main migrant-receiving province in the country. The Department of Disease Control (DDC) of the Thai Ministry of Public Health (MOPH) considered initiating a vaccination strategy in combination with active case finding (ACF) in the epidemic area. The DDC commissioned a research team to predict the impact of various vaccination and ACF policy scenarios in terms of case reduction and deaths averted, which is the objective of this study. METHODS: The design of this study was a secondary analysis of quantitative data. Most of the data were obtained from the DDC, MOPH. Deterministic system dynamics and compartmental models were exercised. A basic reproductive number (R(0)) was estimated at 3 from the beginning. Vaccine efficacy against disease transmission was assumed to be 50%. A total of 10,000 people were estimated as an initial population size. RESULTS: The findings showed that the greater the vaccination coverage, the smaller the size of incident and cumulative cases. Compared with a no-vaccination and no-ACF scenario, the 90%-vaccination coverage combined with 90%-ACF coverage contributed to a reduction of cumulative cases by 33%. The case reduction benefit would be greater when R(0) was smaller (~53% and ~51% when R(0) equated 2 and 1.5, respectively). CONCLUSION: This study reaffirmed the idea that a combination of vaccination and ACF measures contributed to favourable results in reducing the number of COVID-19 cases and deaths, relative to the implementation of only a single measure. The greater the vaccination and ACF coverage, the greater the volume of cases saved. Though we demonstrated the benefit of vaccination strategies in this setting, actual implementation should consider many more policy angles, such as social acceptability, cost-effectiveness and operational feasibility. Further studies that address these topics based on empirical evidence are of great value. Dove 2021-08-03 /pmc/articles/PMC8349215/ /pubmed/34377040 http://dx.doi.org/10.2147/RMHP.S318012 Text en © 2021 Suphanchaimat et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Suphanchaimat, Rapeepong Nittayasoot, Natthaprang Thammawijaya, Panithee Teekasap, Pard Ungchusak, Kumnuan Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand |
title | Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand |
title_full | Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand |
title_fullStr | Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand |
title_full_unstemmed | Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand |
title_short | Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand |
title_sort | predicted impact of vaccination and active case finding measures to control epidemic of coronavirus disease 2019 in a migrant-populated area in thailand |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349215/ https://www.ncbi.nlm.nih.gov/pubmed/34377040 http://dx.doi.org/10.2147/RMHP.S318012 |
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