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Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability

BACKGROUND: More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread....

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Autores principales: Abdulsalam, Fatima Ibrahim, Antúnez, Pablo, Jawjit, Warit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351518/
https://www.ncbi.nlm.nih.gov/pubmed/37465156
http://dx.doi.org/10.7717/peerj.15619
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author Abdulsalam, Fatima Ibrahim
Antúnez, Pablo
Jawjit, Warit
author_facet Abdulsalam, Fatima Ibrahim
Antúnez, Pablo
Jawjit, Warit
author_sort Abdulsalam, Fatima Ibrahim
collection PubMed
description BACKGROUND: More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread. The risk of dengue infection at the population level varies in time and space among sub-populations thus, it is important to study the risk of infection considering spatio-temporal variation. METHODS: This study presents a joint spatio-temporal epidemiological model in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation with the CARBayesST package of R software. For this purpose, monthly dengue records by district from 2002 to 2018 from the southern region of Thailand provided by the Ministry of Public Health of Thailand and eight environmental variables were used. RESULTS: Results show that an increasing level of temperature, number of rainy days and sea level pressure are associated with a higher occurrence of dengue fever and consequently higher incidence risk, while an increasing level of wind speed seems to suggest a protective factor. Likewise, we found that the elevated risks of dengue in the immediate future are in the districts of Phipun, Phrom Kili, Lan Saka, Phra Phrom and Chaloem Phakiat. The resulting estimates provide insights into the effects of covariate risk factors, spatio-temporal trends and dengue-related health inequalities at the district level in southern Thailand. CONCLUSION: Possible implications are discussed considering some anthropogenic factors that could inhibit or enhance dengue occurrence. Risk maps indicated which districts are above and below baseline risk, allowing for the identification of local anomalies and high-risk boundaries. In the event of near future, the threat of elevated disease risk needs to be prevented and controlled considering the factors underlying the spread of mosquitoes in the Southeast Asian region.
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spelling pubmed-103515182023-07-18 Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability Abdulsalam, Fatima Ibrahim Antúnez, Pablo Jawjit, Warit PeerJ Epidemiology BACKGROUND: More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread. The risk of dengue infection at the population level varies in time and space among sub-populations thus, it is important to study the risk of infection considering spatio-temporal variation. METHODS: This study presents a joint spatio-temporal epidemiological model in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation with the CARBayesST package of R software. For this purpose, monthly dengue records by district from 2002 to 2018 from the southern region of Thailand provided by the Ministry of Public Health of Thailand and eight environmental variables were used. RESULTS: Results show that an increasing level of temperature, number of rainy days and sea level pressure are associated with a higher occurrence of dengue fever and consequently higher incidence risk, while an increasing level of wind speed seems to suggest a protective factor. Likewise, we found that the elevated risks of dengue in the immediate future are in the districts of Phipun, Phrom Kili, Lan Saka, Phra Phrom and Chaloem Phakiat. The resulting estimates provide insights into the effects of covariate risk factors, spatio-temporal trends and dengue-related health inequalities at the district level in southern Thailand. CONCLUSION: Possible implications are discussed considering some anthropogenic factors that could inhibit or enhance dengue occurrence. Risk maps indicated which districts are above and below baseline risk, allowing for the identification of local anomalies and high-risk boundaries. In the event of near future, the threat of elevated disease risk needs to be prevented and controlled considering the factors underlying the spread of mosquitoes in the Southeast Asian region. PeerJ Inc. 2023-07-14 /pmc/articles/PMC10351518/ /pubmed/37465156 http://dx.doi.org/10.7717/peerj.15619 Text en ©2023 Abdulsalam et al. 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Epidemiology
Abdulsalam, Fatima Ibrahim
Antúnez, Pablo
Jawjit, Warit
Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability
title Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability
title_full Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability
title_fullStr Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability
title_full_unstemmed Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability
title_short Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability
title_sort spatio-temporal dengue risk modelling in the south of thailand: a bayesian approach to dengue vulnerability
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351518/
https://www.ncbi.nlm.nih.gov/pubmed/37465156
http://dx.doi.org/10.7717/peerj.15619
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