Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis

© 2019 Mahikul et al. Background Melioidosis is an infectious disease that is transmitted mainly through contact with contaminated soil or water, and exhibits marked seasonality in most settings, including Southeast Asia. In this study, we used mathematical modelling to examine the impacts of such d...

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Main Authors: Wiriya Mahikul, Lisa J. White, Kittiyod Poovorawan, Ngamphol Soonthornworasiri, Pataporn Sukontamarn, Phetsavanh Chanthavilay, Graham F. Medley, Wirichada Panngum
Other Authors: London School of Hygiene & Tropical Medicine
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Published: 2020
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/51698
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spelling th-mahidol.516982020-01-27T16:53:21Z Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis Wiriya Mahikul Lisa J. White Kittiyod Poovorawan Ngamphol Soonthornworasiri Pataporn Sukontamarn Phetsavanh Chanthavilay Graham F. Medley Wirichada Panngum London School of Hygiene & Tropical Medicine Chulalongkorn University Mahidol University Nuffield Department of Clinical Medicine Institute of Research and Education Development Medicine © 2019 Mahikul et al. Background Melioidosis is an infectious disease that is transmitted mainly through contact with contaminated soil or water, and exhibits marked seasonality in most settings, including Southeast Asia. In this study, we used mathematical modelling to examine the impacts of such demographic changes on melioidosis incidence, and to predict the disease burden in a developing country such as Thailand. Methodology/Principal findings A melioidosis infection model was constructed which included demographic data, diabetes mellitus (DM) prevalence, and melioidosis disease processes. The model was fitted to reported melioidosis incidence in Thailand by age, sex, and geographical area, between 2008 and 2015, using a Bayesian Markov Chain Monte Carlo (MCMC) approach. The model was then used to predict the disease burden and future trends of melioidosis incidence in Thailand. Our model predicted two-fold higher incidence rates of melioidosis compared with national surveillance data from 2015. The estimated incidence rates among males were two-fold greater than those in females. Furthermore, the melioidosis incidence rates in the Northeast region population, and among the transient population, were more than double compared to the non-Northeast region population. The highest incidence rates occurred in males aged 45–59 years old for all regions. The average incidence rate of melioidosis between 2005 and 2035 was predicted to be 11.42 to 12.78 per 100,000 population per year, with a slightly increasing trend. Overall, it was estimated that about half of all cases of melioidosis were symptomatic. In addition, the model suggested a greater susceptibility to melioidosis in diabetic compared with non-diabetic individuals. Conclusions/Significance The increasing trend of melioidosis incidence rates was significantly higher among working-age Northeast and transient populations, males aged ≤45 years old, and diabetic individuals. Targeted intervention strategies, such as health education and awareness raising initiatives, should be implemented on high-risk groups, such as those living in the Northeast region, and the seasonally transient population. 2020-01-27T09:53:21Z 2020-01-27T09:53:21Z 2019-05-01 Article PLoS Neglected Tropical Diseases. Vol.13, No.5 (2019) 10.1371/journal.pntd.0007380 19352735 19352727 2-s2.0-85066457588 https://repository.li.mahidol.ac.th/handle/123456789/51698 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066457588&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Medicine
spellingShingle Medicine
Wiriya Mahikul
Lisa J. White
Kittiyod Poovorawan
Ngamphol Soonthornworasiri
Pataporn Sukontamarn
Phetsavanh Chanthavilay
Graham F. Medley
Wirichada Panngum
Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis
description © 2019 Mahikul et al. Background Melioidosis is an infectious disease that is transmitted mainly through contact with contaminated soil or water, and exhibits marked seasonality in most settings, including Southeast Asia. In this study, we used mathematical modelling to examine the impacts of such demographic changes on melioidosis incidence, and to predict the disease burden in a developing country such as Thailand. Methodology/Principal findings A melioidosis infection model was constructed which included demographic data, diabetes mellitus (DM) prevalence, and melioidosis disease processes. The model was fitted to reported melioidosis incidence in Thailand by age, sex, and geographical area, between 2008 and 2015, using a Bayesian Markov Chain Monte Carlo (MCMC) approach. The model was then used to predict the disease burden and future trends of melioidosis incidence in Thailand. Our model predicted two-fold higher incidence rates of melioidosis compared with national surveillance data from 2015. The estimated incidence rates among males were two-fold greater than those in females. Furthermore, the melioidosis incidence rates in the Northeast region population, and among the transient population, were more than double compared to the non-Northeast region population. The highest incidence rates occurred in males aged 45–59 years old for all regions. The average incidence rate of melioidosis between 2005 and 2035 was predicted to be 11.42 to 12.78 per 100,000 population per year, with a slightly increasing trend. Overall, it was estimated that about half of all cases of melioidosis were symptomatic. In addition, the model suggested a greater susceptibility to melioidosis in diabetic compared with non-diabetic individuals. Conclusions/Significance The increasing trend of melioidosis incidence rates was significantly higher among working-age Northeast and transient populations, males aged ≤45 years old, and diabetic individuals. Targeted intervention strategies, such as health education and awareness raising initiatives, should be implemented on high-risk groups, such as those living in the Northeast region, and the seasonally transient population.
author2 London School of Hygiene & Tropical Medicine
author_facet London School of Hygiene & Tropical Medicine
Wiriya Mahikul
Lisa J. White
Kittiyod Poovorawan
Ngamphol Soonthornworasiri
Pataporn Sukontamarn
Phetsavanh Chanthavilay
Graham F. Medley
Wirichada Panngum
format Article
author Wiriya Mahikul
Lisa J. White
Kittiyod Poovorawan
Ngamphol Soonthornworasiri
Pataporn Sukontamarn
Phetsavanh Chanthavilay
Graham F. Medley
Wirichada Panngum
author_sort Wiriya Mahikul
title Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis
title_short Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis
title_full Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis
title_fullStr Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis
title_full_unstemmed Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis
title_sort modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis
publishDate 2020
url https://repository.li.mahidol.ac.th/handle/123456789/51698
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