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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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 |
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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 |
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© 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. |
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London School of Hygiene & Tropical Medicine |
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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 |
_version_ |
1763494231899373568 |