Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis.
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...
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ftdoajarticles:oai:doaj.org/article:05e8bc979a6f49eab5d0a31731dc15e5 2023-12-10T09:46:14+01:00 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 Pan-Ngum 2019-05-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0007380 https://doaj.org/article/05e8bc979a6f49eab5d0a31731dc15e5 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0007380 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0007380 https://doaj.org/article/05e8bc979a6f49eab5d0a31731dc15e5 PLoS Neglected Tropical Diseases, Vol 13, Iss 5, p e0007380 (2019) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2019 ftdoajarticles https://doi.org/10.1371/journal.pntd.0007380 2023-11-12T01:39:46Z 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 ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 13 5 e0007380 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Wiriya Mahikul Lisa J White Kittiyod Poovorawan Ngamphol Soonthornworasiri Pataporn Sukontamarn Phetsavanh Chanthavilay Graham F Medley Wirichada Pan-Ngum Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis. |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
Wiriya Mahikul Lisa J White Kittiyod Poovorawan Ngamphol Soonthornworasiri Pataporn Sukontamarn Phetsavanh Chanthavilay Graham F Medley Wirichada Pan-Ngum |
author_facet |
Wiriya Mahikul Lisa J White Kittiyod Poovorawan Ngamphol Soonthornworasiri Pataporn Sukontamarn Phetsavanh Chanthavilay Graham F Medley Wirichada Pan-Ngum |
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. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2019 |
url |
https://doi.org/10.1371/journal.pntd.0007380 https://doaj.org/article/05e8bc979a6f49eab5d0a31731dc15e5 |
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Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 13, Iss 5, p e0007380 (2019) |
op_relation |
https://doi.org/10.1371/journal.pntd.0007380 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0007380 https://doaj.org/article/05e8bc979a6f49eab5d0a31731dc15e5 |
op_doi |
https://doi.org/10.1371/journal.pntd.0007380 |
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PLOS Neglected Tropical Diseases |
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5 |
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