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Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling

BACKGROUND: Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria e...

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Autores principales: Memarsadeghi, Natalie, Stewart, Kathleen, Li, Yao, Sornsakrin, Siriporn, Uthaimongkol, Nichaphat, Kuntawunginn, Worachet, Pidtana, Kingkan, Raseebut, Chatree, Wojnarski, Mariusz, Jongsakul, Krisada, Jearakul, Danai, Waters, Norman, Spring, Michele, Takala-Harrison, Shannon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924182/
https://www.ncbi.nlm.nih.gov/pubmed/36782196
http://dx.doi.org/10.1186/s12936-023-04478-6
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author Memarsadeghi, Natalie
Stewart, Kathleen
Li, Yao
Sornsakrin, Siriporn
Uthaimongkol, Nichaphat
Kuntawunginn, Worachet
Pidtana, Kingkan
Raseebut, Chatree
Wojnarski, Mariusz
Jongsakul, Krisada
Jearakul, Danai
Waters, Norman
Spring, Michele
Takala-Harrison, Shannon
author_facet Memarsadeghi, Natalie
Stewart, Kathleen
Li, Yao
Sornsakrin, Siriporn
Uthaimongkol, Nichaphat
Kuntawunginn, Worachet
Pidtana, Kingkan
Raseebut, Chatree
Wojnarski, Mariusz
Jongsakul, Krisada
Jearakul, Danai
Waters, Norman
Spring, Michele
Takala-Harrison, Shannon
author_sort Memarsadeghi, Natalie
collection PubMed
description BACKGROUND: Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarily Plasmodium vivax) in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution of P. vivax malaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes. METHODS: A maximum entropy model was trained to estimate the distribution of P. vivax malaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type. RESULTS: The MaxEnt (full name) model indicated a higher occurrence of P. vivax malaria in forested areas especially along the Thailand–Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand. CONCLUSION: The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations.
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spelling pubmed-99241822023-02-14 Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling Memarsadeghi, Natalie Stewart, Kathleen Li, Yao Sornsakrin, Siriporn Uthaimongkol, Nichaphat Kuntawunginn, Worachet Pidtana, Kingkan Raseebut, Chatree Wojnarski, Mariusz Jongsakul, Krisada Jearakul, Danai Waters, Norman Spring, Michele Takala-Harrison, Shannon Malar J Research BACKGROUND: Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarily Plasmodium vivax) in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution of P. vivax malaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes. METHODS: A maximum entropy model was trained to estimate the distribution of P. vivax malaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type. RESULTS: The MaxEnt (full name) model indicated a higher occurrence of P. vivax malaria in forested areas especially along the Thailand–Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand. CONCLUSION: The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations. BioMed Central 2023-02-13 /pmc/articles/PMC9924182/ /pubmed/36782196 http://dx.doi.org/10.1186/s12936-023-04478-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Memarsadeghi, Natalie
Stewart, Kathleen
Li, Yao
Sornsakrin, Siriporn
Uthaimongkol, Nichaphat
Kuntawunginn, Worachet
Pidtana, Kingkan
Raseebut, Chatree
Wojnarski, Mariusz
Jongsakul, Krisada
Jearakul, Danai
Waters, Norman
Spring, Michele
Takala-Harrison, Shannon
Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
title Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
title_full Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
title_fullStr Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
title_full_unstemmed Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
title_short Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
title_sort understanding work-related travel and its relation to malaria occurrence in thailand using geospatial maximum entropy modelling
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924182/
https://www.ncbi.nlm.nih.gov/pubmed/36782196
http://dx.doi.org/10.1186/s12936-023-04478-6
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