Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
Abstract 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 m...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-02-01
|
| Series: | Malaria Journal |
| Online Access: | https://doi.org/10.1186/s12936-023-04478-6 |
| _version_ | 1827985110934224896 |
|---|---|
| author | Natalie Memarsadeghi Kathleen Stewart Yao Li Siriporn Sornsakrin Nichaphat Uthaimongkol Worachet Kuntawunginn Kingkan Pidtana Chatree Raseebut Mariusz Wojnarski Krisada Jongsakul Danai Jearakul Norman Waters Michele Spring Shannon Takala-Harrison |
| author_facet | Natalie Memarsadeghi Kathleen Stewart Yao Li Siriporn Sornsakrin Nichaphat Uthaimongkol Worachet Kuntawunginn Kingkan Pidtana Chatree Raseebut Mariusz Wojnarski Krisada Jongsakul Danai Jearakul Norman Waters Michele Spring Shannon Takala-Harrison |
| author_sort | Natalie Memarsadeghi |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T23:09:37Z |
| format | Article |
| id | doaj.art-4e1d0b152ff0461cbee3621419165c96 |
| institution | Directory Open Access Journal |
| issn | 1475-2875 |
| language | English |
| last_indexed | 2024-04-09T23:09:37Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Malaria Journal |
| spelling | doaj.art-4e1d0b152ff0461cbee3621419165c962023-03-22T10:28:32ZengBMCMalaria Journal1475-28752023-02-0122111110.1186/s12936-023-04478-6Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modellingNatalie Memarsadeghi0Kathleen Stewart1Yao Li2Siriporn Sornsakrin3Nichaphat Uthaimongkol4Worachet Kuntawunginn5Kingkan Pidtana6Chatree Raseebut7Mariusz Wojnarski8Krisada Jongsakul9Danai Jearakul10Norman Waters11Michele Spring12Shannon Takala-Harrison13Department of Geographical Sciences, University of MarylandDepartment of Geographical Sciences, University of MarylandDepartment of Geographical Sciences, University of MarylandArmed Forces Research Institute of Medical Sciences (AFRIMS)Armed Forces Research Institute of Medical Sciences (AFRIMS)Armed Forces Research Institute of Medical Sciences (AFRIMS)Armed Forces Research Institute of Medical Sciences (AFRIMS)Division of Vector Borne Diseases, Ministry of Public HealthArmed Forces Research Institute of Medical Sciences (AFRIMS)Armed Forces Research Institute of Medical Sciences (AFRIMS)Division of Vector Borne Diseases, Ministry of Public HealthArmed Forces Research Institute of Medical Sciences (AFRIMS)Armed Forces Research Institute of Medical Sciences (AFRIMS)Center for Vaccine Development and Global Health, University of Maryland School of MedicineAbstract 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.https://doi.org/10.1186/s12936-023-04478-6 |
| spellingShingle | Natalie Memarsadeghi Kathleen Stewart Yao Li Siriporn Sornsakrin Nichaphat Uthaimongkol Worachet Kuntawunginn Kingkan Pidtana Chatree Raseebut Mariusz Wojnarski Krisada Jongsakul Danai Jearakul Norman Waters Michele Spring Shannon Takala-Harrison Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling Malaria Journal |
| 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 |
| url | https://doi.org/10.1186/s12936-023-04478-6 |
| work_keys_str_mv | AT nataliememarsadeghi understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT kathleenstewart understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT yaoli understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT siripornsornsakrin understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT nichaphatuthaimongkol understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT worachetkuntawunginn understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT kingkanpidtana understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT chatreeraseebut understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT mariuszwojnarski understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT krisadajongsakul understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT danaijearakul understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT normanwaters understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT michelespring understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling AT shannontakalaharrison understandingworkrelatedtravelanditsrelationtomalariaoccurrenceinthailandusinggeospatialmaximumentropymodelling |