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Abstract BackgroundEstimating 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 (primarilyPlasmodium vivax)in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution ofP. vivaxmalaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes. MethodsA maximum entropy model was trained to estimate the distribution ofP. vivaxmalaria 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. ResultsThe MaxEnt (full name) model indicated a higher occurrence ofP. vivaxmalaria 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. ConclusionThe 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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This content will become publicly available on February 1, 2026
What motivates the choice to custom hire pest management spraying services?
Abstract BACKGROUNDThis article presents a model of how farmers choose to custom hire for pest control. The decision‐making process is illustrated through a discrete choice experiment conducted via a pilot survey of soybean growers in Michigan, Illinois, and Indiana. Farmers responded to a hypothetical pest infestation by choosing between custom operators, spraying on their own, or leaving the field to its fate. RESULTSAmong farmers who choose to spray, the mean willingness to pay for marginal increases in timeliness (as defined as the chance of late spraying) ranges from 37 to 52 cents per acre. We also find that farmers more averse to risk are more sensitive to custom operator timeliness and that farmers with better‐developed social networks are less sensitive to the risk of delay. CONCLUSIONThe results of this study can motivate future research into the drivers of on‐farm decision‐making, especially as it relates to custom hire behavior in pest control and other field operations. © 2024 The Author(s).Pest Management Sciencepublished by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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- Award ID(s):
- 2224712
- PAR ID:
- 10613694
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Pest Management Science
- Volume:
- 81
- Issue:
- 2
- ISSN:
- 1526-498X
- Page Range / eLocation ID:
- 912 to 922
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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