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  1. 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 (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.

    Methods

    A 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.

    Results

    The 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.

    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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    Free, publicly-accessible full text available December 1, 2024
  2. Abstract Background

    More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar.

    Methods

    An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation.

    Results

    Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups.

    Conclusions

    Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.

     
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  3. The estimation of malaria parasite migration can play a vital role in informing elimination strategies by pinpointing regions with higher parasite migration that act as transmission sources, and that could be the focus of elimination interventions. Gene flow simulation methods such as Estimated Effective Migration Surfaces (EEMS) and Migration and Population-Size Surfaces (MAPS) use a Markov Chain Monte Carlo simulation-based approach to visualize a species' migration and diversity. These methods utilize georeferenced genomic data and present output in the form of migration contour maps. Despite their potential, there is uncertainty in EEMS and MAPS outputs when sampling locations are sparse - an aspect that remains under-explored in current research. We present a framework designed to systematically assess the impact of sample locations and sample size on migration contours in gene flow simulations that goes beyond the posterior probability map available in EEMS. We test our framework using publicly available genomic data collected from Cambodia and border regions of Thailand, Vietnam, and Laos during 2008-2013. The methodology leverages kernel density estimation and topological skeletons in conjunction with other spatial analysis methods to quantify the impact of sparse sample locations on gene flow simulations. Multiple sample resolutions were tested against a baseline resolution, and the findings highlight how migration contours vary with sampling resolution and how our approach can be applied to guide the production and mapping of reliable migration contours. Our research provides valuable insights about both the reliability and precision of model outputs when employing gene flow simulation techniques e.g., EEMS and MAPS, to estimate malaria parasite migration. The findings revealed that by employing our approach, we were able to maintain approximately 67% consistency between the contours and the reference dataset, even when utilizing only half of the sample locations. This knowledge will improve both the reliability and precision of these model outputs in future studies. 
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