College of Behavioral & Social Sciences

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Author: search.filters.author.Stewart, Kathleen

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    Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
    (Springer Nature, 2023-02-13) 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
    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. 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. 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. 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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    Detecting geospatial patterns of Plasmodium falciparum parasite migration in Cambodia using optimized estimated effective migration surfaces
    (Springer Nature, 2020-04-10) Li, Yao; Shetty, Amol C.; Lon, Chanthap; Spring, Michele; Saunders, David L.; Fukuda, Mark M.; Hien, Tran Tinh; Pukrittayakamee, Sasithon; Fairhurst, Rick M.; Dondorp, Arjen M.; Plowe, Christopher V.; O’Connor, Timothy D.; Takala-Harrison, Shannon; Stewart, Kathleen
    Understanding the genetic structure of natural populations provides insight into the demographic and adaptive processes that have affected those populations. Such information, particularly when integrated with geospatial data, can have translational applications for a variety of fields, including public health. Estimated effective migration surfaces (EEMS) is an approach that allows visualization of the spatial patterns in genomic data to understand population structure and migration. In this study, we developed a workflow to optimize the resolution of spatial grids used to generate EEMS migration maps and applied this optimized workflow to estimate migration of Plasmodium falciparum in Cambodia and bordering regions of Thailand and Vietnam. The optimal density of EEMS grids was determined based on a new workflow created using density clustering to define genomic clusters and the spatial distance between genomic clusters. Topological skeletons were used to capture the spatial distribution for each genomic cluster and to determine the EEMS grid density; i.e., both genomic and spatial clustering were used to guide the optimization of EEMS grids. Model accuracy for migration estimates using the optimized workflow was tested and compared to grid resolutions selected without the optimized workflow. As a test case, the optimized workflow was applied to genomic data generated from P. falciparum sampled in Cambodia and bordering regions, and migration maps were compared to estimates of malaria endemicity, as well as geographic properties of the study area, as a means of validating observed migration patterns. Optimized grids displayed both high model accuracy and reduced computing time compared to grid densities selected in an unguided manner. In addition, EEMS migration maps generated for P. falciparum using the optimized grid corresponded to estimates of malaria endemicity and geographic properties of the study region that might be expected to impact malaria parasite migration, supporting the validity of the observed migration patterns. Optimized grids reduce spatial uncertainty in the EEMS contours that can result from user-defined parameters, such as the resolution of the spatial grid used in the model. This workflow will be useful to a broad range of EEMS users as it can be applied to analyses involving other organisms of interest and geographic areas.
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    Modeling spatial access to cervical cancer screening services in Ondo State, Nigeria
    (Springer Nature, 2020-07-21) Stewart, Kathleen; Li, Moying; Xia, Zhiyue; Adewole, Stephen Ayodele; Adeyemo, Olusegun; Adebamowo, Clement
    Women in low- and middle-income countries (LMIC) remain at high risk of developing cervical cancer and have limited access to screening programs. The limits include geographical barriers related to road network characteristics and travel behaviors but these have neither been well studied in LMIC nor have methods to overcome them been incorporated into cervical cancer screening delivery programs. To identify and evaluate spatial barriers to cervical cancer prevention services in Ondo State, Nigeria, we applied a Multi-Mode Enhanced Two-Step Floating Catchment Area model to create a spatial access index for cervical cancer screening services in Ondo City and the surrounding region. The model used inputs that included the distance between service locations and population centers, local population density, quantity of healthcare infrastructures, modes of transportation, and the travel time budgets of clients. Two different travel modes, taxi and mini bus, represented common modes of transit. Geocoded client residential locations were compared to spatial access results to identify patterns of spatial access and estimate where gaps in access existed. Ondo City was estimated to have the highest access in the region, while the largest city, Akure, was estimated to be in only the middle tier of access. While 73.5% of clients of the hospital in Ondo City resided in the two highest access zones, 21.5% of clients were from locations estimated to be in the lowest access catchment, and a further 2.25% resided outside these limits. Some areas that were relatively close to cervical cancer screening centers had lower access values due to poor road network coverage and fewer options for public transportation. Variations in spatial access were revealed based on client residential patterns, travel time differences, distance decay assumptions, and travel mode choices. Assessing access to cervical cancer screening better identifies potentially underserved locations in rural Nigeria that can inform plans for cervical cancer screening including new or improved infrastructure, effective resource allocation, introduction of service options for areas with lower access, and design of public transportation networks.