Pumhirunroj, B.; Littidej, P.; Boonmars, T.; Bootyothee, K.; Artchayasawat, A.; Wongkalasin, A.; Lertsiriudom, E.; Khamphilung, P.; Slack, D. Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling. Preprints2023, 2023060933. https://doi.org/10.20944/preprints202306.0933.v1
APA Style
Pumhirunroj, B., Littidej, P., Boonmars, T., Bootyothee, K., Artchayasawat, A., Wongkalasin, A., Lertsiriudom, E., Khamphilung, P., & Slack, D. (2023). Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling. Preprints. https://doi.org/10.20944/preprints202306.0933.v1
Chicago/Turabian Style
Pumhirunroj, B., Phusit Khamphilung and Donald Slack. 2023 "Spatial Analysis of Influence Factors Associated With Liver Fluke (Opisthorchis viverrini) Infection in Small Sub-Watershed Using GWR Modeling" Preprints. https://doi.org/10.20944/preprints202306.0933.v1
Abstract
Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. Spatial monitoring of fluke infection at the small -basin analysis scale is important because this can enable analysis at the level of the spatial factors involved and influencing infections. A geographic weighted regression model was developed to analyze the spatial characteristics of liver fluke infection, aiming to 1. analyze the spatial factors associated with human liver fluke infection according to sub-basin boundaries and 2. generate an alternative model for enhancing the effectiveness of preventive public health management to reduce the risk of liver fluke infection in humans. The number of infected persons was obtained from local authorities and converted into a percentage of infected people and generated as raster data with a heat map so that the data were continuous and defined as dependent variables. The independent set consisted of nine variables, both vector and raster data, that correlated the location with the village location of an infected person. The results showed that the variables X5stream, X7ndmi, and X9savi were statistically significantly correlated to the percentage of infected people, with the t-stat and p-value being (-2.068, 1.875, and -2.661) and (0.048, 0.034, and 0.021), respectively. The GWR model was able to increase accuracy more than the comparable models such as OLS, in all tests of the four alternative models, with an accuracy increase in R2 of 7.69% (0.576 to 0.624). This study confirms that the development of spatial models with GWR models can screen for factors associated with liver fluke infection at the level of small spatial units such as sub-basins.
Public Health and Healthcare, Public, Environmental and Occupational Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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