Mapping Child Leprosy Cases and Its Factors Associated in Indonesia Using Geographically Weighted Poisson Regression Model.

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Title:	Mapping Child Leprosy Cases and Its Factors Associated in Indonesia Using Geographically Weighted Poisson Regression Model.
Authors:	Suyitno¹ suyitno@fmipa.unmul.ac.id, Darnah² darnahfmipaunmul@gmail.com, Hayati, Memi Nor³ meminorhayati@fmipa.unmul.ac.id, Prangga, Surya² suryapranggae@fmipa.unmul.ac.id, Dani, Andrea Tri Rian³ andreatririandani@fmipa.unmul.ac.id, Mahmuda, Siti¹ sitimahmuda@fmipa.unmul.ac.id, Tumilaar, Rinancy¹ rinancytumilaar@gmail.com, Nugraha, Pratama Yuly² pratamayn@fmipa.unmul.ac.id, Aulia, Misbah Nur³ misbahnuraulia3403@gmail.com
Source:	IAENG International Journal of Applied Mathematics. May2026, Vol. 56 Issue 5, p1654-1667. 14p.
Subjects:	Hansen's disease, Geographic spatial analysis, Health policy, Indonesians, Population density, Poverty, Vaccination coverage
Geographic Terms:	Indonesia
Abstract:	This study applies Geographically Weighted Poisson Regression (GWPR) to analyze child leprosy cases in Indonesia, aiming to identify spatial variations and influencing factors. Leprosy remains a significant health issue, particularly for children, causing severe disabilities. The GWPR model accounts for spatial heterogeneity, offering a localized approach to understanding leprosy trends. Key findings reveal that global factors like poverty rates and local factors such as population density and immunization coverage are critical in influencing leprosy prevalence. The model provides more accurate, regionspecific insights into leprosy distribution, highlighting areas needing targeted interventions. This study emphasizes the importance of tailored health policies to address regional disparities in leprosy cases and improve recovery rates. The methodology includes Poisson regression, maximum likelihood estimation, and spatial heterogeneity testing. Results are used to recommend policy changes to prevent leprosy spread and improve recovery, focusing on children. This research provides valuable guidance for the Indonesian government in developing practical, region-specific strategies to combat leprosy and enhance child health outcomes. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	This study applies Geographically Weighted Poisson Regression (GWPR) to analyze child leprosy cases in Indonesia, aiming to identify spatial variations and influencing factors. Leprosy remains a significant health issue, particularly for children, causing severe disabilities. The GWPR model accounts for spatial heterogeneity, offering a localized approach to understanding leprosy trends. Key findings reveal that global factors like poverty rates and local factors such as population density and immunization coverage are critical in influencing leprosy prevalence. The model provides more accurate, regionspecific insights into leprosy distribution, highlighting areas needing targeted interventions. This study emphasizes the importance of tailored health policies to address regional disparities in leprosy cases and improve recovery rates. The methodology includes Poisson regression, maximum likelihood estimation, and spatial heterogeneity testing. Results are used to recommend policy changes to prevent leprosy spread and improve recovery, focusing on children. This research provides valuable guidance for the Indonesian government in developing practical, region-specific strategies to combat leprosy and enhance child health outcomes. [ABSTRACT FROM AUTHOR]
ISSN:	19929978