02017nas a2200277   4500000000100000008004100001260002500042653002600067653001800093653001200111653001300123653000900136100001300145700001400158700001700172700001400189700001400203700001500217700001500232700001300247245012700260856006700387300001600454490000700470520126200477       2026                            d  c05/2026bElsevier BV10aSpatial heterogeneity10aChild leprosy10aMapping10aProvince10aGWPR1 aDarnah D1 aSuyitno S1 aNor Hayati M1 aPrangga S1 aMahmuda S1 aTumilaar R1 aNugraha PY1 aAulia MN00aMapping Child Leprosy Cases and its Factors Associated in Indonesia Using Geographically Weighted Poisson Regression Model  uhttps://www.iaeng.org/IJAM/issues_v56/issue_5/IJAM_56_5_05.pdf  a1654 - 16670 v563 a<p>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.</p>