Mapping Leprosy Distribution with Geographically Weighted Bivariate Zero Inflated Poisson Regression Method
Siti Masliyah Lubis1, Henny Pramoedyo2, Suci Astutik3
1Siti Masliyah Lubis, Master Student of Statistics Departement, Brawijaya Universiy, Malang, Indonesia.
2Henny Pramoedyo, Lecturer of Statistics Departement, Brawijaya University, Malang, Indonesia.
3Suci Astutik, department, Lecturer of Statistics Departement, Brawijaya University, Malang, Indonesia.
Manuscript received on 6 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 3034-3037 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4859098319/2019©BEIESP | DOI: 10.35940/ijrte.C4859.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Geographically Weighted Bivariate Zero Inflated Poisson regression modelling has been developed to evaluate overdispersion and spatial heterogeneity in factors the number of PB Leprosy and MB Leprosy Cases in North Sumatera Province in 2017. The modelling results shows there are 25 different models for each district. PB Leprosy cases are mostly influenced by the percentage of poor people and the percentage of healthy houses and MB Leprosy cases are influenced by percentage of poor people, percentage of clean and healthy life behavior, the ratio of medical personnel and the percentage of healthy houses.
Keywords: Leprosy, Overdispersion, Geographically Weighted Bivariate Zero Inflated Poisson Regression.
Scope of the Article: Regression and Prediction