Abstract
The number of confirmed cases of malaria in East Java from 2020 to 2022 is a count data with numerous zero values and is exhibiting overdispersion. With Zero Inflated Poisson (ZIP) regression, this work seeks to solve the overdispersion issue that plagues Poisson regression. The ZIP model is superior to the Poisson regression model, according to the results. Data modeling with ZIP regression carries out model fit testing with the G test, parameter significance testing with the Wald test, and parameter estimation using Maximum Likelihood Estimation. For case data of malaria positivity, the ZIP regression model in this research is log(𝜇𝑖) = 3,695 + 0,0057X1 − 0,0569X2 − 0,0085X3 − 0,0619X4 + 0,0604X5 − 0,0118X6 − 0,012X8, and logit(𝜔𝑖) = −5,5185 + 0,0039X2 − 0,592X6. The test's findings indicate that factors such as population density, poverty levels, the proportion of households with access to clean water and sanitation, the morbidity rate, the number of medical facilities, the number of health complaints, and the percentage of public spaces that adhere to health standards all significantly influence the number of confirmed cases of malaria in East Java.Keywords: Malaria, excess zeros, overdispersion, Zero Inflated Poisson.
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