Zero Inflated Negative Binomial Regression In Malaria Cases In North Sumatera

Riska Aulia Dalimunthe, Ismail Husein

Abstract


Malaria cases in North Sumatra Province continue to be a public health concern, with regional incidence rates varying. This investigation was designed to assess the variables that contribute to the prevalence of malaria in the province by employing the Zero Inflated Negative Binomial (ZINB) regression model.. The response variable is the number of malaria cases, while the predictor variables are the number of impoverished individuals, population density, percentage of households with proper sanitation, number of healthy homes, and rainfall. For the year 2022, secondary data was acquired from the North Sumatra Provincial Health Office. Excess zero, overdispersion, and multicollinearity tests were conducted prior to the implementation of the ZINB model. The study results indicated that the ZINB model was more suitable than the Poisson and Negative Binomial models. The data indicates that the following variables have a substantial impact on malaria prevalence: an increase in the number of individuals living in poverty (X₁) by 7.8%, an increase in population density (X₂) by 1.8%, an increase in the percentage of households with adequate sanitation facilities (X₃) by 5.9%, and an increase in the percentage of rainfall (X₅) by 3.3%.

Keywords


Malaria, overdispersion, excess zero, North Sumatra, Zero Inflated Negative Binomial

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DOI: http://dx.doi.org/10.30829/jistech.v10i1.25658

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