Modeling the influence of climatic factors on the number of dengue hemorrahagic fever (DHF) patients in DKI Jakarta 2017-2020 using generalized linear mixed model

Abstract

The number of DBD patients in DKI Jakarta in 2017-2020 is counted data so Poisson regression can be used to modellling the relationship between climatic factors and the number of DBD patients. However, in its application, this model has violated the overdispersion assumption so that handling is carried out using the Generalized Linear Mixed Model (GLMM) with Poisson regression and Negative Binom regression. The GLMM model was used to accommodate the random effects of measurement time and measurement location. For both models, the Autoregressive 1 (AR1) variance matrix is used because there is a strong correlation between observations and previous observations. The GLMM model with Negative Binom regression is considered the best model because it has a lower AIC value than the GLMM model AIC with Poisson regression. In this model, only the variables of average temperature per month and average humidity per month have a significant effect on the number of DBD patients in DKI Jakarta in 2017-2020 at the 5% significance level.