Modeling Dengue Fever by using Conditional Autoregressive Bessag-York-Mollie

Abstract

Dengue fever is a tropical disease caused by the dengue virus. The small proportional of this dengue fever disease can develops into a more severe dengue hemorrhagic fever (DHF). This research discussed about the model for disease mapping in Ciamis District. The purpose of this research is to characterize relative risk and factors correlated with case DHF. Independent variables used in this research are population density, attitude of region, and the number of health worker. To analysis this data, we used conditional autoregressive Bessag-York-Mollie (CAR-BYM) model. Based on descriptive statistic, the maximum and minimum DHF cases are Ciamis and Sukamantri, respectively. Furthermore, basedon model results, we found that the maximum and minimum relative risk are Cijeungjing and Sukamantri, respectively. Furthermore, there were 7 sub districts which relative risk are greater than one and 20 sub districts which relative risk are less than one. The sub districts which relative risk are greater than one show that DHF cases in these sub districts are greater than expectation. Based on the CAR-BYM model result showed that Each increase the population density by one unit contributes to the addition of DHF cases by 0.0012 units. Each additional health worker one unit, it will reduce the number of DHF cases by 0.0675 units. Each additional altitude of one unit will reduce the number of dengue cases by 0.0011 units. Based on relative risk (RR) value of the CAR-BYM model we found that the Cijengjing and Ciamis Districts have darkest color. The RR values in the two sub-districts are 3,449 and 3,240, respectively. The RR values of the two sub-districts are more than expected values.