Relative risk analysis of dengue cases using convolution extended into spatio-temporal model

Dengue Hemmorage Fever (DHF) cases have become a serious problem every year in tropical countries such as Indonesia. Understanding the dynamic spread of the disease is essential in order to find an effective strategy in controlling its spread. In this study, a convolution (Poisson-lognormal) model that integrates both uncorrelated and correlated random effects was developed. A spatial-temporal convolution model to accomodate both spatial and temporal variations of the disease spread dynamics was considered. The model was applied to the DHF cases in the city of Kendari, Indonesia. DHF data for 10 districts during the period 2007-2010 were collected from the health services. The data of rainfall and population density were obtained from the local offices in Kendari. The numerical experiments indicated that both the rainfall and the population density played an important role in the increasing DHF cases in the city of Kendari. The result suggested that DHF cases mostly occured in January, the wet session with high rainfall, and in Kadia, the densest district in the city. As people in the city have high mobility while dengue mosquitoes tend to stay localized in their area, the best intervention is in January and in the district of Kadia.