Forecasting of Dengue Disease Incident Risks Using Non-stationary Spatial of Geostatistics Model in Bone Regency Indonesia

Abstract

Background: Standard statistical models generally assume that any incident of dengue disease in one location are independent with the incidence of dengue disease in other locations. However, the independent assumption does not apply in the spread of dengue disease. The spread of dengue disease tends to occur almost simultaneously in a same area or in the adjacent area due to similar environmental factors in the area. Similar environmental factors results in their risk spatial correlation of disease spread. If spatial correlation aspects is not considered in modeling then the conclusion of the significant factors that influence on the risk of spreading disease becomes inaccurate. Objective: The purpose of this study was to make mapping the risk of dengue fever incidence in Bone Regency South Sulawesi province by region (districts) with non-stationary spatial geostatistics model. Materials and Methods: The analysis variables included are larvae density, temperature, population density, rainfall, altitude from sea level and the incidence rate of dengue fever. Results: The results showed that risk rate model of dengue in Bone Regency with stationary spatial geostatistics models are as follows: log(pi) = -0.08+0.006X1+0.02X2-0.02X3-0.04X4-0.01X5. X1 = The density of larvae, X2 = Air temperature, X3 = Population density, X4 = Rainfall and X5 = The height of the sea level. Risk rate model of dengue in Northern Bone Regency (cluster 1) with non-stationary spatial geostatistics models are as follow: log(pi) = -0.02-0.0009X1-0.17X2+0.003X3+0.22X4-0.006X5. Risk rate model of dengue in Southern Bone Regency (cluster 2) with non-stationary spatial geostatistics models are as follow: log(pi) = -0.02-0.02X1-0.01X2-0.02X3-0.02X40.03X5. Risk rate model of dengue in Western Bone Regency (cluster 3) with non-stationary spatial geostatistics models are as follow: log(pi) = -0.08-0.007X1-0.02X2-0.03X3-0.009X4-0.06X5. Conclusion: The prediction error values on the non-stationary model between (0.27-3.6) lower than stationary model with variation between (0.68-6.37).