Dengue Incidence using Climate Variables as Predictors

Dengue is the most widely distributed and rapidly spreading mosquito-borne viral disease in the world. The Philippines, like many other of the developing countries, is among the most vulnerable. The study aims to investigate the relationship of morbidity with humidity, temperature, and rainfall. The model in this study was generated using the monthly data from January 2011 to December 2014. The data on the climate variables were gathered from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), and the data on the number of dengue cases in Cagayan de Oro City were gathered from the Department of Health (DOH). The first step in conducting the linear regression model was to have a cross-correlation between morbidity and the climate variables, humidity, rainfall and temperature. After the preliminary model was set, diagnostic checking followed which was to inspect visually the scatter diagram between morbidity and the residuals of the model. The next step that was followed again to compute the regression model using the two variables, temperature and rainfall as predictors. When the new variablerevealed to be insignificant, and did not improve the value of the root mean square error (RMSE). Results showed that among the three variables (humidity, temperature and rainfall), temperature had the highest correlation with morbidity. This shows that temperature could better explain the variability in the variable morbidity. The temperature is a good predictor of the morbidity rate of dengue cases in Cagayan de Oro City. However, there should be further investigation as to the unseen variables that would affect the morbidity rate.

 

Keywords: Dengue incidence, temperature, humidity, rainfall, Cagayan de Oro City