A predictive model for Dengue Hemorrhagic Fever epidemics

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Abstract
A statistical model for predicting monthly Dengue Hemorrhagic Fever (DHF) cases from the city of Makassar is developed and tested. The model uses past and present DHF cases, climate and meteorological observations as inputs. These inputs are selected using a stepwise regression method to predict future DHF cases. The model is tested independently and its skill assessed using two skill measures. Using the selected variables as inputs, the model is capable of predicting a moderately-severe epidemic at lead times of up to six months. The most important input variable in the prediction is the present number of DHF cases followed by the relative humidity three to four months previously. A prediction 1–6 months in advance is sufficient to initiate various activities to combat DHF epidemic. The model is suitable for warning and easily becomes an operational tool due to its simplicity in data requirement and computational effort.