There are several factors that can be used to predict a dengue fever outbreak. Almost all existing research approaches, however, usually exploit the use of a basic set of core attributes to forecast an outbreak, e.g. temperature, humidity, wind speed, and rainfall. In contrast, this research identifies new attributes to improve the prediction accuracy of the outbreak. The experimental results are analyzed using a correlation analysis and demonstrate that the density of dengue virus infection rate in female mosquitoes and seasons have strong correlation with a dengue fever outbreak. In addition, the research constructs a forecast model using Poisson regression analysis. The result shows the proposed model obtains significantly low forecasting error rate when compared it against the conventional model using only temperature, humidity, wind speed, and rainfall parameters.
Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm