Research of Dengue Fever Prediction in San Juan, Puerto Rico Based on a KNN Regression Model

Abstract

Existed dengue prediction model associated with temperature data are always based on Poisson regression methods or linear models. However, these models are difficult to be applied to non-stationary climate data, such as rainfall or precipitation. A novel k-nearest neighbor (KNN) regression method was proposed to improve the prediction accuracy of dengue fever regression model in this paper. The dengue cases and the climatic factors (average minimum temperature, average maximum temperature, average temperature, average dew point temperature, temperature difference, relative humidity, absolute humidity, Precipitation) in San Juan, Puerto Rico during the period 1990–2013 were regressed by the KNN algorithm. The performances of KNN regression were studied by compared with correlation analysis and Poisson regression method. Results showed that the KNN model fitted real dengue outbreak better than Poisson regression method while the root mean square error was 6.88.