Forecasting the magnitude of dengue in Southern Vietnam

Abstract

With recent rises of sophisticated and dangerous epidemics, there is a growing need for a system that could predict disease severity with high accuracy. In this paper, we address the problem of forecasting the magnitude of dengue in a short term period, i.e. one week ahead. We consider inputs as both statistics of historical cases and biological factors affecting the dengue virus, including the temperature, population and mosquito density. We propose a two-phase model simulating the disease transmission process, which are the local outbreak and then province transmission. The locality phase estimates the number of potential cases in each province independently in the following week. Then, in the transmission phase, an artificial neural network is used to predict the mobility of the dengue virus across provinces. Our proposed method obtains a higher accuracy than the conventional models of time series, linear regression, and ARIMA. Moreover, this provides the first research results about dengue prediction in Vietnam.