Integrating ARIMA and Spatiotemporal Bayesian Networks for High Resolution Malaria Prediction

Since malaria is prevalent in less developed and more remote areas in which public health resources are often scarce, targeted intervention is essential in allocating resources for effective malaria control. To effectively support targeted intervention, predictive models must be not only accurate but they must also have high temporal and spatial resolution to help determine when and where to intervene. In this paper we take the first essential step towards a system to support targeted intervention in Thailand by developing a high resolution prediction model through the combination of Bayes nets and ARIMA. Bayes nets and ARIMA have complementary strengths, with the Bayes nets better able to represent the effect of environmental variables and ARIMA better able to capture the characteristics of the time series of malaria cases. Leveraging these complementary strengths, we develop an ensemble predictor from the two that has significantly better accuracy that either predictor alone. We build and test the models with data from Tha Song Yang district in northern Thailand, creating village-level models with weekly temporal resolution.