MIDAS Poisson regression advancements in dengue fever prediction using Google Trends and environmental data

This paper proposes a new approach to a generalization of Poisson regression for count time series data typically sampled at different frequencies to improve the forecasting accuracy of dengue cases in Pakistan. Mixed data sampling (MIDAS) is introduced in the context of Poisson regression, as it enables us to model and forecast dengue events more accurately. Different selected polynomial weights from the literature are applied in MIDAS and U-MIDAS settings, and different forecast combinations are used to improve the forecasting accuracy of dengue event counts. For the period 2006-2017, the proposed model correctly forecasts significantly more dengue cases than does the standard Poisson regression model for all forecasting horizons. Furthermore, Google trends data can be a useful addition to traditional numeric data to forecast dengue cases.