Risk management, signal processing and econometrics: A new tool for forecasting the risk of disease outbreaks

This paper takes a novel approach for forecasting the risk of disease emergence by combining risk management, signal processing and econometrics to develop a new forecasting approach. We propose quantifying risk using the Value at Risk criterion and then propose a two staged model based on Multivariate Singular Spectrum Analysis and Quantile Regression (MSSA-QR model). The proposed risk measure (PLVaR) and forecasting model (MSSA-QR) is used to forecast the worst cases of waterborne disease outbreaks in 22 European and North American countries based on socio-economic and environmental indica- tors. The results show that the proposed method perfectly forecasts the worst case scenario for less com- mon waterborne diseases whilst the forecasting of more common diseases requires more socio-economic and environmental indicators.