Optimising predictive modelling of Ross River virus using meteorological variables

Background Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists determining factors associated with disease transmission, however, often overlooked this process is evaluation suitability statistical model transmission outbreaks. Here we aim evaluate several modelling methods optimise predictive Ross River virus (RRV) notifications outbreaks epidemiological important regions Victoria Western Australia. Methodology/Principal findings We developed using meteorological RRV data from July 2000 until June 2018 1991 Models were for 11 Local Government Areas (LGAs) seven LGAs found generalised additive boosted regression models, negative binomial be best fit when predicting notifications, respectively. No association was a model’s ability predict greater activity, or outbreak predictions have higher accuracy notifications. Moreover, assessed use factor analysis generate independent variables modelling. In majority LGAs, method did not result better performance. Conclusions/Significance demonstrate that which may suitable outbreaks, vice versa . Furthermore, poor performance transmissions inappropriate methods. Our provide approaches facilitate mosquito-borne surveillance.