Optimising predictive modelling of Ross River virus using meteorological variables

• Published in: PLoS neglected tropical diseases 2021-03, Vol.15 (3), p.e0009252
• Main Authors: Koolhof, Iain S, Firestone, Simon M, Bettiol, Silvana, Charleston, Michael, Gibney, Katherine B, Neville, Peter J, Jardine, Andrew, Carver, Scott
• Format: Article
• Language: English
• Subjects: Aquatic insects; Atmospheric models; Biology and life sciences; Climate change; Disease; Disease transmission; Distribution; Ecological effects; Epidemics; Epidemiology; Evapotranspiration; Forecasting; Health surveillance; Infectious diseases; Local government; Mean sea level; Medicine and Health Sciences; Meteorological data; Meteorology; Mosquitoes; Pathogens; People and Places; Physical Sciences; Population; Prediction models; Public health; Rain; Rainfall; Relative humidity; Research and Analysis Methods; Risk factors; Rivers; RNA virus infections; RNA viruses; Ross River virus; Sea level; Sea level pressure; Social Sciences; Statistical analysis; Statistical methods; Statistical models; Transmission; Tropical diseases; Vapor pressure; Vapour pressure; Variables; Vector-borne diseases; Viruses; Weather stations
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0009252

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