Abstract
BackgroundStatistical 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.Methodology/Principal findingsWe 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.Conclusions/SignificanceWe 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.
Highlights
Meteorological factors influence the transmission ecology of pathogen, host and vector species populations, and human behaviour, which can act directly or indirectly to drive mosquitoborne disease dynamics [1,2]
Mosquito breeding, which leads to disease transmission, is driven by favorable climatic and meteorological events
We demonstrate that statistical model selection plays an important role in accurately forecasting mosquito-borne disease and poor predictive performance may be due to inappropriate model selection
Summary
Meteorological factors influence the transmission ecology of pathogen, host and vector species populations, and human behaviour, which can act directly or indirectly to drive mosquitoborne disease dynamics [1,2]. The time between meteorological events that lead to increases in mosquito populations and when mosquito-borne diseases are detected in humans represents the enzootic transmission cycle. This period includes the diseases’ intrinsic incubation period and the circulation through animal populations before transmission spilling over into human populations. The time delay preceding meteorological events (e.g., heavy rainfall), which represents the circulation and transmission of disease before the spillover into humans, make mosquito-borne diseases well suited for predictive modelling (i.e., forecasting) of outbreaks. We address this problem by assessing commonly used statistical methods in forecasting mosquito-borne disease notifications and outbreaks in Australia. 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.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
