Optimising predictive modelling of Ross River virus using meteorological variables

Iain S Koolhof, Simon M Firestone, Silvana Bettiol, Michael Charleston, Katherine B Gibney, Peter J Neville, Andrew Jardine and Scott Carver

PLOS Neglected Tropical Diseases, 2021, vol. 15, issue 3, 1-21

Abstract: Background: 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. Methodology/Principal findings: 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 models’ 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. However, this approach did not result in as many best fit models than when not using this approach. Conclusions/Significance: We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or visa 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. Author summary: Mosquito-borne diseases cause significant illness worldwide. Mosquito breeding which leads to disease transmission is driven by favorable climatic and meteorological events (i.e., rainfall and warm temperatures). Understanding the association meteorological conditions have with mosquito breeding aids in directing mosquito control activities when there is a likelihood of disease transmission. Predictive models are used in public health decision making and resource allocation to guide mosquito control programs. However, there are multiple modelling methods all of which provide differing degrees of accuracy in their predictions and suitability to the disease transmission dynamics. This study aims to assess commonly used statistical models for predicting mosquito-borne disease notifications and outbreaks. 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. Furthermore, a model suited to predicting disease notifications may not always be the best model to accurately predict the occurrence of disease outbreaks. The methods used here can aid in public health to establish suitable predictive mosquito-borne disease surveillance systems to help guide disease prevention and resource allocation, and mosquito control activities.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pntd00:0009252

DOI: 10.1371/journal.pntd.0009252

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