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Optimising predictive modelling of Ross River virus using meteorological variables


Koolhof, IS and Firestone, SM and Bettiol, S and Charleston, M and Gibney, KB and Neville, PJ and Jardine, A and Carver, S, Optimising predictive modelling of Ross River virus using meteorological variables, PLoS Neglected Tropical Diseases, 15, (3) Article e0009252. ISSN 1935-2727 (2021) [Refereed Article]


Copyright Statement

Copyright: © 2021 Koolhof et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.1371/journal.pntd.0009252


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 modelís 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/Significance: 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.

Item Details

Item Type:Refereed Article
Keywords:disease surveillance, forecasting, mosquito-borne disease, vector-borne disease, transmission, modelling
Research Division:Health Sciences
Research Group:Epidemiology
Research Field:Disease surveillance
Objective Division:Health
Objective Group:Clinical health
Objective Field:Prevention of human diseases and conditions
UTAS Author:Koolhof, IS (Mr Iain Koolhof)
UTAS Author:Bettiol, S (Dr Silvana Bettiol)
UTAS Author:Charleston, M (Professor Michael Charleston)
UTAS Author:Carver, S (Associate Professor Scott Carver)
ID Code:143688
Year Published:2021
Deposited By:Office of the School of Natural Sciences
Deposited On:2021-03-30
Last Modified:2021-04-28
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