Prediction of Ross River virus incidence in Queensland, Australia: building and comparing models

PeerJ

article

Wei Qian¹  David Harley¹  Kathryn Glass²  Elvina Viennet³  Cameron Hurst⁵ 

[1] The University of Queensland, UQ Centre for Clinical Research;Research School of Population Health, Australian National University;Clinical Services and Research;Institute for Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology;Molly Wardaguga Research Centre, Charles Darwin University;Department of Statistics, QIMR Berghofer Medical Research Institute

关键词: Ross River virus;    Prediction;    Modelling;    Exposures;    Disease incidence;    Over-dispersion;    Zero-inflation;   

DOI  :  10.7717/peerj.14213

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Transmission of Ross River virus (RRV) is influenced by climatic, environmental, and socio-economic factors. Accurate and robust predictions based on these factors are necessary for disease prevention and control. However, the complicated transmission cycle and the characteristics of RRV notification data present challenges. Studies to compare model performance are lacking. In this study, we used RRV notification data and exposure data from 2001 to 2020 in Queensland, Australia, and compared ten models (including generalised linear models, zero-inflated models, and generalised additive models) to predict RRV incidence in different regions of Queensland. We aimed to compare model performance and to evaluate the effect of statistical over-dispersion and zero-inflation of RRV surveillance data, and non-linearity of predictors on model fit. A variable selection strategy for screening important predictors was developed and was found to be efficient and able to generate consistent and reasonable numbers of predictors across regions and in all training sets. Negative binomial models generally exhibited better model fit than Poisson models, suggesting that over-dispersion in the data is the primary factor driving model fit compared to non-linearity of predictors and excess zeros. All models predicted the peak periods well but were unable to fit and predict the magnitude of peaks, especially when there were high numbers of cases. Adding new variables including historical RRV cases and mosquito abundance may improve model performance. The standard negative binomial generalised linear model is stable, simple, and effective in prediction, and is thus considered the best choice among all models.

【 授权许可】

CC BY