Koolhof Iain S, Firestone Simon M, Bettiol Silvana, Charleston Michael, Gibney Katherine B, Neville Peter J, Jardine Andrew, Carver Scott
College of Health and Medicine, School of Medicine, University of Tasmania, Hobart, Tasmania, Australia.
School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia.
PLoS Negl Trop Dis. 2021 Mar 9;15(3):e0009252. doi: 10.1371/journal.pntd.0009252. eCollection 2021 Mar.
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 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/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.
统计模型常用于传染病的预测和监测，以指导公共卫生工作。变量选择有助于确定与疾病传播相关的因素，然而，在此过程中，用于预测疾病传播和暴发的统计模型的评估和适用性常常被忽视。在此，我们旨在评估几种建模方法，以优化对维多利亚州和西澳大利亚州重要流行病学区域的罗斯河病毒（RRV）疾病通报和疫情的预测建模。
方法/主要发现：我们利用2000年7月至2018年6月维多利亚州以及1991年7月至2018年6月西澳大利亚州的气象和RRV监测数据，开发了几种统计方法。针对维多利亚州的11个地方政府区域（LGA）和西澳大利亚州的7个LGA建立了模型。我们发现，广义相加模型和广义增强回归模型分别是预测RRV疫情和通报的最佳拟合模型。未发现模型预测RRV活动较多的LGA中RRV通报的能力，或预测RRV通报较多的LGA中疫情的准确性之间存在关联。此外，我们评估了使用因子分析来生成预测建模中使用的自变量。在大多数LGA中，这种方法并未带来更好的模型预测性能。
结论/意义：我们证明，开发用于预测疾病通报的模型可能不适用于预测疾病暴发，反之亦然。此外，在疾病传播建模中预测性能不佳可能是模型选择方法不当的结果。我们的研究结果提供了方法和途径，以促进选择最适合的统计模型来预测用于疾病监测的蚊媒疾病通报和暴发。
