Zarebski Alexander E, Dawson Peter, McCaw James M, Moss Robert
School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.
Land Personnel Protection Branch, Land Division, Defence Science and Technology Organisation, Melbourne, Australia.
Infect Dis Model. 2017 Jan 10;2(1):56-70. doi: 10.1016/j.idm.2016.12.004. eCollection 2017 Feb.
Epidemics of seasonal influenza inflict a huge burden in temperate climes such as Melbourne (Australia) where there is also significant variability in their timing and magnitude. Particle filters combined with mechanistic transmission models for the spread of influenza have emerged as a popular method for forecasting the progression of these epidemics. Despite extensive research it is still unclear what the optimal models are for forecasting influenza, and how one even measures forecast performance. In this paper, we present a likelihood-based method, akin to Bayes factors, for model selection when the aim is to select for predictive skill. Here, "predictive skill" is measured by the probability of the data the forecasting date, conditional on the data from the forecasting date. Using this method we choose an optimal model of influenza transmission to forecast the number of laboratory-confirmed cases of influenza in Melbourne in each of the 2010-15 epidemics. The basic transmission model considered has the susceptible-exposed-infectious-recovered structure with extensions allowing for the effects of absolute humidity and inhomogeneous mixing in the population. While neither of the extensions provides a significant improvement in fit to the data they do differ in terms of their predictive skill. Both measurements of absolute humidity and a sinusoidal approximation of those measurements are observed to increase the predictive skill of the forecasts, while allowing for inhomogeneous mixing reduces the skill. We discuss how our work could be integrated into a forecasting system and how the model selection method could be used to evaluate forecasts when comparing to multiple surveillance systems providing disparate views of influenza activity.
季节性流感疫情在墨尔本（澳大利亚）等温带地区造成了巨大负担，其发生时间和规模也存在显著差异。粒子滤波器与流感传播的机械传播模型相结合，已成为预测这些疫情发展的常用方法。尽管进行了广泛研究，但仍不清楚预测流感的最佳模型是什么，以及如何衡量预测性能。在本文中，我们提出了一种基于似然性的方法，类似于贝叶斯因子，用于在旨在选择预测技能时进行模型选择。这里，“预测技能”是根据预测日期之前的数据来衡量数据在预测日期出现的概率。使用这种方法，我们选择了一个最佳的流感传播模型，以预测2010 - 2015年墨尔本每次流感疫情中实验室确诊的流感病例数。所考虑的基本传播模型具有易感 - 暴露 - 感染 - 康复结构，并进行了扩展，以考虑绝对湿度的影响和人群中的不均匀混合。虽然这两种扩展在拟合数据方面都没有显著改善，但在预测技能方面存在差异。观察到绝对湿度的测量值及其正弦近似值都提高了预测的预测技能，而考虑不均匀混合则降低了技能。我们讨论了如何将我们的工作整合到一个预测系统中，以及在与提供不同流感活动观点的多个监测系统进行比较时，如何使用模型选择方法来评估预测。
