Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
PLoS Comput Biol. 2023 Sep 12;19(9):e1011453. doi: 10.1371/journal.pcbi.1011453. eCollection 2023 Sep.
Mathematical and statistical models can be used to make predictions of how epidemics may progress in the near future and form a central part of outbreak mitigation and control. Renewal equation based models allow inference of epidemiological parameters from historical data and forecast future epidemic dynamics without requiring complex mechanistic assumptions. However, these models typically ignore interaction between age groups, partly due to challenges in parameterising a time varying interaction matrix. Social contact data collected regularly during the COVID-19 epidemic provide a means to inform interaction between age groups in real-time. We developed an age-specific forecasting framework and applied it to two age-stratified time-series: incidence of SARS-CoV-2 infection, estimated from a national infection and antibody prevalence survey; and, reported cases according to the UK national COVID-19 dashboard. Jointly fitting our model to social contact data from the CoMix study, we inferred a time-varying next generation matrix which we used to project infections and cases in the four weeks following each of 29 forecast dates between October 2020 and November 2021. We evaluated the forecasts using proper scoring rules and compared performance with three other models with alternative data and specifications alongside two naive baseline models. Overall, incorporating age interaction improved forecasts of infections and the CoMix-data-informed model was the best performing model at time horizons between two and four weeks. However, this was not true when forecasting cases. We found that age group interaction was most important for predicting cases in children and older adults. The contact-data-informed models performed best during the winter months of 2020-2021, but performed comparatively poorly in other periods. We highlight challenges regarding the incorporation of contact data in forecasting and offer proposals as to how to extend and adapt our approach, which may lead to more successful forecasts in future.
数学和统计学模型可用于预测传染病在不久的将来如何发展，并成为疫情缓解和控制的核心部分。基于更新方程的模型允许从历史数据中推断出流行病学参数，而无需复杂的机械假设即可预测未来的疫情动态。然而，这些模型通常忽略了年龄组之间的相互作用，部分原因是难以参数化时变相互作用矩阵。在 COVID-19 疫情期间定期收集的社会接触数据提供了一种实时了解年龄组之间相互作用的方法。我们开发了一个特定年龄的预测框架，并将其应用于两个按年龄分层的时间序列：从全国感染和抗体流行率调查中估计的 SARS-CoV-2 感染发病率；以及根据英国国家 COVID-19 仪表板报告的病例。我们共同拟合 CoMix 研究中的社会接触数据，推断出时变的下一代矩阵，并用它来预测 2020 年 10 月至 2021 年 11 月的 29 个预测日期中的每一个之后的四周内的感染和病例。我们使用适当的评分规则评估预测，并将性能与具有替代数据和规范的另外三个模型以及两个简单基准模型进行比较。总体而言，纳入年龄相互作用可以改善对感染的预测，并且 CoMix 数据驱动的模型在两到四周的时间范围内是表现最好的模型。但是，在预测病例时并非如此。我们发现年龄组相互作用对预测儿童和老年人的病例最为重要。在 2020-2021 年的冬季，接触数据驱动的模型表现最佳，但在其他时期表现相对较差。我们强调了在预测中纳入接触数据的挑战，并提出了如何扩展和调整我们的方法的建议，这可能会导致未来的预测更加成功。
