Dataset for: Probabilistic forecasting in infectious disease epidemiology: The thirteenth Armitage lecture

Routine surveillance of notifiable infectious diseases gives rise to daily or weekly counts of reported cases stratified by region and age group. From a public health perspective, forecasts of infectious disease spread are of central importance. We argue that such forecasts need to properly incorporate the attached uncertainty, so should be probabilistic in nature. However, forecasts also need to take into account temporal dependencies inherent to communicable diseases, spatial dynamics through human travel, and social contact patterns between age groups. We describe a multivariate time series model for weekly surveillance counts on norovirus gastroenteritis from the 12 city districts of Berlin, in six age groups, from week 2011/27 to week 2015/26. The following year (2015/27 to 2016/26) is used to assess the quality of the predictions. Probabilistic forecasts of the total number of cases can be derived through Monte Carlo simulation, but first and second moments are also available analytically. Final size forecasts as well as multivariate forecasts of the total number of cases by age group, by district, and by week are compared across different models of varying complexity. This leads to a more general discussion of issues regarding modelling, prediction and evaluation of public health surveillance data.法定传染病常规监测可生成按地区与年龄组分层的每日或每周报告病例数。从公共卫生视角出发，传染病传播预测具有核心重要性。我们认为，此类预测需合理纳入其附带的不确定性，因此本质上应具备概率性特征。然而，预测还需充分考量传染病固有的时间依赖性、经由人类出行介导的空间动态，以及不同年龄组间的社会接触模式。我们针对柏林12个市辖区、6个年龄组的诺如病毒胃肠炎（norovirus gastroenteritis）周度监测数据构建多变量时间序列模型，数据覆盖时段为2011年第27周至2015年第26周。后续时段（2015年第27周至2016年第26周）用于评估预测质量。病例总数的概率性预测可通过蒙特卡洛（Monte Carlo）模拟推导得出，同时也可通过解析方法获取其一阶矩与二阶矩。针对不同复杂程度的各类模型，我们将对比其最终规模预测结果，以及按年龄组、地区、周度划分的病例总数多变量预测结果。借此可更全面地探讨公共卫生监测数据的建模、预测与评估相关议题。