Congdon Peter
School of Geography, Queen Mary University of London, Mile End Rd, London, E1 4NS UK.
J Geogr Syst. 2022;24(4):583-610. doi: 10.1007/s10109-021-00366-2. Epub 2022 Apr 26.
The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases-linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity.
新冠疫情引发了有关病例、死亡和住院等结果的建模与预测的重大问题。特别是，当传染病的地区特定计数变化迅速且存在感染热点时，如在疫情形势下，对其进行预测会面临诸多问题。此类预测对于确定干预措施的优先级或为不同地区指定严重程度至关重要。在本文中，我们考虑发病率计数中自回归依赖性的不同设定，因为这些设定可能会对疫情形势下的适应性产生重大影响。特别是，我们引入参数以实现自回归依赖性的时间适应性。一项案例研究考察了2020年末至2021年初英国疫情第二波期间144个英国地方当局的新冠疫情数据，这些数据显示新病例存在地理聚集现象，与当时出现的阿尔法变体有关。该模型考虑了自回归效应在空间和时间上的变化。我们评估了短期预测的敏感性以及对设定（空间自回归与时空自回归、线性与对数线性以及空间衰减形式）的拟合情况，并表明使用包括时间适应性的时空自回归可改善一步预测和样本内预测。
