Christopher Jewell's Discussion Contribution to Papers in Session 2 of The Royal Statistical Society's Special Topic Meeting on COVID-19 Transmission: 11 June 2021
Christopher Jewell's Discussion Contribution to Papers in Session 2 of The Royal Statistical Society's Special Topic Meeting on COVID-19 Transmission: 11 June 2021
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Christopher Jewell 在英国皇家统计学会关于 COVID-19 传播的专题会议第 2 场中对论文的讨论贡献：2021 年 6 月 11 日
DOI：
10.1111/rssa.12975
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Jewell C
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文献类型：
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作者：
Jewell C
祝贺为本次专题会议做出贡献的作者。在英国，SARS-CoV-2流行的特点是高度的空间和时间异质性：与其他地区相比，一些地区（特别是西北部）的发病率一直很高，社会混合的大幅波动部分是由强制性社交距离措施造成的。这种异质性需要先进的流行病学情报，以便及早发现时空波动，从而提供宝贵的证据，为当地疾病控制措施的有效决策提供信息。这两篇论文提出了估计当地有效繁殖数的方法，Rit，是基于更新方程法应用于英国地方当局区（LAD; Teh等人和Mishra等人）。这些方法的随机过程的性质是需要在这个级别上，连续状态空间假设的常微分方程（ODE）近似失败和离散空间模型成为唯一的选择。Teh等人在考虑LAD之间的空间关系、利用LAD-LAD人类移动性数据和Rit的空间相关性方面具有明显的优势。由于Rit是在时间t时由LAD i中的受感染个体“创建”的感染的预期数量，因此它是个体对其余群体而不仅仅是LAD内的群体构成的风险的量度。因此，他们避免低估Rit（提高他们的流动性数据的准确性），因为他们没有忽略感染个体在他们自己的LAD之外的影响。Rit的空间相关性具有汇集来自相邻LAD的信息的进一步优势，允许通过空间接近度来通知低发病率LAD的估计。在低发病率SARS-CoV-2的最后阶段，病例的空间建模将成为异质监测环境中流行率估计的核心。Parag等人强调的上述方法的主要局限性，是生成间隔的先验估计，其本身可以在时空上变化。解决办法在于直接
Congratulations to the authors of the contributions to this Special Topic Meeting. In the United Kingdom, the SARS-CoV-2 epidemic has been characterised by a high degree of spatial and temporal heterogeneity: some regions (notably the North-West) have experienced persistently high incidence compared to others, and large fluctuations in social mixing have been caused partly by mandatory social distancing measures. Such heterogeneity requires sophisticated epidemiological intelligence to detect spatio-temporal fluctuations early, thus providing valuable evidence to inform effective decisions on local disease control measures. The two papers presenting methods for estimating local effective reproduction numbers, Rit, are based on the renewal equation method applied to UK Local Authority Districts (LADs; Teh et al. and Mishra et al.). The stochastic process nature of these approaches is required at this level, where the continuous-state-space assumption of Ordinary Differential Equation (ODE) approximations fail and discrete-space models become the only option. Teh et al. have the distinct advantage of accounting for the spatial relation between LADs, making use of LAD–LAD human mobility data, and spatial correlation of Rit. Since Rit is the expected number of infections ‘created’by an infected individual in LAD i at time t, it is a measure of risk posed by an individual to the rest of the population, and not just the population within the LAD. Thus they avoid underestimating Rit (up the accuracy of their mobility data) by not ignoring the effect of an infected individual outside their own LAD. Spatial correlation of Rit has the further advantage of pooling information from neighbouring LADs, allowing estimates for low-incidence LADs to be informed via spatial proximity. In the low-incidence SARS-CoV-2 endgame, spatial modelling of cases will become central to prevalence estimation in a heterogeneous surveillance landscape. The main limitation of the above approaches, highlighted by Parag et al., is a priori estimation of the generation interval, which itself may vary spatiotemporally. The solution lies in directly