Department of Disease Control, Faculty of Tropical and Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, Bloomsbury, London, WC1E 7HT, UK.
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Sci Rep. 2021 Mar 8;11(1):5378. doi: 10.1038/s41598-021-83780-2.
COVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths at small-area resolution, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at high spatial resolution in coming weeks. We applied a Bayesian hierarchical space-time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England [Middle Layer Super Output Area (MSOA), 6791 units] and by week (using observed data from week 5 to 34 of 2020), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA. Reductions in population mobility during the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates. While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have significantly contributed to the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.
英格兰的 COVID-19 病例数已过第一个高峰,目前似乎在逐渐增加,这可能表明第二波疫情正在出现。为了确保对疫情的持续应对最为有效,必须更好地从回顾性和前瞻性两个方面了解 COVID-19 病例数和死亡人数在小区域尺度上的地理演变,确定时空上风险显著更高的局部地区,量化局部人口流动(或迁移)变化对病例数的影响,确定死亡率升高的局部风险因素,并以高空间分辨率预测未来几周的疫情发展。我们应用贝叶斯分层时空 SEIR 模型来评估英格兰 COVID-19 病例数(传播)和死亡人数的时空变化,在小区域尺度上(中层超级输出区 (MSOA),6791 个单位),按周(使用 2020 年第 5 周到第 34 周的观察数据),包括关键决定因素、模型传播动态和时空随机效应。我们还根据 MSOA 预测病例数和死亡数的不确定性(截至 2020 年第 51 周),量化移动性减少(随后放宽)对 COVID-19 病例数的影响,并量化关键社会人口风险因素对 MSOA 中 COVID-19 相关死亡率风险的影响。在第一次封锁期间,人口流动的减少对英格兰 COVID-19 病例数的减少产生了重大影响,但各地方当局的人口流动减少速度各不相同,我们的模型表明,这对小区域尺度上病例数的地理异质性产生了实质性影响。从 4 月底开始,人口流动的稳定增长似乎导致 6 月底病例数减少放缓,随后出现第二波疫情。MSOA 中 70 岁以上老年人和生活贫困的老年人比例较高(70 岁以上),两者的地理分布都非常明显,COVID-19 死亡率明显升高。虽然非药物干预措施(即减少人口流动和社交距离)对英格兰 COVID-19 疫情第一波的发展轨迹产生了深远影响,但人口流动的增加似乎显著促成了第二波疫情。一些连续的小区域似乎处于 COVID-19 高传播的显著高风险中,其中许多区域的死亡率也较高。建议在地理上错开重新引入强化社会距离措施,如果要减少第二波疫情的规模和地理范围,则应限制 MSOA 之间的交叉流动。