Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England.
Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England.
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DOI：
10.1038/s41598-021-83780-2
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发表时间：
2021-03-08
影响因子：
4.6
通讯作者：
Pullan RL
中科院分区：
文献类型：
--
作者：
Sartorius B;Lawson AB;Pullan RL
英国的新冠肺炎案件数量已经经历了第一个高峰，在进行本分析时，案件数量似乎正在逐渐增加，这可能预示着第二波浪潮的出现。为了确保对疫情的持续应对最有效，必须更好地回顾和前瞻性地了解新冠肺炎病例数量和小区域分辨率死亡人数的地理演变，确定风险显著较高的时空局部地区，量化局部人口流动(或流动)变化对病例工作量的影响，确定导致死亡率上升的局部风险因素，并以高空间分辨率预测未来几周疫情的可能进程。我们应用贝叶斯分层时空SEIR模型来评估英格兰小区域尺度[中层超级输出区，6791个单位]的新冠肺炎病例数和死亡人数的时空变异性(使用2020年第5周至第34周的观测数据)，包括关键决定因素、建模的传播动力学和时空随机效应。我们还估计了MSOA在时间上预测的具有不确定性的小区域解决方案的病例和死亡人数(截至2020年第51周)、流动性减少(以及随后的缓解)对新冠肺炎病例量的影响，并量化了关键社会人口风险因素对MSOA预测的新冠肺炎相关死亡风险的影响。第一次封锁期间人口流动性的减少对整个英格兰新冠肺炎案件量的减少产生了重大影响，然而地方当局人口流动的减少速度各不相同，我们的模型表明，这对小区域规模的案件量的地理异质性产生了重大影响。从4月下旬开始观察到的人口流动的稳步增加，似乎是导致6月下旬和随后的第二波浪潮开始时案件数量减少放缓的原因之一。具有较高比例的老年人(70+ 岁)和生活在贫困中的老年人，两者具有非常不同的地理分布，其新冠肺炎死亡率显著升高。尽管非药物干预(即减少人口流动性和社会距离)对英国新冠肺炎第一波爆发的轨迹产生了深远影响，但人口流动性的增加似乎对第二波疫情起到了重大作用。一些毗邻的小地区似乎面临着高传播新冠肺炎的风险显著增加，其中许多地区也面临更高的死亡率风险。如果要减少第二波的规模和地理范围，建议在地理上交错地重新引入强化的社会疏远措施，并限制跨MSOA的移动。
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.
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影响因子：
13.6
作者：
Levin AT;Hanage WP;Owusu-Boaitey N;Cochran KB;Walsh SP;Meyerowitz-Katz G
通讯作者：
Meyerowitz-Katz G
DOI：
10.1016/j.jmii.2020.04.004
发表时间：
2020-06-01
影响因子：
7.4
作者：
Chintalapudi, Nalini;Battineni, Gopi;Amenta, Francesco
通讯作者：
Amenta, Francesco
影响因子：
1.7
作者：
Lawson, A. B.;Rotejanaprasert, C.;Choi, J.
通讯作者：
Choi, J.
影响因子：
3.4
作者：
Lawson, Andrew B.;Song, Hae-Ryoung
通讯作者：
Song, Hae-Ryoung
影响因子：
56.9
作者：
Chinazzi, Matteo;Davis, Jessica T.;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro