Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK.
BMC Public Health. 2022 Apr 11;22(1):716. doi: 10.1186/s12889-022-13069-0.
The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths.
We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.
A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%.
Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.
COVID-19 疫情在英格兰各地对社区造成了不同的影响，确诊病例、住院和死亡的地区差异明显。随着疫情的不断升级，监测范围不断扩大，因此这种负担的衡量标准在最初的几个月发生了重大变化。实验室确诊最初仅限于临床需要（“支柱 1”），然后扩大到社区范围内的症状学（“支柱 2”）。本研究旨在确定由于检测覆盖范围的不同而导致病例数据的不一致测量是否可以通过从与 COVID-19 相关的死亡中推断出来得到协调。
我们对整个第一波（2020 年 1 月 1 日至 6 月 30 日）期间按地方当局（LTLA）每周 COVID-19 相关死亡人数拟合贝叶斯时空模型，同时调整了当地疫情时间以及其人口的年龄、贫困程度和种族构成。我们将该模型的预测结果与社区范围内的症状性检测下的病例数据以及 ONS 感染调查中的感染流行率估计值结合起来，以推断出每个 LTLA 死亡病例所隐含的感染可能轨迹。
在考虑到当地人口特征后，发现包括时间和空间相关随机效应的模型最适合容纳 COVID-19 相关死亡的观察到的变化。在社区范围内的症状性检测下，预测病例数表明，在第一波期间总共会有 275,000-420,000 例病例——中位数比实际检测覆盖范围下确诊的病例数多出 100,000 多例。这相当于英格兰每周约有 200,000 例总感染的峰值发病率。我们发现，在不同的 LTLA 中，估计的总感染病例数在确诊病例数中的反映程度差异很大，从莱斯特的 7%到格洛斯特的 96%，中位数为 23%。
检测能力的限制导致了第一波期间 COVID-19 感染的观察轨迹出现偏差。通过基于 COVID-19 相关死亡率和更高覆盖率的检测来推断后期的情况，我们可以更明确地探索这种偏差的程度。有证据表明，全国范围内初始增长和高峰期感染的严重程度存在严重的代表性不足，而不同地区的贡献程度不同。
