Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Lancet Microbe. 2022 Oct;3(10):e753-e761. doi: 10.1016/S2666-5247(22)00182-3. Epub 2022 Aug 31.
Assessment of disease severity associated with a novel pathogen or variant provides crucial information needed by public health agencies and governments to develop appropriate responses. The SARS-CoV-2 omicron variant of concern (VOC) spread rapidly through populations worldwide before robust epidemiological and laboratory data were available to investigate its relative severity. Here we develop a set of methods that make use of non-linked, aggregate data to promptly estimate the severity of a novel variant, compare its characteristics with those of previous VOCs, and inform data-driven public health responses.
Using daily population-level surveillance data from the National Institute for Communicable Diseases in South Africa (March 2, 2020, to Jan 28, 2022), we determined lag intervals most consistent with time from case ascertainment to hospital admission and within-hospital death through optimisation of the distance correlation coefficient in a time series analysis. We then used these intervals to estimate and compare age-stratified case-hospitalisation and case-fatality ratios across the four epidemic waves that South Africa has faced, each dominated by a different variant.
A total of 3 569 621 cases, 494 186 hospitalisations, and 99 954 deaths attributable to COVID-19 were included in the analyses. We found that lag intervals and disease severity were dependent on age and variant. At an aggregate level, fluctuations in cases were generally followed by a similar trend in hospitalisations within 7 days and deaths within 15 days. We noted a marked reduction in disease severity throughout the omicron period relative to previous waves (age-standardised case-fatality ratios were consistently reduced by >50%), most substantial for age strata with individuals 50 years or older.
This population-level time series analysis method, which calculates an optimal lag interval that is then used to inform the numerator of severity metrics including the case-hospitalisation and case-fatality ratio, provides useful and timely estimates of the relative effects of novel SARS-CoV-2 VOCs, especially for application in settings where resources are limited.
National Institute for Communicable Diseases of South Africa, South African National Government.
评估与新型病原体或变体相关的疾病严重程度可为公共卫生机构和政府提供所需的关键信息，以制定相应的应对措施。在有足够的流行病学和实验室数据来研究其相对严重程度之前，新型关注的 SARS-CoV-2 奥密克戎变体（VOC）在全球人群中迅速传播。在这里，我们开发了一组方法，利用非链接的综合数据来快速估计新型变体的严重程度，将其特征与之前的 VOC 进行比较，并为数据驱动的公共卫生应对措施提供信息。
利用南非国家传染病研究所（2020 年 3 月 2 日至 2022 年 1 月 28 日）的每日人群水平监测数据，我们通过时间序列分析中的距离相关系数优化，确定与从病例确定到住院和院内死亡的时间最一致的滞后间隔。然后，我们使用这些间隔来估计和比较南非面临的四个流行波次中年龄分层的病例住院和病死率，每个波次都由不同的变体主导。
共有 3569621 例、494186 例住院和 99954 例与 COVID-19 相关的死亡病例被纳入分析。我们发现，滞后间隔和疾病严重程度取决于年龄和变体。在综合水平上，病例的波动通常会在 7 天内出现类似的住院趋势，在 15 天内出现类似的死亡趋势。我们注意到，在奥密克戎期间，疾病严重程度显著降低，与之前的波次相比（年龄标准化病死率持续降低超过 50%），50 岁及以上年龄段的个体降幅最大。
这种人群水平的时间序列分析方法计算出一个最佳的滞后间隔，然后用于通知严重程度指标的分子，包括病例住院和病死率，为新型 SARS-CoV-2 VOC 的相对影响提供了有用且及时的估计，特别是在资源有限的情况下。
南非国家传染病研究所，南非国家政府。
