South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa.
PLoS One. 2023 Sep 22;18(9):e0287026. doi: 10.1371/journal.pone.0287026. eCollection 2023.
The aim of this study was to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources, and using data from public and private sector service providers.
R was estimated from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalisations, and hospital-associated deaths, using a method that models daily incidence as a weighted sum of past incidence, as implemented in the R package EpiEstim. R was also estimated separately using public and private sector data.
Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves, respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves, but higher during the fourth wave for case-based estimates. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave.
Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. The high R estimates for Omicron relative to earlier waves are interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.
本研究旨在通过估计时变繁殖数（R）来量化南非在 COVID-19 大流行的前四波中的传播趋势，并比较基于三种不同数据源的 R 估计的稳健性，同时使用公共和私营部门服务提供商的数据。
根据实时聚合酶链反应（rt-PCR）确诊病例、住院和医院相关死亡的时间序列，从 2020 年 3 月到 2022 年 4 月，在全国和各省估算 R。使用模型化每日发病率为过去发病率加权和的方法，该方法在 R 包 EpiEstim 中实现。还分别使用公共和私营部门的数据估算 R。
在全国范围内，封锁措施实施后，基于病例的最大 R 分别在第一波（武汉-武汉）、第二波（Beta）、第三波（Delta）和第四波（Omicron）中达到 1.55（CI：1.43-1.66）、1.56（CI：1.47-1.64）、1.46（CI：1.38-1.53）和 3.33（CI：2.84-3.97）。在前三波中，基于三种数据源（病例、住院、死亡）的估计值通常相似，但在第四波中基于病例的估计值更高。公共和私营部门的 R 估计值通常相似，除了在最初的封锁期间和第四波中的基于病例的估计值。
在前三波中使用不同数据源的 R 估计值之间的一致性表明，在未来大流行的早期阶段，可以使用这些来源中的任何一个。与之前的波相比，Omicron 的高 R 估计值很有趣，因为在 Omicron 之前已经有了很高的暴露水平。公共和私营部门 R 估计值之间的一致性突出表明，公共和私营部门的客户没有经历两次单独的疫情，除了在第一波最严格的封锁期间可能受到了一定程度的影响。
