MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom.
Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
PLoS Comput Biol. 2023 Aug 28;19(8):e1011439. doi: 10.1371/journal.pcbi.1011439. eCollection 2023 Aug.
The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved.
时变繁殖数（Rt）是衡量传染病传播能力的重要指标，直接为政策决策和控制措施的优化提供依据。EpiEstim 是一种广泛使用的开源软件工具，它使用病例发病率和序列间隔（SI，病例症状出现和其感染者之间的时间间隔）实时估计 Rt。发病率和 SI 分布必须在相同的时间分辨率下提供，这可能会限制 EpiEstim 和其他类似方法的适用性，例如在发病率报告的时间窗口长于平均 SI 的情况下。在 EpiEstim R 包中，我们实现了一种期望最大化算法，从时间聚合数据中重建每日发病率，然后可以从该数据中估计 Rt。我们使用广泛的模拟研究评估了我们方法的有效性，并将其应用于 COVID-19 和流感数据。对于所有数据集，通过使用聚合的每周数据来减轻每周报告数据中的内在变异性的影响。使用从每周数据中重建的发病率估算的每周滑动窗口上的 Rt 与原始每日数据的估算值密切相关。模拟研究表明，Rt 在所有情况下都得到了很好的估计，而与数据的时间聚合无关。在周末效应存在的情况下，从重建数据中获得的 Rt 估计值比从报告的每日数据中获得的 Rt 估计值更成功地恢复了 Rt 的真实值。这些结果表明，这种新方法允许使用简单的方法和很少的数据要求，从聚合数据中成功恢复 Rt。此外，通过在重建每日发病率数据时消除行政噪声，可以提高 Rt 估计值的准确性。
