MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom.
Department of Statistics, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol. 2022 Apr 11;18(4):e1010004. doi: 10.1371/journal.pcbi.1010004. eCollection 2022 Apr.
We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5-10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.
我们发现，疫情反弹（即传染病有效繁殖数（R）从亚临界值上升到超临界值）在实时检测方面非常困难。病原体传播固有的滞后性，再加上亚临界传播阶段病例发生率较小且本质上更嘈杂，意味着如果没有疾病的代际时间级别的显著延迟，就无法可靠地检测到疫情反弹，即使病例报告是完美的。相比之下，由于控制措施通常应用于发病率自然更高的情况，疫情抑制（即 R 从超临界值下降到亚临界值）可能会快 5-10 倍被确定。我们证明，当纳入空间或人口统计学异质性时，检测疫情反弹的这些固有限制只会恶化。因此，我们认为，通过更优质和多样化的监测数据来制定政策，而不是通过进一步优化用于处理常规疫情数据的统计模型，更有效地主动应对疫情反弹，可能会导致误报。当政策基于比常规爆发数据处理中使用的统计模型进一步优化更优质和多样化的监测数据时，及时应对再次出现的感染或新出现的令人担忧的变异，就更有可能实现。
