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.

中文翻译：

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使用有效繁殖数实时推断流行病复发的基本限制

我们发现，流行病死灰复燃，定义为传染病的有效繁殖数 ( R ) 从亚临界值上升到超临界值，从根本上难以实时检测到。病原体传播的固有潜伏期，加上在亚临界传播期间更小且本质上更嘈杂的病例发生率，意味着即使病例报告是完美的，如果没有疾病发生时间的显着延迟，也无法可靠地检测到复发。相比之下，流行病抑制（其中R从超临界值下降到亚临界值）可以更快地确定 5-10 倍，因为通常应用控制动作的发生率自然较大。我们证明，当结合空间或人口异质性时，检测复苏的这些先天限制只会恶化。因此，我们认为积极主动地处理复苏会更有效，可能会以误报为代价。与进一步优化用于处理常规暴发数据的统计模型相比，如果监测数据的质量和多样性更高，更有可能及时应对复发性感染或新出现的关注变体。