Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers.
Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers.
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DOI：
10.1371/journal.pcbi.1010004
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发表时间：
2022-04
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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我们发现，流行病的复苏，定义为一个上升的有效再现数（R）的传染病从亚临界到超临界值，是从根本上很难发现在真实的时间。病原体传播中固有的潜伏期，加上亚临界传播期内较小和固有的噪音病例发生率，意味着即使病例报告完美，如果不显著延迟疾病的产生时间，也无法可靠地检测到死灰复燃。相比之下，流行病抑制（其中R福尔斯从超临界值下降到亚临界值）可以更快地确定5-10倍，这是由于通常应用控制措施的自然较大的发生率。我们证明，这些天生的限制检测复苏时，空间或人口异质性纳入恶化。因此，我们认为，复苏是更有效地处理积极主动，可能以牺牲假警报。如果监测数据的质量和多样性更高，而不是进一步优化用于处理常规疫情数据的统计模型，就更有可能及时应对复发感染或新出现的令人关切的变异。及时发现流行病死灰复燃（即，监测新出现的感染病例（即将出现的感染病例浪潮）对于为公共卫生政策提供信息、为实施干预措施提供有价值的信号以及查明新出现的致病变异或重要的人口行为变化至关重要。由随时间变化的繁殖数R参数化的流行病传播率的增加通常意味着复苏。虽然许多研究已经改进了从案例数据推断R的计算方法，以增强实时复苏检测，但很少有人研究什么限制（如果有的话）从根本上限制了我们进行这种推断的能力。我们应用最佳贝叶斯检测算法和灵敏度测试，发现复苏（向上）R-变化本质上是更难以检测到的，比同等的向下变化表明控制。这种不对称性源于与复苏相关的通常较低和随机噪声的病例数，并导致疾病产生时间的检测延迟。我们证明，这些延迟只会恶化，如果空间或人口的差异，在传输率建模。由于即使病例数据是完美的，这些基本限制也存在，我们得出结论，设计综合监测系统，融合潜在的更及时的数据源（例如，废水）可能比改进R-估计方法更重要，并推断保守的复苏应对举措可能有好处（受误报成本影响）。
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. The timely detection of epidemic resurgence (i.e., upcoming waves of infected cases) is crucial for informing public health policy, providing valuable signals for implementing interventions and identifying emerging pathogenic variants or important population-level behavioural shifts. Increases in epidemic transmissibility, parametrised by the time-varying reproduction number, R, commonly signify resurgence. While many studies have improved computational methods for inferring R from case data, to enhance real-time resurgence detection, few have examined what limits, if any, fundamentally restrict our ability to perform this inference. We apply optimal Bayesian detection algorithms and sensitivity tests and discover that resurgent (upward) R-changes are intrinsically more difficult to detect than equivalent downward changes indicating control. This asymmetry derives from the often lower and stochastically noisier case numbers that associate with resurgence, and induces detection delays on the order of the disease generation time. We prove these delays only worsen if spatial or demographic differences in transmissibility are modelled. As these fundamental limits exist even if case data are perfect, we conclude that designing integrated surveillance systems that fuse potentially timelier data sources (e.g., wastewater) may be more important than improving R-estimation methodology and deduce that there may be merit (subject to false alarm costs) in conservative resurgence response initiatives.
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影响因子：
3
作者：
Casella, Francesco
通讯作者：
Casella, Francesco
DOI：
10.1126/science.abh0635
发表时间：
2021-07-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Hay JA;Kennedy-Shaffer L;Kanjilal S;Lennon NJ;Gabriel SB;Lipsitch M;Mina MJ
通讯作者：
Mina MJ
影响因子：
4.3
作者：
Parag KV;Vinnicombe G
通讯作者：
Vinnicombe G
通过非药物干预措施，SARS-CoV-2 的序列间隔随着时间的推移而缩短
影响因子：
56.9
作者：
Ali, Sheikh Taslim;Wang, Lin;Cowling, Benjamin J.
通讯作者：
Cowling, Benjamin J.
影响因子：
5
作者：
Cauchemez, Simon;Boelle, Pierre-Yves;Valleron, Alain-Jacques
通讯作者：
Valleron, Alain-Jacques