[论文解读] COVID-19 Plateau: A Phenomenon of Epidemic Development under Adaptive Prevention Strategies.

Ziqiang Wu, Hao Liao+3 more|arXiv (Cornell University)|Nov 6, 2020

COVID-19 epidemiological studies参考文献 22被引用 5

一句话总结

本文提出了一种自适应的多城市流行病模型，整合了随时间变化的防控策略和人口流动，展示了这些因素如何导致感染人数出现长期平台期。该模型揭示，平台期源于相互关联的区域动态，并通过全球多个地区的现实数据得到验证，显示出平台现象的存在。

ABSTRACT

Since the beginning of the COVID-19 spreading, the number of studies on the epidemic models increased dramatically. It is important for policy makers to know how the disease will spread, and what are the effects of the policies and environment on the spreading. In this paper, we propose two extensions to the standard infectious disease models: (a) We consider the prevention measures adopted based on the current severity of the infection, those measures are adaptive and change over time. (b) Multiple cities and regions are considered, with population movements between those cities/regions, while taking into account that each region may have different prevention measures. While the adaptive measures and mobility of the population were often observed during the pandemic, these effects are rarely explicitly modeled and studied in the classical epidemic models. The model we propose gives rise to a plateau phenomenon: the number of people infected by the disease stay at the same level during an extended period of time. We show what are conditions needs to be met in order for the spreading to exhibit a plateau period, and we show that this phenomenon is interdependent: when considering multiples cities, the conditions are different from a single city. We verify from the real-world data that plateau phenomenon does exists in many regions of the world in the current COVID-19 development. Finally, we provide theoretical analysis on the plateau phenomenon for the single-city model, and derive a series of results on the emergence and ending of the plateau, and on the height and length of the plateau. Our theoretical results match well with our empirical findings.

研究动机与目标

• 解决经典流行病模型中缺乏对自适应、随时间变化的防控措施的建模问题。
• 研究在区域特定干预措施下，多个城市间的人口流动如何影响流行病动力学。
• 解释在现实世界新冠数据中观察到的长期感染平台期的出现与持续原因。
• 推导单城市与多城市情境下平台期形成与结束的理论条件。
• 将模型预测与全球多个地区的实证数据进行对比验证。

提出的方法

• 通过引入基于各区域实时感染严重程度自适应调整的防控措施，扩展了标准流行病模型。
• 采用基于网络的框架，整合城市间的人口流动，并结合区域特定的控制政策。
• 建立动态反馈回路：感染人数上升触发更严格的措施，进而减少传播并稳定病例数。
• 利用理论稳定性与分岔分析研究单城市情形，推导平台期形成与持续的条件。
• 将理论预测与现实世界感染数据进行比较，验证平台期的存在性与特征。
• 基于传播率与自适应响应阈值，推导平台期高度、长度与终止的数学条件。

实验结果

研究问题

• RQ1 在应用自适应防控策略时，何种条件下流行病会出现长期的感染平台期？
• RQ2 多个城市间的人口流动如何影响感染平台期的出现与稳定性？
• RQ3 单城市模型与多城市互联系统在平台期动力学上存在哪些差异？
• RQ4 来自全球多个地区的现实数据在多大程度上表现出模型预测的平台现象？
• RQ5 平台期的高度与持续时间在多大程度上取决于自适应干预的时机与强度？

主要发现

• 该模型成功复现了在全球多个地区现实新冠数据中观察到的平台期现象。
• 当自适应防控措施因感染人数上升而被触发时，平台期出现，并使传播稳定在持续水平。
• 在多城市系统中，平台期通过区域间反馈得以维持：一个区域的下降导致放松措施，而另一区域的上升则维持整体稳定。
• 平台期的高度与持续时间在很大程度上取决于自适应措施的阈值与响应速度，以及区域间的人口流动模式。
• 理论分析证实，只有当传播被抑制到新感染与控制措施导致的减少量达到平衡时，平台期才能持续。
• 该模型对平台期特征的预测与现实世界数据的实证观察高度一致。