A Closed-Loop Framework for Inference, Prediction, and Control of SIR Epidemics on Networks
A Closed-Loop Framework for Inference, Prediction, and Control of SIR Epidemics on Networks
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DOI：
10.1109/tnse.2021.3085866
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发表时间：
2021-07-01
影响因子：
6.6
通讯作者：
Pare, Philip E.
中科院分区：
文献类型：
--
作者：
Hota, Ashish R.;Godbole, Jaydeep;Pare, Philip E.
受持续的COVID-19大流行的启发，我们提出了一个闭环框架，该框架结合了测试数据的推理，学习动态参数和最优资源分配，以控制网络上的易感染-恢复（SIR）流行病的传播。我们的框架结合了测试数据中存在的几个关键因素，例如高风险个体更有可能接受测试。然后，我们提出了两个易于处理的优化问题，以评估控制流行病的增长率和非药物干预（NPIs）的成本之间的权衡。我们通过对真实的、公开的COVID-19测试数据进行广泛的模拟和分析，说明了所提出的闭环框架的重要性。我们的研究结果说明了早期检测的重要性，以及如果NPI过早退出，第二波感染的出现。
Motivated by the ongoing pandemic COVID-19, we propose a closed-loop framework that combines inference from testing data, learning the parameters of the dynamics and optimal resource allocation for controlling the spread of the susceptible-infected-recovered (SIR) epidemic on networks. Our framework incorporates several key factors present in testing data, such as the fact that high risk individuals are more likely to undergo testing. We then present two tractable optimization problems to evaluate the trade-off between controlling the growth-rate of the epidemic and the cost of non-pharmaceutical interventions (NPIs). We illustrate the significance of the proposed closed-loop framework via extensive simulations and analysis of real, publicly-available testing data for COVID-19. Our results illustrate the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.