论文标题
网络流行的推理，预测和控制网络上的闭环框架
A Closed-Loop Framework for Inference, Prediction and Control of SIR Epidemics on Networks
论文作者
论文摘要
由持续的大流行Covid-19，我们提出了一个闭环框架，该框架结合了测试数据，学习动力学的参数和最佳资源分配的参数，以控制网络上易感性感染的恢复反射（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，例如高风险个人更有可能接受测试。然后，我们提出了两个可进行的优化问题，以评估控制流行病的生长率和非药物干预措施（NPI）之间的权衡。我们通过广泛的模拟和分析Covid-19的真实，公共可用的测试数据来说明拟议的闭环框架的重要性。我们的结果说明了如果NPI过早地撤回，早期测试的重要性以及第二波感染的出现。
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illustrate the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.
