论文标题
在模型不确定性下对流行爵士的最佳缓解
Optimal Mitigation of SIR Epidemics Under Model Uncertainty
论文作者
论文摘要
我们研究模型参数不确定性对最佳缓解流行病的影响的影响。我们使用易感感染的（SIR）流行模型捕获流行病扩散过程，并考虑测试隔离为控制策略。我们使用测试策略去除（分离）一部分受感染人群。我们的目标是将日常感染人群保持在一定水平以下，同时最大程度地减少测试总数。与现有的有关流行病传播中控制策略的作品不同，我们提出了一种测试策略，通过高估了流行病的严重性并研究了模型参数不确定性影响下的系统的可行性。与最佳测试策略相比，我们确定模型参数不确定性下的拟议策略将有效地扁平曲线，但需要更多的测试和更长的时间段。
We study the impact of model parameter uncertainty on optimally mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and consider testing for isolation as the control strategy. We use a testing strategy to remove (isolate) a portion of the infected population. Our goal is to maintain the daily infected population below a certain level, while minimizing the total number of tests. Distinct from existing works on leveraging control strategies in epidemic spreading, we propose a testing strategy by overestimating the seriousness of the epidemic and study the feasibility of the system under the impact of model parameter uncertainty. Compared to the optimal testing strategy, we establish that the proposed strategy under model parameter uncertainty will flatten the curve effectively but require more tests and a longer time period.
