在受控大流行中的干预规划：以COVID-19为例
Planning interventions in a controlled pandemic: the COVID-19 case
AI总结：
本研究针对新发大流行，提出两种控制策略（控制感染率与控制住院/ICU占用），通过一阶分析揭示各自稳定性问题，并构建考虑人口异质性、闭环控制和逐步接种的框架以评估其经济与社会成本。
摘要：
限制社会和经济活动以及疫苗接种一直是遏制COVID-19疫情的关键干预措施。我们的工作侧重于更好地理解在新疫情爆发所创造的条件和不确定性下，政策制定者可用的选项。更具体地说，我们关注两种控制策略。第一种旨在控制新感染率，以防止医疗系统拥堵。第二种直接控制住院和重症监护病房（ICU）占用。通过一阶分析，我们表明，一方面，由于接触追踪的困难以及准确信息的缺乏，控制传播率可能很困难，导致不稳定性。另一方面，尽管住院和ICU数据易于获取且比新感染率噪声更小，但在控制回路中引入了延迟，这可能危及系统稳定性。我们的框架允许在以下场景中评估上述策略对经济和社会成本的影响：i）人口在死亡率和风险暴露方面的异质性，ii）流行病学曲线的闭环控制，以及iii）个体逐步接种疫苗。
英文摘要：
Restrictions on social and economic activities, as well as vaccinations, have been a key intervention in containing the COVID-19 epidemic. Our work focuses on better understanding the options available to policymakers under the conditions and uncertainties created by the onset of a new pandemic. More precisely, we focus on two control strategies. The first aims to control the rate of new infections to prevent congestion of the health care system. The latter directly controls hospitalizations and intensive care units (ICUs) occupation. By a first-order analysis, we show that, on the one hand, due to the difficulty in contact tracing and the lack of accurate information, controlling the transmission rate may be difficult, leading to instability. On the other hand, although hospitalizations and ICUs are easily accessible and less noisy than the rate of new infections, a delay is introduced in the control loop, which may endanger system stability. Our framework allows assessing the impact on economic and social costs of the above strategies in a scenario enriched by: i) population heterogeneity in terms of mortality rate and risk exposure, ii) closed-loop control of the epidemiological curve, and iii) progressive vaccination of individuals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
