Pandemic economics: optimal dynamic confinement under uncertainty and learning
Pandemic economics: optimal dynamic confinement under uncertainty and learning
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DOI：
10.1057/s10713-020-00052-1
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发表时间：
2020-08-17
影响因子：
1.5
通讯作者：
Gollier, Christian
中科院分区：
文献类型：
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作者：
Gollier, Christian
大多数Covid大流行的综合模型都是在对政策敏感的繁殖数量是确定的假设下开发的。大多数国家已经做出了退出封锁的决定，但并不知道解禁后将出现的复制数量。在本文中，我探讨了不确定性和学习对最优动态锁定策略的作用。我将分析限制在抑制策略中，其中SIR动态可以用指数感染衰减来近似。在没有不确定性的情况下，最佳的限制政策是实施恒定的封锁速度，直到病毒在人群中被抑制。我表明，引入关于解除禁闭的人的再生产数量的不确定性，会降低最佳禁闭的初始速度。
Most integrated models of the covid pandemic have been developed under the assumption that the policy-sensitive reproduction number is certain. The decision to exit from the lockdown has been made in most countries without knowing the reproduction number that would prevail after the deconfinement. In this paper, I explore the role of uncertainty and learning on the optimal dynamic lockdown policy. I limit the analysis to suppression strategies where the SIR dynamics can be approximated by an exponential infection decay. In the absence of uncertainty, the optimal confinement policy is to impose a constant rate of lockdown until the suppression of the virus in the population. I show that introducing uncertainty about the reproduction number of deconfined people reduces the optimal initial rate of confinement.