Gollier Christian
Toulouse School of Economics, University of Toulouse-Capitole, Toulouse, France.
Geneva Risk Insur Rev. 2020;45(2):80-93. doi: 10.1057/s10713-020-00052-1. Epub 2020 Aug 17.
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.
大多数新冠疫情的综合模型都是在政策敏感繁殖数确定的假设下开发的。在大多数国家，解除封锁的决定是在不知道解封后实际繁殖数的情况下做出的。在本文中，我探讨了不确定性和学习在最优动态封锁政策中的作用。我将分析限制在抑制策略上，即易感-感染-康复（SIR）动态可以用指数感染衰减近似的情况。在不存在不确定性的情况下，最优的封锁政策是维持恒定的封锁率，直到病毒在人群中被抑制。我表明，引入关于解封人群繁殖数的不确定性会降低最优的初始封锁率。
