Pandemic economics: Optimal dynamic confinement under uncertainty and learning

Christian Gollier ()

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Abstract: 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. 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.

Keywords: Covid; Confinement; Uncertainty; Learning; Pandemic (search for similar items in EconPapers)
Date: 2020-07-03
Note: View the original document on HAL open archive server: https://hal.science/hal-02909470v1
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Citations: View citations in EconPapers (15)

Published in Covid Economics Papers, 2020, 34, pp.1-14

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Related works:
Journal Article: Pandemic economics: optimal dynamic confinement under uncertainty and learning (2020)
Working Paper: Pandemic economics: optimal dynamic confinement under uncertainty and learning (2020)
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