Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Bern Open Repository...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
https://dx.doi.org/10.48350/18...
Other literature type . 2024
Data sources: Datacite
EconStor
Research . 2023
License: CC BY
Data sources: EconStor
versions View all 3 versions
addClaim

Optimal Epidemic Control

Authors: Gonzalez-Eiras, Martin; Niepelt, Dirk;

Optimal Epidemic Control

Abstract

We develop a flexible single-state model to represent tradeoffs between infections and activity during the early phase of an epidemic. We prove that optimal policy is continuous in the state but discontinuous in the deterministic arrival date of a cure; optimal lockdowns are followed by stimulus policies; and re-infection risk renders laissez faire inefficient even in steady state. Calibrated to the COVID-19 pandemic the model prescribes initial activity reductions of 38 percent. Stimulus policies account for a third of the welfare gains of intervention. Robustness along many dimensions contrasts with sensitivity of the policy prescriptions with respect to the intertemporal elasticity of substitution, activity-infections nexus, and re-infection risk.

Country
Switzerland
Related Organizations
Keywords

lockdown, optimal control, D62, I18, ddc:330, Epidemic, COVID-19, stimulus, 330 Economics, logistic model

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
Related to Research communities