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Pandemic economics: optimal dynamic confinement under uncertainty and learning
1Toulouse School of Economics, University of Toulouse-Capitole, Toulouse, France.
The Geneva Risk and Insurance Review
|August 25, 2020
Summary
Uncertainty in the reproduction number, a key factor in COVID-19 transmission, impacts lockdown policies. Introducing this uncertainty suggests a reduced initial confinement rate for optimal disease control.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health Policy
Background:
- COVID-19 models often assume a certain reproduction number, influencing lockdown decisions.
- Exiting lockdowns globally occurred without precise knowledge of post-confinement transmission rates.
Purpose of the Study:
- To investigate the impact of uncertainty and learning on optimal dynamic lockdown strategies.
- To analyze suppression strategies within SIR models approximating exponential infection decay.
Main Methods:
- Utilized integrated models of COVID-19 dynamics.
- Assumed SIR dynamics approximated by exponential infection decay for analysis.
- Explored optimal dynamic lockdown policies under uncertainty.
Main Results:
- In scenarios without uncertainty, optimal policy is constant lockdown until virus suppression.
- Introducing uncertainty regarding the reproduction number after deconfining reduces the optimal initial lockdown rate.
Conclusions:
- Uncertainty in the reproduction number is a critical factor in optimizing COVID-19 lockdown policies.
- Dynamic lockdown strategies should account for uncertainty to improve effectiveness and reduce societal burden.
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