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Data-driven optimized control of the COVID-19 epidemics.

Afroza Shirin1,2, Yen Ting Lin3, Francesco Sorrentino4

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Optimizing COVID-19 containment strategies requires balancing economic impact and epidemic suppression. Optimal control suggests consistent social distancing, varying by region, rather than phased reopening, and can be adjusted with vaccination availability.

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Area of Science:

  • Epidemiology
  • Public Health
  • Mathematical Modeling

Background:

  • Containing COVID-19 spread while minimizing economic disruption presents a significant challenge.
  • Daily new case data from US Metropolitan Statistical Areas (MSAs) are crucial for understanding disease propagation.

Purpose of the Study:

  • To optimize control strategies for COVID-19 containment, balancing economic impact with epidemic suppression.
  • To determine optimal social distancing levels and durations for different MSAs.

Main Methods:

  • Parametrization of a disease propagation model using daily COVID-19 case counts.
  • Introduction of a time-varying control input representing social distancing levels.
  • Solving an optimal control problem to minimize economic impact under epidemic suppression constraints.

Main Results:

  • Optimal social distancing is often a constant, area-specific level, differing from phased reopening strategies.
  • Optimal distancing levels are generally stricter than those estimated from current data.
  • The optimal duration for control measures varies significantly across MSAs.
  • Increasing transmissibility necessitates stricter social distancing.
  • Vaccinations may allow for reduced social distancing during administration periods.

Conclusions:

  • Constant, area-specific social distancing levels may be more effective than phased approaches for COVID-19 control.
  • Optimal control strategies must consider varying transmissibility and the impact of vaccinations.
  • Tailored control strategies are essential for balancing public health and economic considerations across different regions.