Multiscale heterogeneous optimal lockdown control for COVID-19 using geographic information.

Area of Science:

• Epidemiology and Public Health
• Computational Modeling and Optimization
• Socioeconomic Impact Analysis

Background:

• Pandemics necessitate public health interventions like lockdowns to mitigate mortality.
• Existing lockdown strategies often lack regional specificity and behavioral data integration.
• Optimizing lockdown policies requires balancing disease control with socioeconomic impact.

Purpose of the Study:

• To develop a method for synthesizing heterogeneous lockdown policies in metropolitan statistical areas (MSAs).
• To minimize pandemic-related fatalities while controlling lockdown severity.
• To evaluate the effectiveness of data-driven, region-specific lockdown strategies.

Main Methods:

• Development of a multiscale susceptible, infected, recovered, and deceased (SIRD) model incorporating subregional population behaviors.
• Formulation of lockdown policy synthesis as a nonconvex optimization problem.
• Application of an iterative algorithm using sequential convex programming to solve the optimization problem.

Main Results:

• Empirical application to six major U.S. MSAs demonstrated significant reductions in mortality.
• Heterogeneous lockdown policies reduced deaths by up to 45% over 100 days compared to less heterogeneous baselines.
• The developed policies imposed less severe lockdowns while achieving superior public health outcomes.

Conclusions:

• Data-driven, heterogeneous lockdown policies offer a more effective and less burdensome approach to pandemic management.
• Multiscale modeling and optimization techniques are crucial for developing nuanced public health interventions.
• This approach provides a framework for optimizing non-pharmaceutical interventions in diverse urban environments.