Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health Policy

Background:

• Drastic containment measures for SARS-CoV-2 caused severe economic crises.
• Periodic relaxation of measures is a strategy governments use to mitigate economic impact.
• Assessing the risk of disease resurgence during openings is challenging due to data uncertainty.

Purpose of the Study:

• To develop a model for assessing the impact of selective containment measure relaxation.
• To evaluate strategies for balancing epidemic control and economic activity.
• To analyze the effects of different social activity-based controls on disease spread.

Main Methods:

• Utilized a SEIRD compartmental model with age structure and stochastic inputs.
• Incorporated containment measure effects via an optimal control problem linked to social activities (home, work, school).
• Employed a short-time horizon approximation to derive feedback control models for uncertain data.

Main Results:

• Developed models with multiple feedback controls based on social activities.
• Analyzed the impact of selective relaxation strategies on epidemic spread.
• Presented simulation results for Germany, France, Italy, Spain, UK, and US during the first wave.

Conclusions:

• A careful, progressive relaxation of containment measures can help keep epidemics under control.
• This strategy allows for the restart of various productive economic activities.
• Modeling social contact patterns is crucial for effective public health interventions.