Controlling epidemic diseases based only on social distancing level: General case.

Area of Science:

• Epidemiology
• Control Theory
• Mathematical Modeling

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, presented a global health crisis with limited initial pharmaceutical interventions.
• Controlling person-to-person viral spread necessitates physical distancing, but precise adjustments are complex, risking economic impact or healthcare system collapse.

Purpose of the Study:

• To propose a robust controller for managing COVID-19 hospitalizations by adjusting social distancing levels.
• To develop a new group-structured mathematical model for describing the COVID-19 outbreak dynamics.

Main Methods:

• Development of a novel control system designed to maintain the number of hospitalized individuals below a predefined threshold.
• Creation of a group-structured compartmental model to simulate COVID-19 transmission and impact.
• Utilizing numerical simulations to evaluate the controller's performance and the model's behavior under various scenarios.

Main Results:

• The proposed controller effectively manages the number of infected individuals by modulating social distancing measures.
• The controller demonstrates robustness against uncertainties in the epidemiological model parameters.
• Simulations confirm the controller's ability to keep hospitalizations within safe limits while minimizing economic disruption.

Conclusions:

• The developed controller and group-structured model offer a viable strategy for managing infectious disease outbreaks like COVID-19.
• Dynamic adjustment of social distancing, guided by a robust controller, is crucial for balancing public health and socioeconomic stability.
• This approach provides a framework for data-driven public health interventions during pandemics.