A nonlinear model predictive control model aimed at the epidemic spread with quarantine strategy

Area of Science:

• Epidemiology
• Mathematical Biology
• Control Theory

Background:

• Mathematical modeling is crucial for controlling epidemic disease spread.
• Efficiently controlling epidemics with partial, real-time data presents a significant challenge.
• Timely decision-making requires accurate prediction and control models.

Purpose of the Study:

• To develop a novel framework for epidemic control using limited real-time data.
• To create a nonlinear model predictive control (NMPC) strategy for managing infectious diseases.
• To minimize infectious cases and control costs within resource constraints.

Main Methods:

• Developed a nonlinear model predictive control (NMPC) framework.
• Utilized a discrete-time susceptible-infected-removed (SIR) dynamics model.
• Incorporated a state observer for enhanced real-time data utilization and accuracy.
• Addressed treatment bed capacity and other relevant constraints.

Main Results:

• The NMPC model effectively minimizes total infectious cases and associated costs.
• The integrated state observer improves the accuracy of real-time state estimation.
• The closed-loop control algorithm allows for timely updates based on current statistical data.
• Theoretical results on the state observer were presented and validated.

Conclusions:

• The proposed NMPC framework offers a promising approach for timely epidemic control.
• The integration of a state observer enhances the practical application of predictive control in public health.
• This method provides an effective strategy for resource allocation during disease outbreaks.