Application of Optimal Control of Infectious Diseases in a Model-Free Scenario
Erivelton G Nepomuceno1, Márcia L C Peixoto2, Márcio J Lacerda1
1Control and Modelling Group (GCOM), Department of Electrical Engineering, Federal University of São João del-Rei, São João del-Rei, Brazil.
SN Computer Science
|August 16, 2021
Summary
This study introduces an optimal control law to minimize infections and vaccination rates using the SIR model. The new strategy reduces infected individuals by 50% compared to constant vaccination, offering insights for model-free epidemic control.
Area of Science:
- Epidemiology
- Mathematical Biology
- Control Theory
Background:
- Optimal control is crucial for managing infectious diseases.
- Interpreting optimal control laws in epidemic systems requires further investigation.
- Compartmental models like SIR are widely used for disease dynamics.
Purpose of the Study:
- To develop an optimal control law for infectious disease management.
- To minimize the number of infected individuals and the vaccination rate.
- To provide insights for model-free epidemic control strategies.
Main Methods:
- Application of Pontryagin's maximum principle.
- Utilizing the SIR compartmental model for analysis.
- Developing a model-free strategy using analytic functions.
Main Results:
- The proposed optimal control law reduces infected individuals by 50% compared to constant vaccination.
- The method offers insights for model-free scenarios, not requiring prior population data.
- The approach demonstrates faster disease eradication potential.
Conclusions:
- Optimal control provides an effective strategy for infectious disease mitigation.
- Model-free approaches can be developed for epidemic control.
- Rapid and widespread vaccination is key to minimizing infections and deaths, as seen with COVID-19.
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