Optimal control of an SIRD model with data-driven parameter estimation

Abstract

In this study, an SIRD model designed for infectious disease control, with a specific focus on SARS-CoV-2, is introduced. The model aims to optimize key control variables to reduce deaths, providing a practical framework for strategically mitigating disease spread and saving lives. Unique to our work is the application of advanced tools such as optimization algorithms, regression coefficients, and sensitivity analysis to accurately determine parameter values. The main focus is on three key control variables: the use of medical masks, vaccination rates, and increasing awareness of testing and treatment measures. The findings clearly reveal the efficacy of these interventions in reducing mortality, providing valuable insights into disease control. In summary, this study highlights the importance of data-driven approaches in infectious disease control, providing a unique perspective on optimizing control variables to safeguard life. The results reveal the potential impact of targeted interventions, providing actionable guidance for policymakers and healthcare professionals in the fight against infectious diseases.

Keywords

• SIRD
• Death
• SARS-CoV-2
• Optimal control
• Pontryagin’s maximal principle