Optimal Control Strategies for Coronavirus Outbreak in Nakhon Sawan Province, Thailand, Using Bayesian Parameter Estimation

This research develops a mathematical model of the COVID-19 outbreak using actual infection data from Nakhon Sawan Province. The positivity invariant validates the model, and the next-generation method is used to compute the basic reproduction number ( $$R_{0}$$ ). Parameters are estimated using a Markov Chain Monte Carlo (MCMC) technique within the Bayesian framework. The estimation results indicate the presence of virus-infected data in the environment. Additionally, the model computes the sensitivity indices of infectious individuals, revealing that reducing $$\beta_{2}$$ decreases the number of infectious individuals more effectively than altering parameters $$\Pi$$ and $$\omega$$ . Finally, this research applies the Pontryagin Maximum Principle to the optimal control problem by the control variable $$\mathbb{T}$$ representing preventive measures for COVID-19. The analysis demonstrates that these preventive measures can reduce the outbreak of each population group.