Parameter Estimation of Compartmental Epidemiological Model Using Harmony Search Algorithm and Its Variants

Epidemiological models play a vital role in understanding the spread and severity of a pandemic of infectious disease, such as the COVID-19 global pandemic. The mathematical modeling of infectious diseases in the form of compartmental models are often employed in studying the probable outbreak growth. Such models heavily rely on a good estimation of the epidemiological parameters for simulating the outbreak trajectory. In this paper, the parameter estimation is formulated as an optimization problem and a metaheuristic algorithm is applied, namely Harmony Search (HS), in order to obtain the optimized epidemiological parameters. The application of HS in epidemiological modeling is demonstrated by implementing ten variants of HS algorithm on five COVID-19 data sets that were calibrated with the prototypical Susceptible-Infectious-Removed (SIR) compartmental model. Computational experiments indicated the ability of HS to be successfully applied to epidemiological modeling and as an efficacious estimator for the model parameters. In essence, HS is proposed as a potential alternative estimation tool for parameters of interest in compartmental epidemiological models.

Automated summary

Epidemiological models are crucial in understanding the spread and severity of infectious disease pandemics like COVID-19. Mathematical modeling of infectious diseases, typically in compartmental form, heavily relies on accurate estimation of epidemiological parameters. This study formulates parameter estimation as an optimization problem and applies the Harmony Search algorithm to obtain optimized parameters, showing it as a potential alternative tool for parameter estimation in compartmental epidemiological models.