Abstract
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.
Highlights
Epidemiological models play a vital role in understanding the spread and severity of a pandemic of infectious diseases [1]
Model once again to produce a projection of simulation for a period of 20 subsequent days from the end date of calibration period in order to evaluate the predictive capability of SIR model while using the parameters that were estimated from Harmony Search (HS) algorithms
SIR model simulated cumulative infectious cases using xoptimized, the accuracy of estimation is evaluated by computing the Root Mean Squared Error (RMSE) between CT
Summary
Epidemiological models play a vital role in understanding the spread and severity of a pandemic (or epidemic) of infectious diseases [1]. During an outbreak of an infectious disease, it is crucial to simulate the potential outbreak growth for planning the outbreak control measures in order to provide useful insights into measurable outcome of existing interventions, predictions of subsequent growth, risk estimations, and guiding alternative interventions [2,3,4]. Epidemiological constraints, such as delays in symptom appearance (due to incubation period) and positive test confirmation (due to limited testing and detection resources), may limit the real-time use of epidemiological models [5,6].
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