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

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.

A data-driven optimization model to response to COVID-19 pandemic: a case study

“…Other researchers have published similar articles in this regard, such as (Abbasimehr & Paki, 2021 ; Arora et al, 2020 ; Melin et al, 2020 ; Shahid et al, 2020 ). In addition to using ML methods for forecasting the cases of COVID-19, other models, such as SEIR, have been utilized to estimate the epidemiological parameters of COVID-19, and the obtained predicted parameters are utilized to forecast future cases (Godio et al, 2020 ; Gopal et al, 2021 ; Kumar et al, 2021 ; Malik et al, 2021 ; Zhan et al, 2021 ).…”

Tight Fit of the SIR Dynamic Epidemic Model to Daily Cases of COVID-19 Reported During the 2021-2022 Omicron Surge in New York City: A Novel Approach

“…The gradient expressed in (21) differs from the gradient in equation ( 6) only in terms of the weighting factors # ) ! in each summation term.…”

“…Another idea is to pare down the set of parameters to be identified by making judicious use of prior information on some parameters [18][19][20][21], in some cases derived from previous waves of a multi-wave epidemic [22]. Perhaps the most traveled road to a solution has been the use of various parameter search algorithms [18,[23][24][25][26][27][28][29] which, when it comes down to it, offer only a marginal improvement over brute force search [30]. Bayesian estimation may be better able to Version 4 22-May-2024 3 integrate prior information into our search procedures [31][32][33][34][35][36], but its computational burden is usually even greater.…”

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

“…Due to the novelty of the virus, its epidemiological parameters are unknown, so the SEIR model is fitted to historical COVID-19 data, and the resulting estimated parameters are used to predict future cases. Bayesian optimization [6] , metaheuristics (e.g., particle swarm optimization, stochastic fractal search) [7] – [10] , [104] , [108] – [114] , neural networks [11] , [115] , [116] , and nonlinear curve-fitting based optimization methods [12] , [13] , [117] – [119] are some of the most popular approaches used to fit the model to the data and estimate the epidemiological parameters of the model, such as the reproduction number. In addition to forecasting COVID-19 cases, some studies considered additional aspects, such as the effect of different non-pharmaceutical intervention policies (social distancing and lockdown) and re-opening plans [101] , [114] , [120] – [127] .…”