Estimation of Parameters in the SIR Model Using a Particle Swarm Optimization Algorithm

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


The paper deals with the estimation of parameters in the Susceptible-Infected-Removed (SIR) model of infectious disease transmission in populations using an algorithm based on a Particle Swarm Optimization (PSO) method. The used model in this contribution is based on the dynamics of predator and prey populations. A possible model is represented by the Lotka-Volterra model which is used in such kinds of competitive ecosystems. SIR models can be considered as a particular form of Lotka-Volterra models, in which susceptible and infected people represent the prey and predator. The main contribution of this investigation consists of showing how the model can reflect the real data using a low number of compartments in case the model parameters are estimated adaptively using an optimal procedure. A PSO method is used in a receding horizon window-like structure to estimate these parameters. The procedure is validated using real values of the COVID-19 pandemic in Germany, demonstrating a close matching.

TitelAdvances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023
HerausgeberSwagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Hemant Rathore, Jagdish Chand Bansal
Anzahl der Seiten11
VerlagSpringer Science and Business Media Deutschland GmbH
ISBN (Print)978-981-99-9530-1
ISBN (elektronisch)978-981-99-9531-8
PublikationsstatusErschienen - 2024
Veranstaltung2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023 - BITS Pilani, Indien
Dauer: 21.09.202323.09.2023

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© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.