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

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

Estimation of Parameters in the SIR Model Using a Particle Swarm Optimization Algorithm. / Marquardt, Niklas; Hendriok, Leo; Haus, Benedikt et al.
Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023. ed. / Swagatam Das; Snehanshu Saha; Carlos A. Coello Coello; Hemant Rathore; Jagdish Chand Bansal. Springer Science and Business Media Deutschland, 2024. p. 157-167 (Lecture Notes in Networks and Systems; Vol. 890).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Marquardt, N, Hendriok, L, Haus, B & Mercorelli, P 2024, Estimation of Parameters in the SIR Model Using a Particle Swarm Optimization Algorithm. in S Das, S Saha, CAC Coello, H Rathore & JC Bansal (eds), Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023. Lecture Notes in Networks and Systems, vol. 890, Springer Science and Business Media Deutschland, pp. 157-167, 2nd International Conference on Advances in Data-driven Computing and Intelligent Systems - ADCIS 2023, BITS Pilani, India, 21.09.23. https://doi.org/10.1007/978-981-99-9531-8_13

APA

Marquardt, N., Hendriok, L., Haus, B., & Mercorelli, P. (2024). Estimation of Parameters in the SIR Model Using a Particle Swarm Optimization Algorithm. In S. Das, S. Saha, C. A. C. Coello, H. Rathore, & J. C. Bansal (Eds.), Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023 (pp. 157-167). (Lecture Notes in Networks and Systems; Vol. 890). Springer Science and Business Media Deutschland. https://doi.org/10.1007/978-981-99-9531-8_13

Vancouver

Marquardt N, Hendriok L, Haus B, Mercorelli P. Estimation of Parameters in the SIR Model Using a Particle Swarm Optimization Algorithm. In Das S, Saha S, Coello CAC, Rathore H, Bansal JC, editors, Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023. Springer Science and Business Media Deutschland. 2024. p. 157-167. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-9531-8_13

Bibtex

@inbook{485e40066e36443285be65ecc75ec255,
title = "Estimation of Parameters in the SIR Model Using a Particle Swarm Optimization Algorithm",
abstract = "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.",
keywords = "Lotka-Volterra model, Parameter identification, Particle Swarm Optimization, Engineering",
author = "Niklas Marquardt and Leo Hendriok and Benedikt Haus and Paolo Mercorelli",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 2nd International Conference on Advances in Data-driven Computing and Intelligent Systems - ADCIS 2023, ADCIS 2023 ; Conference date: 21-09-2023 Through 23-09-2023",
year = "2024",
doi = "10.1007/978-981-99-9531-8_13",
language = "English",
isbn = "978-981-99-9530-1",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland",
pages = "157--167",
editor = "Swagatam Das and Snehanshu Saha and Coello, {Carlos A. Coello} and Hemant Rathore and Bansal, {Jagdish Chand}",
booktitle = "Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023",
address = "Germany",
url = "https://scrs.in/conference/adcis23",

}

RIS

TY - CHAP

T1 - Estimation of Parameters in the SIR Model Using a Particle Swarm Optimization Algorithm

AU - Marquardt, Niklas

AU - Hendriok, Leo

AU - Haus, Benedikt

AU - Mercorelli, Paolo

N1 - Conference code: 2

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Lotka-Volterra model

KW - Parameter identification

KW - Particle Swarm Optimization

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85192136759&partnerID=8YFLogxK

U2 - 10.1007/978-981-99-9531-8_13

DO - 10.1007/978-981-99-9531-8_13

M3 - Article in conference proceedings

AN - SCOPUS:85192136759

SN - 978-981-99-9530-1

T3 - Lecture Notes in Networks and Systems

SP - 157

EP - 167

BT - Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023

A2 - Das, Swagatam

A2 - Saha, Snehanshu

A2 - Coello, Carlos A. Coello

A2 - Rathore, Hemant

A2 - Bansal, Jagdish Chand

PB - Springer Science and Business Media Deutschland

T2 - 2nd International Conference on Advances in Data-driven Computing and Intelligent Systems - ADCIS 2023

Y2 - 21 September 2023 through 23 September 2023

ER -

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Research output: Journal contributions › Journal articles › Research › peer-review

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Research output: Journal contributions › Journal articles › Research › peer-review

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Research output: Journal contributions › Journal articles › Research › peer-review

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Kurniawan, S. & Mercorelli, P., 27.05.2025, 5th Congress on Intelligent Systems, CIS 2024: Volume 1. Kumar, S., Mary Anita, E. A., Kim, J. H. & Nagar, A. (eds.). Springer Science and Business Media Deutschland, p. 309-320 12 p. (Lecture Notes in Networks and Systems; vol. 1275).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

DOI

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