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

Niklas Marquardt; Leo Hendriok; Benedikt Haus; Paolo Mercorelli

doi:10.1007/978-981-99-9531-8_13

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

Niklas Marquardt
, Leo Hendriok
, Benedikt Haus
, Paolo Mercorelli^*

^*Korrespondierende/r Autor/-in für diese Arbeit

Leuphana Universität Lüneburg

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

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.

Originalsprache	Englisch
Titel	Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023
Redakteure/-innen	Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Hemant Rathore, Jagdish Chand Bansal
Seitenumfang	11
Herausgeber (Verlag)	Springer Science and Business Media Deutschland
Erscheinungsdatum	2024
Seiten	157-167
ISBN (Print)	978-981-99-9530-1
ISBN (elektronisch)	978-981-99-9531-8
DOIs	https://doi.org/10.1007/978-981-99-9531-8_13
Publikationsstatus	Erschienen - 2024
Veranstaltung	2nd International Conference on Advances in Data-driven Computing and Intelligent Systems - ADCIS 2023 - BITS Pilani, Indien Dauer: 21.09.2023 → 23.09.2023 Konferenznummer: 2 https://scrs.in/conference/adcis23

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

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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 (Hrsg.), Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023 (S. 157-167). (Lecture Notes in Networks and Systems; Band 890). Springer Science and Business Media Deutschland. https://doi.org/10.1007/978-981-99-9531-8_13

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

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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.",

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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",

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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 (Hrsg.), Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023. Lecture Notes in Networks and Systems, Bd. 890, Springer Science and Business Media Deutschland, S. 157-167, 2nd International Conference on Advances in Data-driven Computing and Intelligent Systems - ADCIS 2023, BITS Pilani, Indien, 21.09.23. https://doi.org/10.1007/978-981-99-9531-8_13

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. Hrsg. / Swagatam Das; Snehanshu Saha; Carlos A. Coello Coello; Hemant Rathore; Jagdish Chand Bansal. Springer Science and Business Media Deutschland, 2024. S. 157-167 (Lecture Notes in Networks and Systems; Band 890).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

TY - CHAP

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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.

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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, Hrsg., Advances in Data-Driven Computing and Intelligent Systems - Selected Papers from ADCIS 2023. Springer Science and Business Media Deutschland. 2024. S. 157-167. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-9531-8_13

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

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