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
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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 GmbH, 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
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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 GmbH
T2 - 2nd International Conference on Advances in Data-driven Computing and Intelligent Systems - ADCIS 2023
Y2 - 21 September 2023 through 23 September 2023
ER -