Estimating SIR model parameters from data using differential evolution: An application with COVID-19 data

Sergio Rica, Gonzalo A. Ruz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The problem of fitting parameters of a dynamical system appears to be relevant in many areas of knowledge, like weather forecasting, system biology, epidemiology, and financial markets. In this paper, we analyze the Susceptible-Infected-Recovered (SIR) epidemiological model. We first derive an alternative representation of the SIR model, reducing it to one differential equation that models the cumulative number of infected cases in function of time. Then we present a differential evolution approach to estimate the parameters of this dynamical model from data. We illustrate the proposed approach with COVID-19 data from Santiago, Chile. The goodness of fit, obtained by the differential evolution algorithm outperformed ten times the results obtained by a random search strategy used in previous works.

Original languageEnglish
Title of host publication2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194684
DOIs
StatePublished - 27 Oct 2020
Event2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020 - Vina del Mar, Chile
Duration: 27 Oct 202029 Oct 2020

Publication series

Name2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020

Conference

Conference2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020
Country/TerritoryChile
CityVina del Mar
Period27/10/2029/10/20

Keywords

  • COVID-19 data
  • Differential Evolution
  • Dynamical System
  • SIR model

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