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

Sergio Rica, Gonzalo A. Ruz

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

5 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728194684
DOI
EstadoPublicada - 27 oct. 2020
Evento2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020 - Vina del Mar, Chile
Duración: 27 oct. 202029 oct. 2020

Serie de la publicación

Nombre2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020

Conferencia

Conferencia2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2020
País/TerritorioChile
CiudadVina del Mar
Período27/10/2029/10/20

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