A Flexible Rolling Regression Framework for Time-Varying SIRD models: Application to COVID-19

2021/03/02 by Javier Rubio‐Herrero, Y Wang, Rubio-Herrero, Javier +1
Engineering · Mathematics · #COVID-19 epidemiological studies #Control Systems and Identification #FOS: Biological sciences #FOS: Physical sciences #Fractional Differential Equations Solutions #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE)

paper · pdf · doi:10.48550/arxiv.2103.02048

openalex publication_date 2021/03/02 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28

Abstract

The present paper introduces a data-driven framework for describing the time-varying nature of an SIRD model in the context of COVID-19. By embedding a rolling regression in a mixed integer bilevel nonlinear programming problem, our aim is to provide the research community with a model that reproduces accurately the observed changes in the number of infected, recovered, and death cases, while providing information about the time dependency of the parameters that govern the SIRD model. We propose this optimization model and a genetic algorithm to tackle its solution. Moreover, we test this algorithm with 2020 COVID-19 data from the state of Minnesota and found that our results are consistent both qualitatively and quantitatively, thus proving that the framework proposed is an effective an flexible tool to describe the dynamics of a pandemic.

Citations

• The Mathematics of Infectious Diseases
• Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
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