Data-driven discovery of mechanist evolution equations for an epidemic

Americo Cunha Jr (1) , Rachel Lucena (1) , Lisandro Lovisolo (1)

1 UERJ - Universidade do Estado do Rio de Janeiro [Brasil] = Rio de Janeiro State University [Brazil] = Université d'État de Rio de Janeiro [Brésil]

Infectious diseases are a historic reality, with violent epidemics affecting people's lives from time to time. In an epidemic, so that public managers and health professionals can better respond to the demands of the affected population, it is necessary to obtain a detailed understanding of the underlying mechanism of spread of the infectious disease. Mathematical models are a fundamental tool in this context, as they are able to provide rational explanations for the spread of the disease and, consequently, predict the intensity of its progress and test the effectiveness of different control strategies. In this presentation we will expose a novel strategy to discover informative mechanist evolution equations for epidemic phenomena directly from data, by means of a machine learning framework that employs sparse regression.

Domaines: Systèmes dynamiques [math.DS]; Optimisation et contrôle [math.OC]; Science non linéaire [physics]; Statistiques [math.ST]; Santé publique et épidémiologie

Americo Cunha Jr, Rachel Lucena, Lisandro Lovisolo. Data-driven discovery of mechanist evolution equations for an epidemic. Webinar The Mathematics of COVID-19, Nov 2021, Rio de Janeiro, Brazil. ⟨hal-03425947⟩