Modelling the COVID-19 infection rate through a Physics-Informed learning approach

Over the past two years, the COVID-19 pandemic has been one of the most frequently and hotly debated social topics. Lockdowns and restrictions radically change the way of working and socializing due to social distancing and wearing masks; the ongoing pandemic impacts people's life and psychological health. Infection Rate R
t
has been the main parameter used by national and local governments worldwide for describing the pandemic behavior synthetically. R
t
was adopted to define containment policies (lockdowns, social distancing, intermittent regional strategies, etc.) that have affected social life. In the present paper, we propose an Artificial Intelligence (AI) approach for the modeling of the COVID-19 Infection Rate R
t
by exploiting the novel methodology of the Physics-Informed Neural Networks (PINNs) to compute the susceptible-infected-dead-recovered (SIDR) model. To test the accuracy of the neural network, we predicted the susceptible, infected, dead, and recovered on the next 30 days against the considered period.