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 Rt has been the main parameter used by national and local governments worldwide for describing the pandemic behavior synthetically. Rt 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 Rt 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.
Modelling the COVID-19 infection rate through a Physics-Informed learning approach / De Rosa, M.; Giampaolo, F.; Piccialli, F.; Cuomo, S.. - (2023), pp. 212-218. (Intervento presentato al convegno 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023 tenutosi a University of Naples "Parthenope", ita nel 2023) [10.1109/PDP59025.2023.00041].
Modelling the COVID-19 infection rate through a Physics-Informed learning approach
De Rosa M.;Giampaolo F.;Piccialli F.
;Cuomo S.
2023
Abstract
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 Rt has been the main parameter used by national and local governments worldwide for describing the pandemic behavior synthetically. Rt 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 Rt 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
