An evaluation of COVID-19 in Italy: A data-driven modeling analysis
Yongmei Ding, Liyuan Gao
Abstract
The novel coronavirus (COVID-19) that has been spreading worldwide since December 2019 has sickened millions of people, lock down major cities and some countries, prompted unprecedented global travel restrictions. Real data-driven modeling is an effort to help evaluate and curb the spread of the novel virus. Lockdowns and the effectiveness of reduction in the contacts in Italy has been measured via our modified model, with the addition of auxiliary and state variables that represent, contacts with infected, conversion rate and latent propagation. Results show the decrease in infected people due to stay-at-home orders and tracing quarantine intervention. The effect of quarantine and centralized medical treatment was also measured through numerical modeling analysis.