  In late December 2019, a novel strand of Coronavirus (SARS-CoV-2) causing a
severe, potentially fatal respiratory syndrome (COVID-19) was identified in
Wuhan, Hubei Province, China and is causing outbreaks in multiple world
countries, soon becoming a pandemic. Italy has now become the most hit country
outside of Asia: on March 16, 2020, the Italian Civil Protection documented a
total of 27980 confirmed cases and 2158 deaths of people tested positive for
SARS-CoV-2. In the context of an emerging infectious disease outbreak, it is of
paramount importance to predict the trend of the epidemic in order to plan an
effective control strategy and to determine its impact. This paper proposes a
new epidemic model that discriminates between infected individuals depending on
whether they have been diagnosed and on the severity of their symptoms. The
distinction between diagnosed and non-diagnosed is important because
non-diagnosed individuals are more likely to spread the infection than
diagnosed ones, since the latter are typically isolated, and can explain
misperceptions of the case fatality rate and of the seriousness of the epidemic
phenomenon. Being able to predict the amount of patients that will develop
life-threatening symptoms is important since the disease frequently requires
hospitalisation (and even Intensive Care Unit admission) and challenges the
healthcare system capacity. We show how the basic reproduction number can be
redefined in the new framework, thus capturing the potential for epidemic
containment. Simulation results are compared with real data on the COVID-19
epidemic in Italy, to show the validity of the model and compare different
possible predicted scenarios depending on the adopted countermeasures.
