  OBJECTIVES: to describe the first wave of the COVID-19 pandemic with a focus
on undetected cases and to evaluate different post-lockdown scenarios. DESIGN:
the study introduces a SEIR compartmental model, taking into account the
region-specific fraction of undetected cases, the effects of mobility
restrictions, and the personal protective measures adopted, such as wearing a
mask and washing hands frequently. SETTING AND PARTICIPANTS: the model is
experimentally validated with data of all the Italian regions, some European
countries, and the US. MAIN OUTCOME MEASURES: the accuracy of the model results
is measured through the mean absolute percentage error (MAPE) and Lewis
criteria; fitting parameters are in good agreement with previous literature.
RESULTS: the epidemic curves for different countries and the amount of
undetected and asymptomatic cases are estimated, which are likely to represent
the main source of infections in the near future. The model is applied to the
Hubei case study, which is the first place to relax mobility restrictions.
Results show different possible scenarios. Mobility and the adoption of
personal protective measures greatly influence the dynamics of the infection,
determining either a huge and rapid secondary epidemic peak or a more delayed
and manageable one. CONCLUSIONS: mathematical models can provide useful
insights for healthcare decision makers to determine the best strategy in case
of future outbreaks.
