  Mobility restriction is considered one of the main policies to contain
COVID-10 spreading. However, there are multiple ways to reduce mobility via
differentiated restrictions, and it is not easy to predict the actual impact on
virus spreading. This is a limitation for policy-makers who need to implement
effective and timely measures. Notwithstanding the big role of data analysis to
understand this phenomenon, it is also important to have more general models
capable of predicting the impact of different scenarios. Besides, they should
be able to simulate scenarios in a disaggregated way, so to understand the
possible impact of targeted strategies, e.g. on a geographical scale or in
relation to other variables associated with the potential risk of infection.
This paper presents an agent-based model (ABM) able to dynamically simulate the
COVID-19 spreading under different mobility restriction scenarios. The model
uses the Italian case study with its 20 administrative regions and considers
parameters that can be attributed to the diffusion and lethality of the virus
(based on a virus spread risk model) and population mobility patterns. The
model is calibrated with real data and reproduces the impact that different
mobility restrictions can have on the pandemic diffusion based on a combination
of static and dynamic parameters. Results suggest that virus spreading would
have been similar if differentiated mobility restriction strategies based on
a-priori risk parameters instead of a national lockdown would have been put in
place in Italy during the first wave of the pandemic. The proposed model could
give useful suggestions for decision-makers to tackle pandemics and virus
spreading at a strategic level.
