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Mobility restriction is considered one of the main policies to containCOVID-10 spreading. However, there are multiple ways to reduce mobility viadifferentiated restrictions, and it is not easy to predict the actual impact onvirus spreading. This is a limitation for policy-makers who need to implementeffective and timely measures. Notwithstanding the big role of data analysis tounderstand this phenomenon, it is also important to have more general modelscapable of predicting the impact of different scenarios. Besides, they shouldbe able to simulate scenarios in a disaggregated way, so to understand thepossible impact of targeted strategies, e.g. on a geographical scale or inrelation to other variables associated with the potential risk of infection.This paper presents an agent-based model (ABM) able to dynamically simulate theCOVID-19 spreading under different mobility restriction scenarios. The modeluses the Italian case study with its 20 administrative regions and considersparameters that can be attributed to the diffusion and lethality of the virus(based on a virus spread risk model) and population mobility patterns. Themodel is calibrated with real data and reproduces the impact that differentmobility restrictions can have on the pandemic diffusion based on a combinationof static and dynamic parameters. Results suggest that virus spreading wouldhave been similar if differentiated mobility restriction strategies based ona-priori risk parameters instead of a national lockdown would have been put inplace in Italy during the first wave of the pandemic. The proposed model couldgive useful suggestions for decision-makers to tackle pandemics and virusspreading at a strategic level.

Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach