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The patterns of human mobility play a key role in the spreading of infectiousdiseases and thus represent a key ingredient of epidemic modeling andforecasting. Unfortunately, as the Covid-19 pandemic has dramaticallyhighlighted, for the vast majority of countries there is no availability ofgranular mobility data. This hinders the possibility of developingcomputational frameworks to monitor the evolution of the disease and to adopttimely and adequate prevention policies. Here we show how this problem can beaddressed in the case study of Italy. We build a multiplex mobility networkbased solely on open data, and implement a SIR metapopulation model that allowsscenario analysis through data-driven stochastic simulations. The mobilityflows that we estimate are in agreement with real-time proprietary data fromsmartphones. Our modeling approach can thus be useful in contexts wherehigh-resolution mobility data is not available.

Multiplex mobility network and metapopulation epidemic simulations of Italy based on Open Data