Multiplex mobility network and metapopulation epidemic simulations of Italy based on open data

Abstract

The patterns of human mobility play a key role in the spreading of infectious diseases and thus represent a key ingredient of epidemic modeling and forecasting. Unfortunately, as the Covid-19 pandemic has dramatically highlighted, for the vast majority of countries there is no availability of granular mobility data. This hinders the possibility of developing computational frameworks to monitor the evolution of the disease and to adopt timely and adequate prevention policies. Here we show how this problem can be addressed in the case study of Italy. We build a multiplex mobility network based solely on open data, and implement a SIR metapopulation model that allows scenario analysis through data-driven stochastic simulations. The mobility flows that we estimate are in agreement with real-time proprietary data from smartphones. Our modeling approach can thus be useful in contexts where high-resolution mobility data is not available.

Opening of hotels and ski facilities: Impact on mobility, spending, and Covid‐19 outcomes

“…It is also reassuring that FB mobility data has been shown to correlate well with other mobility sources, such as geolocation from mobile operator O2 (Jeffrey et al, 2020) and Google mobility measures (Pérez-Arnal et al, 2021). On the other hand, Desiderio et al (2022) used open source data, for example, train and flight traffic, to argue that FB may underestimate long-distance movements. Such bias would make estimated effects on long-distance travel conservative.…”

“…On the other hand, Desiderio et al. (2022) used open source data, for example, train and flight traffic, to argue that FB may underestimate long‐distance movements. Such bias would make estimated effects on long‐distance travel conservative.…”