On the Use of Human Mobility Proxies for Modeling Epidemics

By M. Tizzoni, P. Bajardi, Adeline Decuyper, G. K. K. King, Christian M. Schneider, Vincent D. Blondel, Zbigniew Smoreda, Marta C. González, Vittoria Colizza. PLoS Comput. Biol., 2013

367 citations recorded by the source corpus.

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.

Subfields: Epidemiology, Modeling and Simulation, Transportation

View Paper: https://www.semanticscholar.org/paper/80622365c531185e967a82e6fe6db43163b561ee

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Citations in the corpus

Statistical physics of vaccination (2016, methodology): “The data can be visualized in near real-time, providing interactive maps describing the current unfolding of ILIs that are readily available for modeling efforts at the sub-population level as those described in Sections 3 and 8.”
A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015, background): “…, estimates population densities 8,9 , models cities structures 10 , predicts socio-economic indicators and outcomes of territories 11 – 13 , and models the spread of diseases 10,14 – 17 (See Blondel et al. 18 for a comprehensive review of recent advances in studies using mobile phone datasets).”
COVID-19 lockdown induces disease-mitigating structural changes in mobility networks (2020, positive): “We implement a well-known commuter-dynamics SIR metapopulation model (32) with minor modifications.”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “[61] compared the ability of different types of data to effectively represent commuting networks at different spatial scales.”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “The characterization of individuals and entire populations’ mobility is of paramount importance for public health [57]: for example, it is key to predict the spatial and temporal risk of diseases [35, 82, 94], to quantify exposure to air pollution [48], to understand human migrations after natural d”
Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, neutral): “For example, Wesolowski et”