Multiscale mobility networks and the spatial spreading of infectious diseases

By Duygu Balcan, Vittoria Colizza, Bruno Gonçalves, Hao Hu, José J. Ramasco, Alessandro Vespignani. Proceedings of the National Academy of Sciences of the United States of America, 2009

1,383 citations recorded by the source corpus.

Among the realistic ingredients to be considered in the computational modeling of infectious diseases, human mobility represents a crucial challenge both on the theoretical side and in view of the limited availability of empirical data. To study the interplay between short-scale commuting flows and long-range airline traffic in shaping the spatiotemporal pattern of a global epidemic we (i) analyze mobility data from 29 countries around the world and find a gravity model able to provide a global description of commuting patterns up to 300 kms and (ii) integrate in a worldwide-structured metapopulation epidemic model a timescale-separation technique for evaluating the force of infection due to multiscale mobility processes in the disease dynamics. Commuting flows are found, on average, to be one order of magnitude larger than airline flows. However, their introduction into the worldwide model shows that the large-scale pattern of the simulated epidemic exhibits only small variations with respect to the baseline case where only airline traffic is considered. The presence of short-range mobility increases, however, the synchronization of subpopulations in close proximity and affects the epidemic behavior at the periphery of the airline transportation infrastructure. The present approach outlines the possibility for the definition of layered computational approaches where different modeling assumptions and granularities can be used consistently in a unifying multiscale framework.

Subfields: Modeling and Simulation, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/4ba297a677e500bfb11711a8d0969911394d2428

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Citations in the corpus

Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, methodology): “These results allowed de ﬁ ning the spatial – temporal features to improve the estimates of the individual ’ s ﬁ nancial well-being 21 .”
Mathematical modeling of infectious disease dynamics (2013, background): “Burke et al. (2006) presented an agent-based model for the spread of smallpox.”
Human mobility: Models and applications (2017, methodology): “It has been also used in situations where the knowledge of mobility flows is essential but the empirical data is not available as in the case of the characterization and modeling of epidemic spreading patterns [162, 163, 164, 165, 155].”
Statistical physics of vaccination (2016, methodology): “Further research on behavior-vaccination dynamics on temporal networks also requires better integration with empirical data, which we cover in more detail in Section 11 [600, 601].”
Inter-Series Attention Model for COVID-19 Forecasting (2020, background): “Moreover, geographic information can also be incorporated into the mechanistic models to better illustrate the spreading process of an infectious disease [3, 4].”
Understanding Human Mobility from Twitter (2014, background): “We find that the higher resolution Twitter data reveals multiple modes of human mobility [2] from intra-site to metropolitan and inter-city movements.”