Natural human mobility patterns and spatial spread of infectious diseases

By Vitaly Belik, Theo Geisel, Dirk Brockmann. 2011

355 citations recorded by the source corpus.

We investigate a model for spatial epidemics explicitly taking into account bidirectional movements between base and destination locations on individual mobility networks. We provide a systematic analysis of generic dynamical features of the model on regular and complex metapopulation network topologies and show that significant dynamical differences exist to ordinary reaction-diffusion and effective force of infection models. On a lattice we calculate an expression for the velocity of the propagating epidemic front and find that, in contrast to the diffusive systems, our model predicts a saturation of the velocity with an increasing traveling rate. Furthermore, we show that a fully stochastic system exhibits a novel threshold for the attack ratio of an outbreak that is absent in diffusion and force of infection models. These insights not only capture natural features of human mobility relevant for the geographical epidemic spread, they may serve as a starting point for modeling important dynamical processes in human and animal epidemiology, population ecology, biology, and evolution.

Subfields: Computer Networks and Communications, Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/919188aa7f53a2455e7432db3d96057879cff0f7

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Citations in the corpus

Exploring the impacts of COVID-19 on travel behavior and mode preferences (2020, background): “Past studies have highlighted that human mobility and interaction patterns directly contribute to the spread of infectious diseases, particularly during pandemics (Funk et al., 2010; Belik et al., 2011; Rizzo et al., 2014; Yan et al., 2018; Peixoto et al., 2020).”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “…mobility as a critical need for broad-domain applications (e.g., ranging from simple home preheating and sending dinner coupons to epidemic control [221], [222], urban planning [223]–[225], trafﬁc forecasting systems [226]–[228], resource management of mobile communications [3], [229], [230]).”
Modelling dynamical processes in complex socio-technical systems (2011, background): “Another interesting example is that of simple epidemic processes, such as the SIR model in a metapopulation context 79,82–90 .”
Memory in network flows and its effects on spreading dynamics and community detection (2013, methodology): “f ﬂow only depends on the currently visited city (Fig.1A). This conventional memoryless approach is used in a wide range of problems, from ranking web pages (7,21) to modeling the spread of epidemics (11,12). However, this approach ignores where the passengers come from, and therefore the direction ”