Spatial epidemiology of networked metapopulation: an overview

Abstract

An emerging disease is one infectious epidemic caused by a newly transmissible pathogen, which has either appeared for the first time or already existed in human populations, having the capacity to increase rapidly in incidence as well as geographic range. Adapting to human immune system, emerging diseases may trigger large-scale pandemic spreading, such as the transnational spreading of SARS, the global outbreak of A(H1N1), and the recent potential invasion of avian influenza A(H7N9). To study the dynamics mediating the transmission of emerging diseases, spatial epidemiology of networked metapopulation provides a valuable modeling framework, which takes spatially distributed factors into consideration. This review elaborates the latest progresses on the spatial metapopulation dynamics, discusses empirical and theoretical findings that verify the validity of networked metapopulations, and the sketches application in evaluating the effectiveness of disease intervention strategies as well.

Keywords: Complex networks; Epidemiology; Metapopulation; Spatial dynamics.

Fig. 1

(Color online) Schematic illustrations of the SIS (a) and the SIR (b) compartment models, where β, μ denote the transmission rate and the recovery rate, respectively

Fig. 2

(Color online) Illustration of the individual-network frame of the networked metapopulation model. a The model is composed of a network of subpopulations. The disease transmission among subpopulations stems from the mobility of infected individuals. b Each subpopulation refers to a location, in which a population of individuals interplays according to the compartment rule (e.g., SIR) that induces local disease outbreaks. Individuals are transferred among subpopulations via mobility networks

Fig. 3

(Color online) Air transportation network (a) vs. commuting network (b) of the US. Long-range airlines dominate the air transportation network, whereas the commuting routes are much geographically localized

Fig. 4

(Color online) Effect of location-specific human contact patterns. a, b The structure of the phenomenological metapopulation model used in [124], where the reaction-commuting processes couple two typical subpopulations x, y. In the destination-driven scenario (a), individual characteristic contact rates (c _x, c _y) depend on the visited locations, while in the origin-driven scenario (b), the contacts of individuals correlate to their subpopulations of residence. c, d The phase diagrams of the global $R_0^g$ under these two scenarios, respectively. The white dashed curve in each panel shows the global threshold $R_0^g$ obtained through the NGM analysis. From Wang et al. [124]