Individual identity and movement networks for disease metapopulations

Abstract

The theory of networks has had a huge impact in both the physical and life sciences, shaping our understanding of the interaction between multiple elements in complex systems. In particular, networks have been extensively used in predicting the spread of infectious diseases where individuals, or populations of individuals, interact with a limited set of others-defining the network through which the disease can spread. Here for such disease models we consider three assumptions for capturing the network of movements between populations, and focus on two applied problems supported by detailed data from Great Britain: the commuter movement of workers between local areas (wards) and the permanent movement of cattle between farms. For such metapopulation networks, we show that the identity of individuals responsible for making network connections can have a significant impact on the infection dynamics, with clear implications for detailed public health and veterinary applications.etworks are now a well-understood and powerful scientific tool. When the number of interactions between elements or individuals is relatively low, then networks offer an intuitive means of capturing and describing the structure of such interactions. Examples abound, from computer and Internet connections (1) to metabolic networks (2) and food webs (3), from transportation patterns (4) to actors within the same movies (5)-in each of these, the theory of networks has provided valuable insights into how such interactions are structured and has hinted at deeper underlying patterns. When interactions describe connections between people, then network theory is often used to explore the implications of disease spread through the population (6). However, data on human-to-human contacts through which infections can spread are rare, generally being isolated to small populations that have been sampled with detailed questionnaires-of these, networks of sexual encounters, such as the studies in Colorado Springs (7) and Manitoba (8), are probably the best examples. Another common source of network interaction comes from the movement of individuals between populations, who could potentially carry infection with them. With concerns over novel or reemerging infections, such networks of movements often form the core of mathematical models that examine the spatiotemporal spread of infections and the associated public health implications. Examples include work movements for the spread of seasonal influenza in the United States (9, 10); aviation traffic for the spread of smallpox (11), sudden acute respiratory syndrome (12), and general epidemics (13); commuting movements for deliberate release of smallpox (14); and trade for the 1918-1919 influenza pandemic (15); a more systematic review of techniques and applications is available from Riley (16). The sensitivity of the epidemic to the topological structure of the movement network has already been shown (13, 17); in contrast here, through detailed simulation, we address how such movement networks should be model...

Memory in network flows and its effects on spreading dynamics and community detection

“…Previous work has considered temporal and memory effects on spreading by modelling timerespecting paths in temporal networks of contacts 22,23,52 and bidirectional paths in mobility networks of commuters 27,28,53,54 . Our objective is to quantify the full effect of second-order Markov dynamics in general mobility patterns.…”

Assessment of paper interstate certificates of veterinary inspection used to support disease tracing in cattle

“…[19][20][21] The time required to trace back an individual animal is greatly extended as the percentage of unidentified animals increases. 4 In the United States, it has been estimated that at least 70% of the animals in a specific species sector (eg, beef cattle) must be identified and traceable to effectively respond to an outbreak situation.…”

Quantifying direct and indirect contacts for the potential transmission of infection between species using a multilayer contact network

“…While networks have been used to reveal the importance of heterogeneity in social contacts (VanderWaal & Ezenwa, 2016;White et al, 2017) and in spatial networks of animal movements (Kao et al, 2006;Keeling et al, 2010;Jacoby & Freeman, 2016), there has been little empirical research quantifying variation in indirect transmission opportunities. This is important as quantifying and characterising this variation might facilitate the targeting of management efforts at key sources of environmental transmission.…”