Individual identity and movement networks for disease metapopulations

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 107; no. 19; pp. 8866 - 8870
• Main Authors: Keeling, Matt J, Danon, Leon, Vernon, Matthew C, House, Thomas A
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 11.05.2010; National Acad Sciences
• Subjects: Agriculture; Animals; Biological Sciences; Cattle; Commuters; Disease; Disease models; Disease Outbreaks; Disease transmission; Epidemics; Epidemiology; farms; Great Britain; Humans; Infections; Infectious diseases; Metapopulation ecology; Metapopulations; Modeling; Models, Biological; Movement - physiology; Population Dynamics; prediction; Public health; Spatial models; Theory; Transportation; United Kingdom - epidemiology; United Kingdom > UK; Great Britain
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1000416107

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.