Evaluating the adequacy of gravity models as a description of human mobility for epidemic modelling.

TitleEvaluating the adequacy of gravity models as a description of human mobility for epidemic modelling.
Publication TypeJournal Article
Year of Publication2012
AuthorsTruscott J, Ferguson NM
JournalPLoS Comput Biol
Date Published2012
KeywordsCluster Analysis, Databases, Factual, Disease Transmission, Infectious, Epidemics, Epidemiologic Methods, Great Britain, Human Migration, Humans, Influenza, Human, Models, Biological, Population Dynamics, United States

Gravity models have a long history of use in describing and forecasting the movements of people as well as goods and services, making them a natural basis for disease transmission rates over distance. In agent-based micro-simulations, gravity models can be directly used to represent movement of individuals and hence disease. In this paper, we consider a range of gravity models as fits to movement data from the UK and the US. We examine the ability of synthetic networks generated from fitted models to match those from the data in terms of epidemic behaviour; in particular, times to first infection. For both datasets, best fits are obtained with a two-piece 'matched' power law distance distribution. Epidemics on synthetic UK networks match well those on data networks across all but the smallest nodes for a range of aggregation levels. We derive an expression for time to infection between nodes in terms of epidemiological and network parameters which illuminates the influence of network clustering in spread across networks and suggests an approximate relationship between the log-likelihood deviance of model fit and the match times to infection between synthetic and data networks. On synthetic US networks, the match in epidemic behaviour is initially poor and sensitive to the initially infected node. Analysis of times to infection indicates a failure of models to capture infrequent long-range contact between large nodes. An assortative model based on node population size captures this heterogeneity, considerably improving the epidemiological match between synthetic and data networks.

Alternate JournalPLoS Comput. Biol.
PubMed ID23093917
PubMed Central IDPMC3475681
Grant ListU54 GM088491 / GM / NIGMS NIH HHS / United States
/ / Medical Research Council / United Kingdom
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