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

Truscott J, Ferguson NM - PLoS Comput. Biol. (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK. j.truscott@imperial.ac.uk

ABSTRACT
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.

Show MeSH

Related in: MedlinePlus

A) Comparison of observed node outflows with those generated by the globally-constrained MK model.B) Distance distribution of synthetic connections generated by SK and MK locally constrained models.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3475681&req=5

pcbi-1002699-g001: A) Comparison of observed node outflows with those generated by the globally-constrained MK model.B) Distance distribution of synthetic connections generated by SK and MK locally constrained models.

Mentions: Figure 1 illustrates the source of the differences in likelihoods. From the point of view of the two part likelihood expressed in equation 4, the globally-constrained model needs to fit the total outflow from each node as well as the relative probabilities of journeys starting from each node. As shown in Figure 1A, the model generally underestimates the number of travelling workers in a node.


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

Truscott J, Ferguson NM - PLoS Comput. Biol. (2012)

A) Comparison of observed node outflows with those generated by the globally-constrained MK model.B) Distance distribution of synthetic connections generated by SK and MK locally constrained models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475681&req=5

pcbi-1002699-g001: A) Comparison of observed node outflows with those generated by the globally-constrained MK model.B) Distance distribution of synthetic connections generated by SK and MK locally constrained models.
Mentions: Figure 1 illustrates the source of the differences in likelihoods. From the point of view of the two part likelihood expressed in equation 4, the globally-constrained model needs to fit the total outflow from each node as well as the relative probabilities of journeys starting from each node. As shown in Figure 1A, the model generally underestimates the number of travelling workers in a node.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK. j.truscott@imperial.ac.uk

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus