Structure of Social Contact Networks and Their Impact on Epidemics

Traditional epidemiological research has focused on ratebased differential-equation models on completely mixing populations. In this paper, we outline an approach based on a combination of network theory and discrete-event simulations to study epidemics in large urban areas. We survey some of our results published in Nature (2004) and the Proc. ACM-SIAM Symposium on Discrete Algorithms (2004), and present some new results on: (i) mathematical properties of large social contact networks, as well as (ii) simulation-based dynamic analysis of disease-spread in such networks. We identify a number of new measures that are significant for understanding epidemics and for developing new strategies in policy planning. We also perform a very detailed structural analysis of the social contact networks produced by TRANSIMS, a simulator for detailed transportation/traffic studies, and study two random graph models to generate realistic social networks: ChungLu’s model and the configuration model. We also develop combinatorial formulations and approximation algorithms for quarantining, vaccination and sensor placement, as aids to decision-making.

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