| Abstract | Traditional epidemiological research has focused on rate-based differential-equation models on completely mixing populations.
In this paper, we outline an approach based on a combination of net-
work 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 anal-
ysis of disease-spread in such networks. We identify a number of new
measures that are significant for understanding epidemics and for devel-
oping new strategies in policy planning. We also perform a very detailed
structural analysis of the social contact networks produced by TRAN-
SIMS, a simulator for detailed transportation/traffic studies, and study
two random graph models to generate realistic social networks: Chung-
Lu’s model and the configuration model. We also develop combinatorial
formulations and approximation algorithms for quarantining, vaccina-
tion and sensor placement, as aids to decision-making.
|
