Infection Spread with External Agents

Professor Sanjay Shakkottai
Professor, University of Texas at Austin
Given on: Feb. 12th, 2015
Venue: Packard 101
Time: 4:15pm to 5:15pm


We study the effect of external infection sources on phase transitions in epidemic processes. In particular, we consider an epidemic spreading on a network via the SI and SIS dynamics, which in addition is aided by external agents - sources unconstrained by the graph, but possessing a limited infection rate or virulence. Such a model captures many existing models of externally aided epidemics, and finds use in many settings - epidemiology, marketing and advertising, network robustness, etc. We provide a detailed characterization of the impact of external agents on epidemic thresholds. For networks which are ‘spatially constrained’, we show that the spread of infection with the SI model can be significantly speeded up even by a few such external agents infecting randomly. On the other hand, for the SIS model, we show that any external infection strategy with constant virulence either fails to significantly affect the lifetime of an epidemic, or at best, sustains the epidemic for a lifetime which is polynomial in the number of nodes. However, a random external-infection strategy, with rate increasing linearly in the number of infected nodes, succeeds under some conditions to sustain an exponential epidemic lifetime. We finally explore the effects of bounded susceptibility on epidemic spread, and discuss the relevance of our results in a variety of settings. Based on joint work with S. Banerjee, A. Chatterjee, A. Das, A. Gopalan, and S. Krishnasamy.


Sanjay Shakkottai received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign in 2002. He is with the ECE Department at the University of Texas at Austin, where he is currently the Ashley H. Priddy Centennial Professor in Engineering. He received the NSF CAREER award in 2004, and was elected as an IEEE Fellow in 2014. His current research interests include network architectures, algorithms and performance analysis for wireless networks, and learning and inference over social networks.