Localized Epidemic Detection in Networks with Overwhelming Noise
Abstract
We consider the problem of detecting an epidemic in a population where individual diagnoses are extremely noisy. We show that exclusively local, approximate knowledge of the contact network suffices to accurately detect the epidemic. The motivation for this problem is the plethora of examples (influenza strains in humans, or computer viruses in smartphones, etc.) where reliable diagnoses are scarce, but noisy data plentiful. In flu or phone-viruses, exceedingly few infected people/phones are professionally diagnosed (only a small fraction go to a doctor) but less reliable secondary signatures (e.g., people staying home, or greater-than-typical upload activity) are more readily available.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 15, 2015
- Source ID
- 10.1145/2796314.2745883
Entities
People
- Ariel Orda
- Chris Milling
- Constantine Caramanis
- Eli A. Meirom
- Sanjay Shakkottai
- Shie Mannor
Organizations
- Army Research Office
- EU Business School
- National Science Foundation
- Technion – Israel Institute of Technology
- University of Texas at Austin