Presenter: Nick Bambos, Stanford University*
Date: 08 July 2024

We model epidemic spreading of rumors/misinformation (or malware, pathogens) in a population of individuals (or devices) within a large region. We allow for mobility of infected individuals, who can infect others while roaming around. We distribute fact checking (testing) centers around the region, where individuals testing positive are "quarantined." Infected and recovered individuals cannot be reinfected in the current epidemic wave. The infection transmission rate (so called, infectivity) is a random field over the region.

We explore conditions under which the infection inherently spreads, as opposed to dying out. We observe that the average infectivity is not enough to characterize spreading, as fluctuations do matter. Indeed, even under subcritical infectivity averages, overcritical fluctuations can cause the infection to spread. We then focus on the impact of testing center density on suppressing an epidemic that has the inherent potential to spread. Finally, we discuss how to optimize testing vs. recovery resources.

• Joint work with Prof. Aris Moustakas (UoA, Physics) and Kyriakos Lotidis (Stanford)
• In memory of Petros Meramveliotakis (UoA, Physics), who was a key contributor to this work

Biography

Nick Bambos is R. Weiland Professor of electrical engineering and of management science & engineering at Stanford University. He received is PhD in electrical engineering and computer sciences from the University of California at Berkeley in 1989 and his engineering diploma from NTUA in 1984. His main research interests are in high-performance engineering of computer systems and networks, including comp/comm resource scheduling, power management of data centers, computing risk management, etc. His methodological interests are in stochastic modeling and control, machine learning and AI.