The Viral Wait: How Infections Spread in Queues

About This Grant

Understanding and mitigating the rapid spread of infectious diseases requires innovative mathematical tools that capture the complexity of real-world human interactions. This research project develops novel stochastic mathematical models that combine queueing theory and advanced probabilistic methods to shed a new light on how diseases propagate within service systems and broader social networks. By quantifying personalized infection risk in crowded environments such as hospitals and transportation systems, these models reveal how quickly a single infected individual can trigger widespread outbreaks in service systems. Furthermore, analyzing infectious disease spread in queueing systems offers valuable insights into side-channel attacks in cybersecurity. In particular, cryptographic operations can be modeled as tasks in a queue, with processing times influenced by factors such as data or device characteristics. Attackers exploit these time variations to extract sensitive information, such as cryptographic keys. By treating security systems as queues, the mathematical models in this proposal can help reveal potential information leakage, thereby contributing to the design of more robust cybersecurity measures. The resulting insights from this work will empower public health officials to make data-driven and model-driven decisions, which will ultimately reduce spread and optimize resource allocation during subsequent health crises, and also provide a framework for understanding and mitigating vulnerabilities in cybersecurity systems. Undergraduate and graduate students will participate in these research activities, contributing to STEM workforce training. This project develops new stochastic models to capture how infectious disease spread in service systems, with a parallel application to understanding cybersecurity vulnerabilities. The primary goal of this work is to provide deeper insights for service system managers and public health officials to assess the infection risk associated with waiting in lines at airports, hospitals, and transportation hubs during public health crises. Simultaneously, these models offer valuable insights into side-channel attacks in cybersecurity, where cryptographic operations can be conceptualized as tasks in a queue, and processing time variations can be exploited to extract sensitive information like cryptographic keys. By leveraging tools from queueing theory and stochastic processes, the research will quantify the individual infection risk of a susceptible individual and the community-based risk that an infected individual has on society, as well as reveal potential information leakage in security systems. The theoretical results will be validated through stochastic simulation and compared with real-world data from various service system environments and relevant cybersecurity contexts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.