Resource allocation and statistical estimation in epidemic networks: Scalable algorithms and analysis
Abstract
Our proposed research will advance the frontiers of stochastic modeling for dynamic networks. We will analyze problems in influence maximization, fractional immunization and boosting, and detection and estimation, with the unifying theme of devising computationally efficient algorithms with rigorous mathematical guarantees for allocating resources and estimating characteristics of time-varying networks. This work significantly generalizes and extends existing approaches in network science by allowing greater flexibility in modeling epidemics and interventions. In particular, we will depart from standard submodularity assumptions known to be irreflective of real-world cascades, and allow targeted interventions to be fractional rather than binary. We will also develop algorithms suited to incorporate uncertainty about network connectivity, and provide methods for estimating graph characteristics based on sensor data. Our research is relevant to multiple areas of interest to the Army. Influence maximization supplies valuable recommendations for broad knowledge dissemination under a fixed budget, which is vital in military and defense operations. Our work would lead to more accurate recommendations, due to more faithful modeling of reality. When the goal is to maximally retard an epidemic outbreak (e.g., an infectious disease or cyberattack), it is expedient to allocate resources efficiently in order to contain the epidemic. In cases where ensuring full immunity of individuals is unrealistic, our research would provide new recommendations informing which individuals or relationships to target. Finally, our work on detection and estimation would allow gleaning of important information about network characteristics based on data from sensors/agents in the graph, which may be useful for military intelligence.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 10, 2019
- Source ID
- W911NF1910343
Entities
People
- Po-ling Loh
Organizations
- Army Contracting Command
- Defense Advanced Research Projects Agency
- University of Wisconsin–Madison