Citation:
Han-Ching Ou, Arunesh Sinha, Sze-Chuan Suen, Andrew Perrault, and Milind Tambe. 2019. “Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty .” In Joint Workshop on Autonomous Agents for Social Good (AASG) at International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19).
Abstract:
Controlling recurrent infectious diseases is a vital yet complicated problem. In this paper, we propose a novel active screeningmodel (ACTS) and algorithms to facilitate active screening for recurrent diseases (no permanent immunity) under infection uncertainty. Our
contributions are: (1) A new approach to modeling multi-round networkbased screening/contact tracing under uncertainty, which is a common
real-life practice in a variety of diseases [10, 30]; (2) Two novel algorithms,
Full- and Fast-REMEDY. Full-REMEDY considers the effect of future actions and finds a policy that provides high solution quality, where
Fast-REMEDY scales linearly in the size of the network; (3) We evaluate
Full- and Fast-REMEDY on several real-world datasets which emulate human contact and find that they control diseases better than the
baselines. To the best of our knowledge, this is the first work on multiround active screening with uncertainty for diseases with no permanent
immunity.
See also: 2019