Name
Abstract | ||
|---|---|---|
Pandemic influenza has the epidemic potential to kill millions of people. While different preventive measures exist, it remains challenging to implement them in an effective and efficient way. To improve preventive strategies, it is necessary to thoroughly understand their impact on the complex dynamics of influenza epidemics. To this end, epidemiological models provide an essential tool to evaluate such strategies in silico. Epidemiological models are frequently used to assist the decision making concerning the mitigation of ongoing epidemics. Therefore, rapidly identifying the most promising preventive strategies is crucial to adequately inform public health officials. To this end, we formulate the evaluation of prevention strategies as a multi-armed bandit problem. Through experiments, we demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, even if there is a large number of preventive strategies to consider. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-71679-4_5 | AAMAS Workshops (Visionary Papers) |
Keywords | Field | DocType |
Epidemiological models,Preventive strategies,Pandemic influenza,Multi-armed bandits,Reinforcement learning | Public health,Computer science,Pandemic,Management science,Reinforcement learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pieter Libin | 1 | 13 | 5.28 |
| Timothy Verstraeten | 2 | 4 | 2.43 |
| Kristof Theys | 3 | 2 | 1.52 |
| Diederik M. Roijers | 4 | 198 | 24.72 |
| Peter Vrancx | 5 | 132 | 21.02 |
| Ann Nowé | 6 | 971 | 123.04 |