Pandemic influenza has the epidemiological 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 \textit{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. The utility of this novel evaluation method is validated through experiments in the context of an individual-based influenza model.

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.
Original languageEnglish
Pages67-85
Number of pages19
DOIs
Publication statusPublished - 9 May 2017
Event2017 Adaptive Learning Agents (ALA) workshop: Workshop of the AAMAS conference - World trade center, Sao Paolo, Brazil
Duration: 8 May 20179 May 2017
http://ala2017.it.nuigalway.ie/

Workshop

Workshop2017 Adaptive Learning Agents (ALA) workshop
Abbreviated titleALA-2017
CountryBrazil
CitySao Paolo
Period8/05/179/05/17
Internet address

    Research areas

  • Epidemiological models, Multi-armed bandits, Pandemic influenza, Preventive strategies, Reinforcement learning

ID: 36442874