Projects per year
Abstract
Epidemics of infectious diseases are an important threat to public health and global economies.
Yet, the development of prevention strategies remains a challenging process.
For this reason, in this work, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in an epidemiological model, in the context of pandemic influenza.
To this end, we construct a new epidemiological metapopulation model, with 379 patches, that balances between model complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable.
First, we set up a ground truth such that we can evaluate the performance of the "Proximal Policy Optimization" algorithm to learn in a single district of this epidemiological model.
Next, we consider a larger scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established.
This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space.
Moreover, through this experiment, we demonstrate that there can be an advantage to consider collaboration between districts when designing prevention strategies.
Yet, the development of prevention strategies remains a challenging process.
For this reason, in this work, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in an epidemiological model, in the context of pandemic influenza.
To this end, we construct a new epidemiological metapopulation model, with 379 patches, that balances between model complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable.
First, we set up a ground truth such that we can evaluate the performance of the "Proximal Policy Optimization" algorithm to learn in a single district of this epidemiological model.
Next, we consider a larger scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established.
This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space.
Moreover, through this experiment, we demonstrate that there can be an advantage to consider collaboration between districts when designing prevention strategies.
Original language  English 

Title of host publication  Proceedings of the Adaptive and Learning Agents Workshop 2020 (ALA2020) at AAMAS 
Number of pages  9 
Publication status  Accepted/In press  2020 
Event  2020 Adaptive Learning Agents workshop at AAMAS  Auckland, New Zealand Duration: 9 May 2020 → 10 May 2020 https://ala2020.vub.ac.be 
Workshop
Workshop  2020 Adaptive Learning Agents workshop at AAMAS 

Abbreviated title  ALA 2020 
Country  New Zealand 
City  Auckland 
Period  9/05/20 → 10/05/20 
Internet address 
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Projects
 1 Finished

FWOSB9: Automatic learning of optimal prevention strategies for latent infectious diseases
1/01/16 → 31/12/19
Project: Applied