Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top exploration

1 code implementation • 30 Jan 2023 • Alexandra Cimpean, Timothy Verstraeten, Lander Willem, Niel Hens, Ann Nowé, Pieter Libin

$m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty.

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

no code implementations • 11 Apr 2022 • Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Rădulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin

As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models.

Decision Making Multi-Objective Reinforcement Learning +1

Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data

1 code implementation • 8 Oct 2021 • Nicolas Franco, Pietro Coletti, Lander Willem, Leonardo Angeli, Adrien Lajot, Steven Abrams, Philippe Beutels, Christel Faes, Niel Hens

Several important aspects related to SARS-CoV-2 transmission are not well known due to a lack of appropriate data.

Deep reinforcement learning for large-scale epidemic control

1 code implementation • 30 Mar 2020 • Pieter Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Nowé

For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza.

Computational Efficiency reinforcement-learning +1