Details

Autor(en) / Beteiligte

• Reymond, Mathieu
• Hayes, Conor F.
• Willem, Lander
• Rădulescu, Roxana
• Abrams, Steven
• Roijers, Diederik M.
• Howley, Enda
• Mannion, Patrick
• Hens, Niel
• Nowé, Ann
• Libin, Pieter

Titel

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

Ist Teil von

• Expert systems with applications, 2024-09, Vol.249, Article 123686

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision-making in the context of epidemic mitigation is multi-dimensional hence complex, reinforcement learning in combination with complex epidemic models provides a methodology to design refined prevention strategies. Current research focuses on optimizing policies with respect to a single objective, such as the pathogen’s attack rate. However, as the mitigation of epidemics involves distinct, and possibly conflicting, criteria (i.a., mortality, morbidity, economic cost, well-being), a multi-objective decision approach is warranted to obtain balanced policies. To enhance future decision-making, we propose a deep multi-objective reinforcement learning approach by building upon a state-of-the-art algorithm called Pareto Conditioned Networks (PCN) to obtain a set of solutions for distinct outcomes of the decision problem. We consider different deconfinement strategies after the first Belgian lockdown within the COVID-19 pandemic and aim to minimize both COVID-19 cases (i.e., infections and hospitalizations) and the societal burden induced by the mitigation measures. As such, we connected a multi-objective Markov decision process with a stochastic compartment model designed to approximate the Belgian COVID-19 waves and explore reactive strategies. As these social mitigation measures are implemented in a continuous action space that modulates the contact matrix of the age-structured epidemic model, we extend PCN to this setting. We evaluate the solution set that PCN returns, and observe that it explored the whole range of possible social restrictions, leading to high-quality trade-offs, as it captured the problem dynamics. In this work, we demonstrate that multi-objective reinforcement learning adds value to epidemiological modeling and provides essential insights to balance mitigation policies. •Epidemic mitigation involves multiple criteria (mortality, economic cost, well-being).•Multi-objective RL (MORL) to explore the Pareto front of deconfinement strategies.•We investigate deconfinement strategies after the first Belgian lockdown of COVID-19.•Minimize both COVID-19 cases and societal burden; leads to many distinct trade-offs.•MORL brings essential insights to balance mitigation policies and help policy makers.