Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning
arXiv:2603.19397v1 Announce Type: new Abstract: Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained by limited resources, particularly in early outbreak stages. In real-world public health settings, resources must be allocated across multiple outbreak clusters that emerge asynchronously, vary in size and risk, and compete for a shared resource budget. Here, a cluster corresponds to a group of close contacts generated by a single infected index case. Thus, decisions must be made under uncertainty and heterogeneous demands, while respecting operational constraints. We formulate this problem as a constrained restless multi-armed bandit and propose a hierarchical reinforcement learning framework. A global controller learns a continuous action cost multiplier that adjusts global resource demand, while a generalized local policy estimates the marginal value of allocating resour
arXiv:2603.19397v1 Announce Type: new Abstract: Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained by limited resources, particularly in early outbreak stages. In real-world public health settings, resources must be allocated across multiple outbreak clusters that emerge asynchronously, vary in size and risk, and compete for a shared resource budget. Here, a cluster corresponds to a group of close contacts generated by a single infected index case. Thus, decisions must be made under uncertainty and heterogeneous demands, while respecting operational constraints. We formulate this problem as a constrained restless multi-armed bandit and propose a hierarchical reinforcement learning framework. A global controller learns a continuous action cost multiplier that adjusts global resource demand, while a generalized local policy estimates the marginal value of allocating resources to individuals within each cluster. We evaluate the proposed framework in a realistic agent-based simulator of SARS-CoV-2 with dynamically arriving clusters. Across a wide range of system scales and testing budgets, our method consistently outperforms RMAB-inspired and heuristic baselines, improving outbreak control effectiveness by 20%-30%. Experiments on up to 40 concurrently active clusters further demonstrate that the hierarchical framework is highly scalable and enables faster decision-making than the RMAB-inspired method.
Executive Summary
This article proposes a hierarchical reinforcement learning framework to optimize resource-constrained non-pharmaceutical interventions for multi-cluster outbreak control. The framework consists of a global controller and a generalized local policy, which learn to adjust resource allocation and estimate marginal value, respectively. The authors evaluate their method in a realistic simulator of SARS-CoV-2 and demonstrate its superiority over baseline methods in terms of outbreak control effectiveness and scalability. The study's findings have significant implications for public health policy and practice, particularly in resource-limited settings. The research contributes to the growing body of work on using machine learning and artificial intelligence to address complex problems in healthcare.
Key Points
- ▸ The authors formulate the problem of resource-constrained NPIs as a constrained restless multi-armed bandit.
- ▸ They propose a hierarchical reinforcement learning framework to optimize resource allocation.
- ▸ The framework consists of a global controller and a generalized local policy.
- ▸ The authors evaluate their method in a realistic simulator of SARS-CoV-2 and demonstrate its superiority over baseline methods.
Merits
Strength in Scalability
The proposed hierarchical framework is highly scalable and enables faster decision-making than baseline methods, making it suitable for large-scale outbreak scenarios.
Improved Outbreak Control Effectiveness
The framework consistently outperforms baseline methods in terms of outbreak control effectiveness, improving effectiveness by 20%-30%.
Demerits
Assumes Simplified Cluster Dynamics
The study assumes simplified cluster dynamics, which may not accurately reflect real-world complexities, and may not be generalizable to all types of infectious disease outbreaks.
Limited Evaluation of Human Factors
The study focuses on optimizing resource allocation, but does not fully explore the impact of human factors, such as decision-maker behavior and communication, on the effectiveness of the proposed framework.
Expert Commentary
The proposed hierarchical reinforcement learning framework is a significant contribution to the field of public health decision-making. The study's findings demonstrate the potential of machine learning to improve outbreak control effectiveness and scalability in resource-constrained settings. However, the study's limitations, such as the assumption of simplified cluster dynamics and limited evaluation of human factors, highlight the need for further research in this area. The study's implications for public health policy and practice are significant, and the proposed framework can inform resource allocation and policy decisions during outbreaks. To build on this work, future studies should explore the impact of human factors on the effectiveness of the proposed framework and investigate its generalizability to different types of infectious disease outbreaks.
Recommendations
- ✓ Future studies should investigate the impact of human factors on the effectiveness of the proposed framework.
- ✓ The study's findings should be validated in real-world public health settings to demonstrate its applicability and effectiveness.
Sources
Original: arXiv - cs.LG
