# Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2101.02766 · HTML: https://mlsanity.com/paper/2101.02766/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling](https://mlsanity.com/paper/1907.11625/) (2019)
- [Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread](https://mlsanity.com/paper/1501.06614/) (2015)
- [Solving Continual Combinatorial Selection via Deep Reinforcement Learning](https://mlsanity.com/paper/1909.03638/) (2019)
- [An efficient curing policy for epidemics on graphs](https://mlsanity.com/paper/1407.2241/) (2014)
- [When is a network epidemic hard to eliminate?](https://mlsanity.com/paper/1510.06054/) (2015)
- [The Cost of Uncertainty in Curing Epidemics](https://mlsanity.com/paper/1711.00167/) (2017)

## Built on by (most cited first, 6 of 6)

- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Contingency-Aware Influence Maximization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2106.07039/) (2021)
- [Networked Restless Multi-Armed Bandits for Mobile Interventions](https://mlsanity.com/paper/2201.12408/) (2022)
- [Networked Restless Multi-Arm Bandits with Reinforcement Learning](https://mlsanity.com/paper/2512.06274/) (2025)
- [Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya](https://mlsanity.com/paper/2604.08902/) (2026)
- [Network-Based Interventions for HIV Prevention via Cascade-Aware Suppression of Transmission](https://mlsanity.com/paper/2605.20218/) (2026)

## Abstract (arXiv)

Active screening is a common approach in controlling the spread of recurring infectious diseases such as tuberculosis and influenza. In this approach, health workers periodically select a subset of population for screening. However, given the limited number of health workers, only a small subset of the population can be visited in any given time period. Given the recurrent nature of the disease and rapid spreading, the goal is to minimize the number of infections over a long time horizon. Active screening can be formalized as a sequential combinatorial optimization over the network of people and their connections. The main computational challenges in this formalization arise from i) the combinatorial nature of the problem, ii) the need of sequential planning and iii) the uncertainties in the infectiousness states of the population. Previous works on active screening fail to scale to large time horizon while fully considering the future effect of current interventions. In this paper, we propose a novel reinforcement learning (RL) approach based on Deep Q-Networks (DQN), with several innovative adaptations that are designed to address the above challenges. First, we use graph convolutional networks (GCNs) to represent the Q-function that exploit the node correlations of the underlying contact network. Second, to avoid solving a combinatorial optimization problem in each time period, we decompose the node set selection as a sub-sequence of decisions, and further design a two-level RL framework that solves the problem in a hierarchical way. Finally, to speed-up the slow convergence of RL which arises from reward sparseness, we incorporate ideas from curriculum learning into our hierarchical RL approach. We evaluate our RL algorithm on several real-world networks.