Optimizing spatiotemporal nonpharmaceutical interventions for influenza: An adaptive reinforcement learning approach for regional heterogeneity.

ABSTRACT:

Background

Influenza remains a significant global public health challenge because of its high transmissibility, widespread circulation, and considerable societal impact. Conventional threshold-based nonpharmaceutical interventions (NPIs) provide valuable frameworks for outbreak control; however, these standardized approaches may not fully account for important regional heterogeneity. It remains difficult to weigh regional characteristics and accurately balance infection control and socioeconomic costs.

Methods

We propose a susceptible-exposed-infectious-quarantined-removed compartmental model-dueling deep Q-network (SEIQR-Dueling DQN) framework tailored to plains, hilly, and plateau cities. By integrating climatic, demographic, and health care resource data, the model captur

SUBMITTER: Tian Y 

PROVIDER: S-EPMC12547227 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature