Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery
This work addresses the problem of assessing epidemic exposure risks for public health applications, offering a low-cost, scalable approach, though it is incremental as it builds on existing street view imagery methods.
The paper tackled predicting epidemic exposure risks from street view imagery by constructing a regional mobility graph and using a transmission-aware GCN to capture disease transmission patterns, resulting in an 8.54% improvement in weighted F1 over baselines.
Built environment supports all the daily activities and shapes our health. Leveraging informative street view imagery, previous research has established the profound correlation between the built environment and chronic, non-communicable diseases; however, predicting the exposure risk of infectious diseases remains largely unexplored. The person-to-person contacts and interactions contribute to the complexity of infectious disease, which is inherently different from non-communicable diseases. Besides, the complex relationships between street view imagery and epidemic exposure also hinder accurate predictions. To address these problems, we construct a regional mobility graph informed by the gravity model, based on which we propose a transmission-aware graph convolutional network (GCN) to capture disease transmission patterns arising from human mobility. Experiments show that the proposed model significantly outperforms baseline models by 8.54% in weighted F1, shedding light on a low-cost, scalable approach to assess epidemic exposure risks from street view imagery.