Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street
  View Imagery
Zhenyu Han Yanxin Xi Tong Xia Yu Liu Yong Li
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.