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Built environment supports all the daily activities and shapes our health.Leveraging informative street view imagery, previous research has establishedthe profound correlation between the built environment and chronic,non-communicable diseases; however, predicting the exposure risk of infectiousdiseases remains largely unexplored. The person-to-person contacts andinteractions contribute to the complexity of infectious disease, which isinherently different from non-communicable diseases. Besides, the complexrelationships between street view imagery and epidemic exposure also hinderaccurate predictions. To address these problems, we construct a regionalmobility graph informed by the gravity model, based on which we propose atransmission-aware graph convolutional network (GCN) to capture diseasetransmission patterns arising from human mobility. Experiments show that theproposed model significantly outperforms baseline models by 8.54% in weightedF1, shedding light on a low-cost, scalable approach to assess epidemic exposurerisks from street view imagery.

Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery