Estimating the State of Epidemics Spreading with Graph Neural Networks
This addresses the challenge of continuous monitoring in epidemics, such as CoVid-19, for public health authorities, though it appears incremental as it applies an existing method to new data.
The paper tackles the problem of inferring the epidemic state of an entire population from limited measurements by using Graph Convolutional Neural Networks, achieving results that demonstrate the model's capability to reason on social network structures in scenarios like a generic homogeneous population and a Boston metropolitan area model.
When an epidemic spreads into a population, it is often unpractical or impossible to have a continuous monitoring of all subjects involved. As an alternative, algorithmic solutions can be used to infer the state of the whole population from a limited amount of measures. We analyze the capability of deep neural networks to solve this challenging task. Our proposed architecture is based on Graph Convolutional Neural Networks. As such it can reason on the effect of the underlying social network structure, which is recognized as the main component in the spreading of an epidemic. We test the proposed architecture with two scenarios modeled on the CoVid-19 pandemic: a generic homogeneous population, and a toy model of Boston metropolitan area.