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In this paper, we introduce Temporal Multiresolution Graph Neural Networks(TMGNN), the first architecture that both learns to construct the multiscaleand multiresolution graph structures and incorporates the time-series signalsto capture the temporal changes of the dynamic graphs. We have applied ourproposed model to the task of predicting future spreading of epidemic andpandemic based on the historical time-series data collected from the actualCOVID-19 pandemic and chickenpox epidemic in several European countries, andhave obtained competitive results in comparison to other previousstate-of-the-art temporal architectures and graph learning algorithms. We haveshown that capturing the multiscale and multiresolution structures of graphs isimportant to extract either local or global information that play a criticalrole in understanding the dynamic of a global pandemic such as COVID-19 whichstarted from a local city and spread to the whole world. Our work brings apromising research direction in forecasting and mitigating future epidemics andpandemics.

Temporal Multiresolution Graph Neural Networks For Epidemic Prediction