Predicting COVID-19 Spread from Large-Scale Mobility Data

@article{Schwabe2021PredictingCS,
  title={Predicting COVID-19 Spread from Large-Scale Mobility Data},
  author={Amray Schwabe and Joel Persson and Stefan Feuerriegel},
  journal={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  year={2021}
}
To manage the COVID-19 epidemic effectively, decision-makers in public health need accurate forecasts of case numbers. A potential near real-time predictor of future case numbers is human mobility; however, research on the predictive power of mobility is lacking. To fill this gap, we introduce a novel model for epidemic forecasting based on mobility data, called mobility marked Hawkes model. The proposed model consists of three components: (1) A Hawkes process captures the transmission dynamics… 

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