A novel approach for predicting epidemiological forecasting parameters
  based on real-time signals and Data Assimilation
Romain Molinas César Quilodrán Casas Rossella Arcucci Ovidiu Şerban
This paper proposes a novel approach to predict epidemiological parameters by
integrating new real-time signals from various sources of information, such as
novel social media-based population density maps and Air Quality data. We
implement an ensemble of Convolutional Neural Networks (CNN) models using
various data sources and fusion methodology to build robust predictions and
simulate several dynamic parameters that could improve the decision-making
process for policymakers. Additionally, we used data assimilation to estimate
the state of our system from fused CNN predictions. The combination of
meteorological signals and social media-based population density maps improved
the performance and flexibility of our prediction of the COVID-19 outbreak in
London. While the proposed approach outperforms standard models, such as
compartmental models traditionally used in disease forecasting (SEIR),
generating robust and consistent predictions allows us to increase the
stability of our model while increasing its accuracy.