A novel approach for predicting epidemiological forecasting parameters based on real-time signals and Data Assimilation
This provides policymakers with more robust epidemiological forecasting tools, though it appears incremental as it builds on existing CNN and data assimilation methods.
The paper tackles epidemiological forecasting by integrating real-time signals like social media-based population density maps and air quality data with an ensemble of CNN models and data assimilation. The approach outperformed traditional compartmental models for COVID-19 outbreak prediction in London, improving performance, flexibility, stability, and accuracy.
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.