A NOVEL APPROACH FOR PREDICTING EPIDEMIOLOGICAL FORECASTING PARAMETERS BASED ON REAL-TIME SIGNALS AND DATA ASSIMILATION

作     者：Molinas, Romain Casas, César Quilodrán Arcucci, Rossella Şerban, Ovidiu 

作者机构：Department of Computing Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom Data Science Institute Department of Earth Science & Engineering Imperial College London United Kingdom Data Science Institute Department of Computing Imperial College London United Kingdom 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：COVID 19 

摘      要：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. © 2023, CC BY-SA.