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.

翻译：本文提出了一种通过整合多源实时信号（包括基于社交媒体的新型人口密度图与空气质量数据）来预测流行病参数的新方法。我们构建了由卷积神经网络（CNN）模型组成的集成系统，采用多源数据融合策略生成稳健预测，并模拟可优化政策制定者决策流程的动态参数。同时，利用数据同化技术从融合后的CNN预测结果中估计系统状态。气象信号与基于社交媒体的人口密度地图的结合，提升了我们对伦敦COVID-19疫情预测的性能与灵活性。相较于传统疾病预测中常用的房室模型（如SEIR），本方法不仅展现出更优表现，其生成的稳健一致性预测还能在提升准确性的同时增强模型稳定性。