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.

本文提出了一种新的方法，利用来自各种信息源的新实时信号（例如基于社交媒体的人口密度地图和空气质量数据）来预测流行病学参数，使用多个卷积神经网络（CNN）模型的集合，并使用各种数据源和融合方法来构建稳健的预测，在伦敦的 COVID-19 爆发预测中，气象信号和基于社交媒体的人口密度地图的组合提高了性能和灵活性，并通过数据同化估计了我们系统的状态，从而提高了我们的模型的稳定性和准确性。

一种基于实时信号和数据同化的流行病预测参数预测新方法

A novel approach for predicting epidemiological forecasting parameters  based on real-time signals and Data Assimilation

In the last several years, remote sensing technology has opened up the
possibility of performing large scale building detection from satellite
imagery. Our work is some of the first to create population density maps from
building detection on a large scale. The scale of our work on population
density estimation via high resolution satellite images raises many issues,
that we will address in this paper. The first was data acquisition. Labeling
buildings from satellite images is a hard problem, one where we found our
labelers to only be about 85% accurate at. There is a tradeoff of quantity vs.
quality of labels, so we designed two separate policies for labels meant for
training sets and those meant for test sets, since our requirements of the two
set types are quite different. We also trained weakly supervised footprint
detection models with the classification labels, and semi-supervised approaches
with a small number of pixel-level labels, which are very expensive to procure.

利用遥感技术的建筑物检测创建人口密度地图，包括数据采集、半监督方法等解决策略。