La Gatta Valerio, Moscato Vincenzo, Postiglione Marco, Sperli Giancarlo
Department of Electrical and Information TechnologyUniversity of Naples Federico II 80125 Naples Italy.
IEEE Trans Big Data. 2020 Oct 21;7(1):45-55. doi: 10.1109/TBDATA.2020.3032755. eCollection 2021 Mar 1.
With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively influence the epidemic diffusion. In this paper, we propose a novel machine learning based framework able to estimate the parameters of any epidemiological model, such as contact rates and recovery rates, based on static and dynamic features of places. In particular, we model mobility data through a graph series whose spatial and temporal features are investigated by combining Graph Convolutional Neural Networks (GCNs) and Long short-term memories (LSTMs) in order to infer the parameters of SIR and SIRD models. We evaluate the proposed approach using data related to the COVID-19 dynamics in Italy and we compare the forecasts of the trained model with available data about the epidemic spread.
随着近期新冠疫情的爆发，我们助力开发了新的疫情模型，或应用现有方法来预测病毒传播，并分析不同的封锁策略如何有效影响疫情扩散。在本文中，我们提出了一个基于机器学习的新颖框架，该框架能够根据场所的静态和动态特征估计任何流行病学模型的参数，如接触率和康复率。具体而言，我们通过一个图序列对移动性数据进行建模，通过结合图卷积神经网络（GCN）和长短期记忆网络（LSTM）来研究其空间和时间特征，以推断SIR和SIRD模型的参数。我们使用与意大利新冠疫情动态相关的数据评估了所提出的方法，并将训练模型的预测结果与有关疫情传播的现有数据进行了比较。
