An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak

Valerio La Gatta; Vincenzo Moscato; Marco Postiglione; Giancarlo Sperli

doi:10.1109/TBDATA.2020.3032755

An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak

IEEE Trans Big Data. 2020 Oct 21;7(1):45-55. doi: 10.1109/TBDATA.2020.3032755. eCollection 2021 Mar 1.

Authors

Valerio La Gatta¹, Vincenzo Moscato¹, Marco Postiglione¹, Giancarlo Sperli¹

Affiliation

¹ Department of Electrical and Information TechnologyUniversity of Naples Federico II 80125 Naples Italy.

Abstract

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.

Keywords: COVID-19; data analytics; data model; deep learning; epidemic diffusion modeling; graph machine learning; spatio temporal data mining.