Predicting the COVID-19 in the Metropolitan Region (Chile) using a GCN-LSTM neural network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

COVID-19 is considered one of the largest pandemics in recent times. Predicting the number of future COVID-19 cases is extremely important for governments in order to make decisions about mobility restrictions, and for hospitals to be able to manage medical supplies, as well as health staff. Most of the predictions of COVID-19 cases are based on mathematical-epidemiological models such as the SEIR and SIR models. In our work, we propose a model of neural networks GCN-LSTM (Graph Convolutional Network - Long Short Term Memory) to predict the spatio-temporal rate incidence of COVID-19 in the Metropolitana Region, Chile. While the GCN network incorporates the spatial correlation in the nearby municipalities, the LSTM network considers the temporal correlation for the prediction over time. To interpolate the missing daily data for the network input, the use of the GAM (Generalized Additive Model) model is proposed. The results show better predictions for some municipalities with higher habitat density.

Original languageEnglish
Title of host publication2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665408738
DOIs
Publication statusPublished - 2021
Event2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021 - Virtual, Online, Chile
Duration: 6 Dec 20219 Dec 2021

Publication series

Name2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021

Conference

Conference2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Country/TerritoryChile
CityVirtual, Online
Period6/12/219/12/21

Keywords

  • GCN-LSTM
  • Neural networks
  • Spatio-temporal models

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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