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Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory
The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads caus...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8307714/ https://www.ncbi.nlm.nih.gov/pubmed/34299827 http://dx.doi.org/10.3390/ijerph18147376 |
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author | Stevenson, Finn Hayasi, Kentaro Bragazzi, Nicola Luigi Kong, Jude Dzevela Asgary, Ali Lieberman, Benjamin Ruan, Xifeng Mathaha, Thuso Dahbi, Salah-Eddine Choma, Joshua Kawonga, Mary Mbada, Mduduzi Tripathi, Nidhi Orbinski, James Mellado, Bruce Wu, Jianhong |
author_facet | Stevenson, Finn Hayasi, Kentaro Bragazzi, Nicola Luigi Kong, Jude Dzevela Asgary, Ali Lieberman, Benjamin Ruan, Xifeng Mathaha, Thuso Dahbi, Salah-Eddine Choma, Joshua Kawonga, Mary Mbada, Mduduzi Tripathi, Nidhi Orbinski, James Mellado, Bruce Wu, Jianhong |
author_sort | Stevenson, Finn |
collection | PubMed |
description | The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces. |
format | Online Article Text |
id | pubmed-8307714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83077142021-07-25 Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory Stevenson, Finn Hayasi, Kentaro Bragazzi, Nicola Luigi Kong, Jude Dzevela Asgary, Ali Lieberman, Benjamin Ruan, Xifeng Mathaha, Thuso Dahbi, Salah-Eddine Choma, Joshua Kawonga, Mary Mbada, Mduduzi Tripathi, Nidhi Orbinski, James Mellado, Bruce Wu, Jianhong Int J Environ Res Public Health Article The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces. MDPI 2021-07-09 /pmc/articles/PMC8307714/ /pubmed/34299827 http://dx.doi.org/10.3390/ijerph18147376 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Stevenson, Finn Hayasi, Kentaro Bragazzi, Nicola Luigi Kong, Jude Dzevela Asgary, Ali Lieberman, Benjamin Ruan, Xifeng Mathaha, Thuso Dahbi, Salah-Eddine Choma, Joshua Kawonga, Mary Mbada, Mduduzi Tripathi, Nidhi Orbinski, James Mellado, Bruce Wu, Jianhong Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory |
title | Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory |
title_full | Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory |
title_fullStr | Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory |
title_full_unstemmed | Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory |
title_short | Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory |
title_sort | development of an early alert system for an additional wave of covid-19 cases using a recurrent neural network with long short-term memory |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8307714/ https://www.ncbi.nlm.nih.gov/pubmed/34299827 http://dx.doi.org/10.3390/ijerph18147376 |
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