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Deep Spatiotemporal Model for COVID-19 Forecasting
COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize the use of policies a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101138/ https://www.ncbi.nlm.nih.gov/pubmed/35591208 http://dx.doi.org/10.3390/s22093519 |
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author | Muñoz-Organero, Mario Queipo-Álvarez, Paula |
author_facet | Muñoz-Organero, Mario Queipo-Álvarez, Paula |
author_sort | Muñoz-Organero, Mario |
collection | PubMed |
description | COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. Although previous machine learning models based on time pattern analysis for COVID-19 sensed data have shown promising results, the spread of the virus has both spatial and temporal components. This manuscript proposes a new deep learning model that combines a time pattern extraction based on the use of a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19 incidence images. The model has been validated with data from the 286 health primary care centers in the Comunidad de Madrid (Madrid region, Spain). The results show improved scores in terms of both root mean square error (RMSE) and explained variance (EV) when compared with previous models that have mainly focused on the temporal patterns and dependencies. |
format | Online Article Text |
id | pubmed-9101138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91011382022-05-14 Deep Spatiotemporal Model for COVID-19 Forecasting Muñoz-Organero, Mario Queipo-Álvarez, Paula Sensors (Basel) Article COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. Although previous machine learning models based on time pattern analysis for COVID-19 sensed data have shown promising results, the spread of the virus has both spatial and temporal components. This manuscript proposes a new deep learning model that combines a time pattern extraction based on the use of a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19 incidence images. The model has been validated with data from the 286 health primary care centers in the Comunidad de Madrid (Madrid region, Spain). The results show improved scores in terms of both root mean square error (RMSE) and explained variance (EV) when compared with previous models that have mainly focused on the temporal patterns and dependencies. MDPI 2022-05-05 /pmc/articles/PMC9101138/ /pubmed/35591208 http://dx.doi.org/10.3390/s22093519 Text en © 2022 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 Muñoz-Organero, Mario Queipo-Álvarez, Paula Deep Spatiotemporal Model for COVID-19 Forecasting |
title | Deep Spatiotemporal Model for COVID-19 Forecasting |
title_full | Deep Spatiotemporal Model for COVID-19 Forecasting |
title_fullStr | Deep Spatiotemporal Model for COVID-19 Forecasting |
title_full_unstemmed | Deep Spatiotemporal Model for COVID-19 Forecasting |
title_short | Deep Spatiotemporal Model for COVID-19 Forecasting |
title_sort | deep spatiotemporal model for covid-19 forecasting |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101138/ https://www.ncbi.nlm.nih.gov/pubmed/35591208 http://dx.doi.org/10.3390/s22093519 |
work_keys_str_mv | AT munozorganeromario deepspatiotemporalmodelforcovid19forecasting AT queipoalvarezpaula deepspatiotemporalmodelforcovid19forecasting |