Cargando…

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, Fr...

Descripción completa

Detalles Bibliográficos
Autores principales: Cinaglia, Pietro, Cannataro, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322732/
https://www.ncbi.nlm.nih.gov/pubmed/35885152
http://dx.doi.org/10.3390/e24070929
_version_ 1784756377436028928
author Cinaglia, Pietro
Cannataro, Mario
author_facet Cinaglia, Pietro
Cannataro, Mario
author_sort Cinaglia, Pietro
collection PubMed
description On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, [Formula: see text] has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an [Formula: see text] estimation by adjusting the data produced by the output layer of the NN on the related [Formula: see text] estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the [Formula: see text] as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the [Formula: see text]. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.
format Online
Article
Text
id pubmed-9322732
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-93227322022-07-27 Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation Cinaglia, Pietro Cannataro, Mario Entropy (Basel) Article On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, [Formula: see text] has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an [Formula: see text] estimation by adjusting the data produced by the output layer of the NN on the related [Formula: see text] estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the [Formula: see text] as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the [Formula: see text]. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively. MDPI 2022-07-04 /pmc/articles/PMC9322732/ /pubmed/35885152 http://dx.doi.org/10.3390/e24070929 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
Cinaglia, Pietro
Cannataro, Mario
Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation
title Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation
title_full Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation
title_fullStr Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation
title_full_unstemmed Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation
title_short Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R(t) Estimation
title_sort forecasting covid-19 epidemic trends by combining a neural network with r(t) estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322732/
https://www.ncbi.nlm.nih.gov/pubmed/35885152
http://dx.doi.org/10.3390/e24070929
work_keys_str_mv AT cinagliapietro forecastingcovid19epidemictrendsbycombininganeuralnetworkwithrtestimation
AT cannataromario forecastingcovid19epidemictrendsbycombininganeuralnetworkwithrtestimation