Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead

The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the v...

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Main Authors: Kathleen Carvalho, João Paulo Vicente, Mihajlo Jakovljevic, João Paulo Ramos Teixeira
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Bioengineering
Subjects:
Online Access:https://www.mdpi.com/2306-5354/8/6/84
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spelling doaj-c534fc3f6eb84d1197a8e8990fe216902021-06-30T23:54:52ZengMDPI AGBioengineering2306-53542021-06-018848410.3390/bioengineering8060084Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks AheadKathleen Carvalho0João Paulo Vicente1Mihajlo Jakovljevic2João Paulo Ramos Teixeira3Research Centre in Digitalization and Intelligent Robotics (CeDRI)—Instituto Politecnico de Braganca, 5300-253 Bragança, PortugalResearch Centre in Digitalization and Intelligent Robotics (CeDRI)—Instituto Politecnico de Braganca, 5300-253 Bragança, PortugalDepartment of Global Health Economics and Policy, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, SerbiaResearch Centre in Digitalization and Intelligent Robotics (CeDRI)—Instituto Politecnico de Braganca, 5300-253 Bragança, PortugalThe use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people.https://www.mdpi.com/2306-5354/8/6/84time series predictionANN forecastingnew coronavirusCOVID-19 prediction casesCOVID-19 prediction deathsCOVID-19 prediction ICU
collection DOAJ
language English
format Article
sources DOAJ
author Kathleen Carvalho
João Paulo Vicente
Mihajlo Jakovljevic
João Paulo Ramos Teixeira
spellingShingle Kathleen Carvalho
João Paulo Vicente
Mihajlo Jakovljevic
João Paulo Ramos Teixeira
Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead
Bioengineering
time series prediction
ANN forecasting
new coronavirus
COVID-19 prediction cases
COVID-19 prediction deaths
COVID-19 prediction ICU
author_facet Kathleen Carvalho
João Paulo Vicente
Mihajlo Jakovljevic
João Paulo Ramos Teixeira
author_sort Kathleen Carvalho
title Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead
title_short Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead
title_full Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead
title_fullStr Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead
title_full_unstemmed Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead
title_sort analysis and forecasting incidence, intensive care unit admissions, and projected mortality attributable to covid-19 in portugal, the uk, germany, italy, and france: predictions for 4 weeks ahead
publisher MDPI AG
series Bioengineering
issn 2306-5354
publishDate 2021-06-01
description The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people.
topic time series prediction
ANN forecasting
new coronavirus
COVID-19 prediction cases
COVID-19 prediction deaths
COVID-19 prediction ICU
url https://www.mdpi.com/2306-5354/8/6/84
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