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A computational tool for trend analysis and forecast of the COVID-19 pandemic

PURPOSE: This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. METHODS: Mathematical functions are employed to describe the number of cases and demises in each region and to predict...

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Autores principales: Paiva, Henrique Mohallem, Afonso, Rubens Junqueira Magalhães, Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga, Velasquez, Ester de Andrade
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944846/
https://www.ncbi.nlm.nih.gov/pubmed/33723487
http://dx.doi.org/10.1016/j.asoc.2021.107289
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author Paiva, Henrique Mohallem
Afonso, Rubens Junqueira Magalhães
Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga
Velasquez, Ester de Andrade
author_facet Paiva, Henrique Mohallem
Afonso, Rubens Junqueira Magalhães
Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga
Velasquez, Ester de Andrade
author_sort Paiva, Henrique Mohallem
collection PubMed
description PURPOSE: This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. METHODS: Mathematical functions are employed to describe the number of cases and demises in each region and to predict their final numbers, as well as the dates of maximum daily occurrences and the local stabilization date. The model parameters are calibrated using a computational methodology for numerical optimization. Trend analyses are run, allowing to assess the effects of public policies. Easy to interpret metrics over the quality of the fitted curves are provided. Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io project describing individually the occurrences in United States counties and in Brazilian states and cities, respectively. U. S. and Brazil were chosen for a more detailed analysis because they are the current focus of the pandemic. RESULTS: Illustrative results for different countries, U. S. counties and Brazilian states and cities are presented and discussed. CONCLUSION: The main contributions of this work lie in (i) a straightforward model of the curves to represent the data, which allows automation of the process without requiring interventions from experts; (ii) an innovative approach for trend analysis, whose results provide important information to support authorities in their decision-making process; and (iii) the developed computational tool, which is freely available and allows the user to quickly update the COVID-19 analyses and forecasts for any country, United States county or Brazilian state or city present in the periodic reports from the authorities.
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spelling pubmed-79448462021-03-11 A computational tool for trend analysis and forecast of the COVID-19 pandemic Paiva, Henrique Mohallem Afonso, Rubens Junqueira Magalhães Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga Velasquez, Ester de Andrade Appl Soft Comput Article PURPOSE: This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics. METHODS: Mathematical functions are employed to describe the number of cases and demises in each region and to predict their final numbers, as well as the dates of maximum daily occurrences and the local stabilization date. The model parameters are calibrated using a computational methodology for numerical optimization. Trend analyses are run, allowing to assess the effects of public policies. Easy to interpret metrics over the quality of the fitted curves are provided. Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io project describing individually the occurrences in United States counties and in Brazilian states and cities, respectively. U. S. and Brazil were chosen for a more detailed analysis because they are the current focus of the pandemic. RESULTS: Illustrative results for different countries, U. S. counties and Brazilian states and cities are presented and discussed. CONCLUSION: The main contributions of this work lie in (i) a straightforward model of the curves to represent the data, which allows automation of the process without requiring interventions from experts; (ii) an innovative approach for trend analysis, whose results provide important information to support authorities in their decision-making process; and (iii) the developed computational tool, which is freely available and allows the user to quickly update the COVID-19 analyses and forecasts for any country, United States county or Brazilian state or city present in the periodic reports from the authorities. Elsevier B.V. 2021-07 2021-03-10 /pmc/articles/PMC7944846/ /pubmed/33723487 http://dx.doi.org/10.1016/j.asoc.2021.107289 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Paiva, Henrique Mohallem
Afonso, Rubens Junqueira Magalhães
Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga
Velasquez, Ester de Andrade
A computational tool for trend analysis and forecast of the COVID-19 pandemic
title A computational tool for trend analysis and forecast of the COVID-19 pandemic
title_full A computational tool for trend analysis and forecast of the COVID-19 pandemic
title_fullStr A computational tool for trend analysis and forecast of the COVID-19 pandemic
title_full_unstemmed A computational tool for trend analysis and forecast of the COVID-19 pandemic
title_short A computational tool for trend analysis and forecast of the COVID-19 pandemic
title_sort computational tool for trend analysis and forecast of the covid-19 pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944846/
https://www.ncbi.nlm.nih.gov/pubmed/33723487
http://dx.doi.org/10.1016/j.asoc.2021.107289
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