A computational tool for trend analysis and forecast of the COVID-19 pandemic
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNIFESP |
Texto Completo: | https://www.sciencedirect.com/science/article/abs/pii/S156849462100212X https://repositorio.unifesp.br/handle/11600/61337 |
Resumo: | 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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Repositório Institucional da UNIFESP |
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3465 |
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Paiva, Henrique Mohallem [UNIFESP]Afonso, Rubens Junqueira MagalhãesCaldeira, Fabiana Mara Scarpelli de Lima AlvarengaVelasquez, Ester de Andradehttp://lattes.cnpq.br/6901974057937430http://lattes.cnpq.br/23986131079417472021-07-29T19:17:23Z2021-07-29T19:17:23Z2021-03-10https://www.sciencedirect.com/science/article/abs/pii/S156849462100212XPaiva, H. M., Afonso, R. J. M., Caldeira, F. M. S. L. A., Velasquez, E. A. (2021). A computational tool for trend analysis and forecast of the COVID-19 pandemic. Applied Soft Computing, 105, 107289. https://doi.org/10.1016/j.asoc.2021.107289https://repositorio.unifesp.br/handle/11600/6133710.1016/j.asoc.2021.107289Purpose: 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2020/14357-1107289engElsevierApplied Soft ComputingCOVID-19EpidemiologyMathematical modelingTrend analysisForecastNumerical optimizationSequential quadratic programming (SQP)A computational tool for trend analysis and forecast of the COVID-19 pandemicA data-driven model to describe and forecast the dynamics of COVID-19 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InstitucionalPUBhttp://www.repositorio.unifesp.br/oai/requestopendoar:34652023-06-05T22:08:25Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP)false |
dc.title.pt_BR.fl_str_mv |
A computational tool for trend analysis and forecast of the COVID-19 pandemic |
dc.title.alternative.pt_BR.fl_str_mv |
A data-driven model to describe and forecast the dynamics of COVID-19 transmission |
title |
A computational tool for trend analysis and forecast of the COVID-19 pandemic |
spellingShingle |
A computational tool for trend analysis and forecast of the COVID-19 pandemic Paiva, Henrique Mohallem [UNIFESP] COVID-19 Epidemiology Mathematical modeling Trend analysis Forecast Numerical optimization Sequential quadratic programming (SQP) |
title_short |
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_sort |
A computational tool for trend analysis and forecast of the COVID-19 pandemic |
author |
Paiva, Henrique Mohallem [UNIFESP] |
author_facet |
Paiva, Henrique Mohallem [UNIFESP] Afonso, Rubens Junqueira Magalhães Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga Velasquez, Ester de Andrade |
author_role |
author |
author2 |
Afonso, Rubens Junqueira Magalhães Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga Velasquez, Ester de Andrade |
author2_role |
author author author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/6901974057937430 http://lattes.cnpq.br/2398613107941747 |
dc.contributor.author.fl_str_mv |
Paiva, Henrique Mohallem [UNIFESP] Afonso, Rubens Junqueira Magalhães Caldeira, Fabiana Mara Scarpelli de Lima Alvarenga Velasquez, Ester de Andrade |
dc.subject.por.fl_str_mv |
COVID-19 Epidemiology Mathematical modeling Trend analysis Forecast Numerical optimization Sequential quadratic programming (SQP) |
topic |
COVID-19 Epidemiology Mathematical modeling Trend analysis Forecast Numerical optimization Sequential quadratic programming (SQP) |
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. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-07-29T19:17:23Z |
dc.date.available.fl_str_mv |
2021-07-29T19:17:23Z |
dc.date.issued.fl_str_mv |
2021-03-10 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.pt_BR.fl_str_mv |
https://www.sciencedirect.com/science/article/abs/pii/S156849462100212X |
dc.identifier.citation.fl_str_mv |
Paiva, H. M., Afonso, R. J. M., Caldeira, F. M. S. L. A., Velasquez, E. A. (2021). A computational tool for trend analysis and forecast of the COVID-19 pandemic. Applied Soft Computing, 105, 107289. https://doi.org/10.1016/j.asoc.2021.107289 |
dc.identifier.uri.fl_str_mv |
https://repositorio.unifesp.br/handle/11600/61337 |
dc.identifier.doi.pt_BR.fl_str_mv |
10.1016/j.asoc.2021.107289 |
url |
https://www.sciencedirect.com/science/article/abs/pii/S156849462100212X https://repositorio.unifesp.br/handle/11600/61337 |
identifier_str_mv |
Paiva, H. M., Afonso, R. J. M., Caldeira, F. M. S. L. A., Velasquez, E. A. (2021). A computational tool for trend analysis and forecast of the COVID-19 pandemic. Applied Soft Computing, 105, 107289. https://doi.org/10.1016/j.asoc.2021.107289 10.1016/j.asoc.2021.107289 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Applied Soft Computing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
107289 |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UNIFESP instname:Universidade Federal de São Paulo (UNIFESP) instacron:UNIFESP |
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Universidade Federal de São Paulo (UNIFESP) |
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UNIFESP |
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UNIFESP |
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Repositório Institucional da UNIFESP |
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Repositório Institucional da UNIFESP |
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