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

Página do item simplificado
dc.citation.volume105pt_BR
dc.contributor.authorPaiva, Henrique Mohallem [UNIFESP]
dc.contributor.authorAfonso, Rubens Junqueira Magalhães
dc.contributor.authorCaldeira, Fabiana Mara Scarpelli de Lima Alvarenga
dc.contributor.authorVelasquez, Ester de Andrade
dc.contributor.authorLatteshttp://lattes.cnpq.br/6901974057937430pt_BR
dc.contributor.authorLatteshttp://lattes.cnpq.br/2398613107941747pt_BR
dc.date.accessioned2021-07-29T19:17:23Z
dc.date.available2021-07-29T19:17:23Z
dc.date.issued2021-03-10
dc.description.abstractPurpose: 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.pt_BR
dc.description.provenanceSubmitted by Henrique Paiva (hmpaiva@unifesp.br) on 2021-07-29T18:35:15Z No. of bitstreams: 1 COVID19_Tool_preprint.pdf: 1597280 bytes, checksum: 078333f1bd215124fe6eb06109163cab (MD5)en
dc.description.provenanceApproved for entry into archive by Edna Lucia Pereira (edna.lucia@unifesp.br) on 2021-07-29T19:17:23Z (GMT) No. of bitstreams: 1 COVID19_Tool_preprint.pdf: 1597280 bytes, checksum: 078333f1bd215124fe6eb06109163cab (MD5)en
dc.description.provenanceMade available in DSpace on 2021-07-29T19:17:23Z (GMT). No. of bitstreams: 1 COVID19_Tool_preprint.pdf: 1597280 bytes, checksum: 078333f1bd215124fe6eb06109163cab (MD5) Previous issue date: 2021-03-10en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorshipID2020/14357-1pt_BR
dc.format.extent107289pt_BR
dc.identifierhttps://www.sciencedirect.com/science/article/abs/pii/S156849462100212Xpt_BR
dc.identifier.citationPaiva, 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.2021.107289pt_BR
dc.identifier.doi10.1016/j.asoc.2021.107289pt_BR
dc.identifier.urihttps://repositorio.unifesp.br/handle/11600/61337
dc.languageengpt_BR
dc.publisherElsevierpt_BR
dc.relation.ispartofApplied Soft Computingpt_BR
dc.rightsAcesso abertopt_BR
dc.subjectCOVID-19pt_BR
dc.subjectEpidemiologypt_BR
dc.subjectMathematical modelingpt_BR
dc.subjectTrend analysispt_BR
dc.subjectForecastpt_BR
dc.subjectNumerical optimizationpt_BR
dc.subjectSequential quadratic programming (SQP)pt_BR
dc.titleA computational tool for trend analysis and forecast of the COVID-19 pandemicpt_BR
dc.title.alternativeA data-driven model to describe and forecast the dynamics of COVID-19 transmissionpt_BR
dc.typeArtigopt_BR
unifesp.assessoresproreitoriasNão se aplicapt_BR
unifesp.campusInstituto de Ciência e Tecnologia (ICT)pt_BR
unifesp.departamentoCiência e Tecnologiapt_BR
unifesp.extensaoNão se aplicapt_BR
Arquivos
Pacote Original
Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
COVID19_Tool_preprint.pdf
Tamanho:
1.52 MB
Formato:
Adobe Portable Document Format
Descrição:
Artigo
Baixar
Licença do Pacote
Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
license.txt
Tamanho:
5.69 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar
Coleções
ICT - Artigos