About: COVID-SGIS: A smart tool for dynamic monitoring and temporal forecasting of Covid-19

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About: COVID-SGIS: A smart tool for dynamic monitoring and temporal forecasting of Covid-19 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-SGIS: A smart tool for dynamic monitoring and temporal forecasting of Covid-19
Creator	Kostkova, Patty Santos, Dos Filho, Silva Antônio, Luiz Araújo De Santana, Maíra Barbosa, Samuel Brito De Araújo, Lucas Cordeiro Da Silva, Cecilia De Freitas Barbosa, Augusto De Souza, Jatobá Gomes Da Silva, Ana Gomes, Juliana Guilhermino, Abel Júnior, Albuquerque Lins De Lima, Clarisse Musah, Anwar Pinheiro, Wellington Silva, Eduardo Souza Marques, Gabriel
Source	MedRxiv
abstract	Objective The new kind of coronavirus SARS-Cov2 spread to countries in all continents in the World. The coronavirus disease 2019 (Covid-19) causes fever, cough, sore throat, and in severe cases shortness of breath and death. To evaluate strategies, it is necessary to forecast the number of cases and deaths, in order to aid the stakeholders in the process of making decisions against the disease. We propose a system for real-time forecast of the cumulative cases of Covid-19 in Brazil. Study Design Monitoring of all Brazilian cities using oficial information from the National Notification System, from March to May 2020, concentrated on Brazil.io databases. Training and evaluation of ARIMA and other machine learning algorithms for temporal forecasting using correlation indexes (Pearson's, Spearman's, and Kendall's) and RMSE(%). Validation from the relative errors of the following six days. Methods Our developed software, COVID-SGIS, captures information from the 26 states and the Distrito Federal at the Brazil.io database. From these data, ARIMA models are created for the accumulation of confirmed cases and death cases by Covid-19. Finally, six-day forecasts graphs are available for Brazil and for each of its federative units, separately, with a 95% CI. In addition to these predictions, the worst and best scenarios are also presented. Results ARIMA models were generated for Brazil and its 27 federative units. The states of Bahia, Maranhao, Piaui, Rio Grande do Norte, Amapa, Rondonia every day of the predictions were in the projection interval. The same happened to the states of Espirito Santo, Minas Gerais, Parana and Santa Catarina. In Brazil, the percentage error between the predicted values and the actual values varied between 2.56% and 6.50%. For the days when the forecasts outside the prediction interval, the percentage errors in relation to the worst case scenario were below 5%. The states of Bahia, Maranhao, Piaui, Rio Grande do Norte, Amapa, and Rondonia every day of the predictions were in the projection interval. The same happened to the states of Espirito Santo, Minas Gerais, Parana and Santa Catarina. Conclusion The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a robust, flexible and fast way. Since it is based on information from multiple databases, it can be adapted to the different realities, becoming an important tool to guide the course of politics and action against Covid-19 pandemic worldwide.
has issue date	2020-06-03(xsd:dateTime)
bibo:doi	10.1101/2020.05.30.20117945
has license	medrxiv
sha1sum (hex)	0ee7d74f1d3ddc16312c0881a6d174ac19bb9b9e
schema:url	https://doi.org/10.1101/2020.05.30.20117945
resource representing a document's title	COVID-SGIS: A smart tool for dynamic monitoring and temporal forecasting of Covid-19
resource representing a document's body	covid:0ee7d74f1d3ddc16312c0881a6d174ac19bb9b9e#body_text
is schema:about of	named entity 'temporal' named entity 'strategies' named entity 'cases' named entity 'process' named entity 'cough' named entity 'coronavirus disease 2019' named entity 'shortness of breath' named entity 'sore throat' named entity 'cough' named entity 'Covid' named entity 'Covid-19' named entity 'virus' named entity 'Midwest Region' named entity 'asymptomatic' named entity 'LSTM' named entity 'preprint' named entity 'May 6' named entity 'CC-BY-ND' named entity 'Mato Grosso do Sul' named entity 'medRxiv' named entity 'medRxiv' named entity 'preprint' named entity 'Covid-19' named entity 'Tocantins' named entity 'Covid-19' named entity 'federative units' named entity 'Italy' named entity 'infection' named entity 'Brazil' named entity 'correlation coefficient' named entity 'CC-BY-ND' named entity 'medRxiv' named entity 'Brazil' named entity 'State of São Paulo' named entity 'preprint' named entity 'chickenpox' named entity 'Polymerase Chain Reaction' named entity 'cancelled or postponed' named entity 'coronavirus' named entity 'CC-BY-ND' named entity 'Covid' named entity '95% confidence interval' named entity 'arboviruses' named entity 'preprint' named entity 'medRxiv' named entity 'preprint' named entity 'medRxiv' named entity 'peer review' named entity 'health professionals' named entity 'preprint' named entity 'virus' named entity 'preprint' named entity 'Rio Grande do Norte' named entity 'Switzerland' named entity 'medRxiv' named entity 'Bahia' named entity 'Iran' named entity 'ARIMA' named entity 'asymptomatic' named entity 'preprint' named entity 'confidence interval' named entity 'CC-BY-ND' named entity 'latent period' named entity 'Maranhão' named entity 'preprint' named entity 'CC-BY-ND' named entity 'preprint'

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