About: Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
Creator	Cocco Mariani, Viviana Coelho, Santos Dal, Molin Dos, Leandro Gomes Da Silva, Ramon Matheus, Henrique Ribeiro,
Source	ArXiv; Elsevier; Medline; PMC
abstract	The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models’ effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87%–3.51%, 1.02%–5.63%, and 0.95%–6.90% in one, three, and six-days-ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.
has issue date	2020-05-01(xsd:dateTime)
bibo:doi	10.1016/j.chaos.2020.109853
bibo:pmid	32501370
has license	no-cc
sha1sum (hex)	7c67e1970fe5d46a467c2bbb48a3c6680603f85e
schema:url	https://doi.org/10.1016/j.chaos.2020.109853
resource representing a document's title	Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
has PubMed Central identifier	PMC7252162
has PubMed identifier	32501370
schema:publication	Chaos Solitons Fractals
resource representing a document's body	covid:7c67e1970fe5d46a467c2bbb48a3c6680603f85e#body_text
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