About: Short-term forecasting of daily COVID-19 cases in Brazil by using the Holt’s model

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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	Short-term forecasting of daily COVID-19 cases in Brazil by using the Holt’s model
Creator	Nunes, Altacílio Aragon, Davi, Casale Zangiacomi Martinez, Edson Zangiacomi, Edson
Source	Medline; PMC
abstract	INTRODUCTION: We evaluated the performance of the Holt’s model to forecast the daily COVID-19 reported cases in Brazil and three Brazilian states. METHODS: We chose the date of the first COVID-19 case to April 25, 2020, as the training period, and April 26 to May 3, 2020, as the test period. RESULTS: The Holt’s model performed well in forecasting the cases in Brazil and in São Paulo and Minas Gerais states, but the forecasts were underestimated in Rio de Janeiro state. CONCLUSIONS: The Holt’s model can be an adequate short-term forecasting method if their assumptions are adequately verified and validated by experts.
has issue date	2020-06-03(xsd:dateTime)
bibo:doi	10.1590/0037-8682-0283-2020
bibo:pmid	32520235
has license	cc-by
sha1sum (hex)	cb811b54ba88db1f50da28207820ce7f4c04ec7a
schema:url	https://doi.org/10.1590/0037-8682-0283-2020
resource representing a document's title	Short-term forecasting of daily COVID-19 cases in Brazil by using the Holt’s model
has PubMed Central identifier	PMC7269522
has PubMed identifier	32520235
schema:publication	Revista da Sociedade Brasileira de Medicina Tropical
resource representing a document's body	covid:cb811b54ba88db1f50da28207820ce7f4c04ec7a#body_text
is schema:about of	named entity 'cases' named entity 'Tropical' named entity 'Holt' named entity 'Methods' named entity 'Brazil' named entity 'forecasting' named entity 'May 3' named entity 'test' named entity 'training period' named entity 'Brazil' named entity 'Minas Gerais' named entity 'COVID' named entity 'Rio de Janeiro' named entity 'São Paulo' named entity 'Wuhan' named entity 'COVID' named entity 'Rio de Janeiro' named entity 'cough' named entity 'Hubei Province, China' named entity 'Minas Gerais' named entity 'São Paulo' named entity 'log-normal' named entity '80 million' named entity 'exponential smoothing' named entity 'virus' named entity 'Saffo' named entity 'Rio de Janeiro' named entity 'Brazil' named entity 'long-term' named entity 'Brazil' named entity 'Minas Gerais' named entity 'epidemic curve' named entity 'epidemic' named entity 'fever' named entity 'Minas Gerais' named entity 'COVID' named entity 'COVID-19' named entity 'time series' named entity 'double exponential smoothing' named entity 'verified and validated' named entity 'epidemic curve' named entity 'decision-making' named entity 'World Health Organization' named entity 'COVID-19' named entity 'Brazil' named entity 'time series' named entity 'exponential smoothing' named entity 'Rio de Janeiro' named entity 'log-normal' named entity 'COVID' named entity 'public-health' named entity 'grows exponentially' named entity 'Minas Gerais' named entity 'Brazil' named entity 'percent error' named entity 'WHO' named entity 'infectious diseases' named entity 'COVID' named entity 'Rio de Janeiro' named entity 'epidemic' named entity 'São Paulo' named entity 'COVID' named entity 'COVID' named entity 'moving average' named entity 'pathogen' named entity 'MAPE' named entity 'incubation period' named entity 'double exponential smoothing' named entity 'statistical model' named entity 'COVID-19' named entity 'Brazil' named entity 'COVID' named entity 'interquartile range' named entity 'SARS-CoV-2' named entity 'Brazil' named entity 'fatigue' named entity 'São Paulo'

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