About: ARIMA forecasting of COVID-19 incidence in Italy, Russia, and the USA

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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	ARIMA forecasting of COVID-19 incidence in Italy, Russia, and the USA
Creator	Perone, Gaetano
Source	ArXiv
abstract	The novel Coronavirus disease (COVID-19) is a severe respiratory infection that officially occurred in Wuhan, China, in December 2019. In late February, the disease began to spread quickly across the world, causing serious health, social, and economic emergencies. This paper aims to forecast the short to medium-term incidence of COVID-19 epidemic through the medium of an autoregressive integrated moving average (ARIMA) model, applied to Italy, Russia, and the USA The analysis is carried out on the number of new daily confirmed COVID-19 cases, collected by Worldometer website. The best ARIMA models are Italy (4,2,4), Russia (1,2,1), and the USA (6,2,3). The results show that: i) ARIMA models are reliable enough when new daily cases begin to stabilize; ii) Italy, the USA, and Russia reached the peak of COVID-19 infections in mid-April, mid-May, and late May, respectively; and iii) Russia and the USA will require much more time than Italy to drop COVID-19 cases near zero. This may suggest the importance of the application of quick and effective lockdown measures, which have been relatively stricter in Italy. Therefore, even if the results should be interpreted with caution, ARIMA models seem to be a good tool that can help the health authorities to monitor the diffusion of the outbreak.
has issue date	2020-05-28(xsd:dateTime)
has license	arxiv
sha1sum (hex)	75b2f34e9b24da2692383e69866d2cec74963013
resource representing a document's title	ARIMA forecasting of COVID-19 incidence in Italy, Russia, and the USA
resource representing a document's body	covid:75b2f34e9b24da2692383e69866d2cec74963013#body_text
is schema:about of	named entity 'number' named entity 'incidence' named entity 'drop' named entity 'good' named entity 'severe' named entity 'COVID-19' named entity 'WEBSITE' named entity 'THE PEAK' named entity 'CASES' named entity 'ACROSS' named entity 'HAVE' named entity 'MONITOR' named entity 'CAUTION' named entity 'PAPER' named entity 'IS A' named entity 'December' named entity 'Russia' named entity 'models' named entity 'models' named entity 'cases' named entity 'USA' named entity 'ARIMA' named entity 'Russia' named entity 'COVID-19' named entity 'reached' named entity 'reliable' named entity 'health' named entity 'The' named entity 'May' named entity 'models' named entity 'forecasting' named entity 'Russia' named entity 'COVID-19 infections' named entity 'ARIMA' named entity 'Italy' named entity 'ARIMA' named entity 'data aggregator' named entity 'time series' named entity 'residuals' named entity 'moving average' named entity 'forecast error' named entity 'AICc' named entity 'PACF' named entity 'Russia' named entity 'China' named entity 'residuals' named entity 'root mean squared error' named entity 'COVID-19 disease' named entity 'ARIMA' named entity 'severe acute respiratory syndrome' named entity 'Russia' named entity 'Italy' named entity 'RMSE' named entity 'ARIMA' named entity 'SEIR' named entity 'Europe' named entity 'randomness' named entity 'residuals' named entity 'ARIMA' named entity 'MAPE' named entity 'MAPE' named entity 'partial autocorrelation function' named entity 'Italy' named entity 'COVID-19' named entity 'India' named entity 'Russia' named entity 'COVID-19' named entity 'ARIMA' named entity 'Italy' named entity 'MASE' named entity 'Turkey' named entity 'time series' named entity 'Russia' named entity 'Spain' named entity 'COVID-2019' named entity 'time series' named entity 'PACF' named entity 'public health' named entity 'autoregressive conditional heteroscedasticity' named entity 'Italy' named entity 'forecast errors' named entity 'MAPE' named entity 'Russia' named entity 'MAPE' named entity 'Hubei Province' named entity 'autoregressive' named entity 'PACF'

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