Application of the ARIMA model on the COVID-2019 epidemic dataset

Benvenuto, Domenico; Giovanetti, Marta; Vassallo, Lazzaro; Angeletti, Silvia; Ciccozzi, Massimo

doi:10.1016/j.dib.2020.105340

Cited by 614 publications

(485 citation statements)

References 5 publications

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“…9 Seasonal and nonseasonal differences were used to stationary the term trend and periodicity. 10 The non-seasonal ARIMA model is usually denoted as ARIMA ( , , ), in which is the order of the autoregression (AR) component, is the order of the differencing process to form a stationary times series, and is the order of the moving average (MA) process. 11 In an ARIMA model, the value of at time is estimated as equation (1)…”

Section: Methodsmentioning

confidence: 99%

Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

Bayyurt

2020

Preprint

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After the outbreak of severe acute respiratory syndrome (SARS-2002/2003) and middle east respiratory syndrome (MERS-2012/2014) in the world, new public health crisis, called new coronavirus disease (COVID-19), started in China in December 2019 and has spread all over countries. COVID-19 coronavirus has been global threat of the disease and infected humans rapidly. Control of the pandemi is urgently essential, and science community have continued to research treatment agents. Support therapy and intensive care units in hospitals are also efective to overcome of COVID-19. Statistic forecasting models could aid to healthcare system in preventation of COVID-19. This study aimed to compose of forecasting model that could be practical to predict the spread of COVID-19 in Italy, Spain and Turkey. For this purpose, we performed Auto Regressive Integrated Moving Average (ARIMA) model on the European Centre for Disease Prevention and Control COVID-19 data to predict the number of cases and deaths in COVID-19. According to the our results, while number of cases in Italy and Spain is expected to decrease as of July, in Turkey is expected to decline as of September. The number of deaths in Italy and Spain is expected to be the lowest in July. In Turkey, this number is expected to reach the highest in July. In addition, it is thought that if studies in which the sensitivity and validity of this method are tested with more cases, they will contribute to researchers working in this field. KEYWORDS: COVID-19, pandemi, ARIMA, time series analysis

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Section: Methodsmentioning

confidence: 99%

Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

Bayyurt

2020

Preprint

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“…A time series is simply expressed as a set of data points ordered in time (Fanoodi et al, 2019). Time series analysis aims to reveal reliable and meaningful statistics and use this knowledge to predict future values of the series (Liu et al, 2011;Elevli et al, 2016;He and Tao, 2018;Benvenuto et al, 2020). The ARIMA model was introduced by Box and Jenkins in the 1970s (Box et al, 2015).…”

Section: Arima Modelsmentioning

confidence: 99%

Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan

2020

Science of The Total Environment

525

453

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Europe has become the epicentre of the virus and hit the continent harder than China.• The apparent mortality rate of COVID-19 is approximately 13% in Italy, 11% in Spain, and 15% in France. • Time series models are significant in predicting the prevalence of the COVID-19 pandemic. • ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) were chosen as the best models for Italy, Spain, and France, respectively. were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.

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“…To view these changes from time to time, estimates of the data need to be done. Forecasting or predictions related to Covid-19 have been studied by various researchers: (Fanelli and Piazza, 2020) studying the forecasting of the spread of covid-19 in China, Italy, and France using the SIRD model, (Roosa et al, 2020) studying about Covid-19 real-time forecast in China with generalized logistic growth model (GLM), (Benvenuto et al, 2020) examines the forecast of Covid-19 using ARIMA, and (Koczkodaj et al, 2020) predicts Covid-19 outside of China by using a simple heuristic (exponential curve). Zt  define stationarity (or weak stationarity) as follows (Brockwell and Davis, 2016;Montgomery et al, 2015) :…”

Section: Introductionmentioning

confidence: 99%

SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain

Ahmar

Val

2020

Science of The Total Environment

158

134

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The purpose of our research is to predict the short-term of confirmed cases of in Spain by using SutteARIMA method. Covid-19 Spanish confirmed data obtained from Worldometer and Spain Stock Market data (IBEX 35) data obtained from Yahoo Finance. Data starts from 12 IBEX). In this study, the SutteARIMA method will be used. For the evaluation of the forecasting methods we applied forecasting accuracy measures, mean absolute percentage error (MAPE). Based on the results of ARIMA and SutteARIMA forecasting methods, we conclude that the SutteARIMA method is most suitable than ARIMA to calculate the daily forecasts of confirmed cases of Covid-19 and IBEX in Spain. The MAPE value of 0.1905 (smaller than 0.04 compared to MAPE value of ARIMA) for confirmed cases of Covid-19 in Spain and 0,0202 for IBEX stock. At the end of the analysis, using the SutteARIMA method, we calculate daily forecasts of confirmed cases of Covid-

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Application of the ARIMA model on the COVID-2019 epidemic dataset

Cited by 614 publications

References 5 publications

Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

Estimation of COVID-19 prevalence in Italy, Spain, and France

SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain

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