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

By D. Benvenuto, Marta Giovanetti, Lazzaro Vassallo, S. Angeletti, M. Ciccozzi. Data in Brief, 2020

682 citations recorded by the source corpus.

Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/dc67ccfd6433eb34c8cda804a1322ca48f6b7258

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In Electrical and Electronic Engineering

Citations in the corpus

SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain (2020, neutral): “, 2020) studied about Covid-19 real-time forecast in China with generalized logistic growth model (GLM), (Benvenuto et al., 2020) examined the forecast of Covid-19 using ARIMA, and (Koczkodaj et al.”
Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM (2020, neutral): “Another effective time series implementation of support mahine (SVM) anticipated by Vapnik is known as support vector reression [9] .”
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil (2020, methodology): “.63%, and 0.95% - 6.90% for ODA, TDA, and SDA forecasting, respectively. The 8 ranking of models in all scenarios is SVR, stacking ensemble, ARIMA, CUBIST, RIDGE, and RF models. In contrast to nds of [29], for the datasets evaluated in this paper, ARIMA modelling was eective in some situation for ve”
Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing (2020, neutral): “Secondly, models like ARIMA [27] can be integrated with Weibull function for further time series analysis and predictions.”