Application of Neural Network Time Series (NNAR) and ARIMA to Forecast Infection Fatality Rate (IFR) of COVID-19 in Brazil

Ansari Saleh Ahmar - Department of Statistics, Universitas Negeri Makassar, Makassar, 90223, Indonesia
Eva Boj - Department of Economic, Financial and Actuarial Mathematics, Faculty of Economics and Business, Universitat de Barcelona, Spain

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Forecasting is a method that is often used to view future events using past time data. Past time data have useful information to use in obtaining the future. The aim of this study was to forecast infection fatality rate (IFR) of COVID-19 in Brazil using NNAR and ARIMA. ARIMA and NNAR are used because (1) ARIMA is a simple stochastic time series method that can be used to train and predict future time points and ARIMA also capable of capturing dynamic interactions when it uses error terms and observations of lagged terms; (2) the Artificial Neural Network (ANN) is a technique capable of analyzing certain non-linear interactions between input regressor and responses, and Neural Network Time Series (NNAR) is one method of ANN in which lagged time series values were used as inputs to a neural network. Data included in this study were derived from the total data of confirmed cases and the total data of death of COVID-19. The data of COVID-19 in Brazil from February 15, 2020 to April 30, 2020 were collected from the Worldometer ( and Microsoft Excel 2013 was used to build a time-series table. Forecasting was accomplished by means of a time series package (forecast package) in R Software.  Neural Network Time Series and ARIMA models were applied to a dataset consisting of 76 days. The accuracy of forecasting was examined by means of an MSE. The forecast of IFR of COVID-19 in Brazil from May 01, 2020 to May 10, 2020 with NNAR (1,1) model was around in 6,85% and ARIMA (0,2,1) was around in 7.11%.


Infection disease; COVID-19; forecasting; NNAR; ARIMA.

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WHO, “Situation report - 77 Coronavirus disease 2019 (COVID-19,” 2020. [Online]. Available: [Accessed: 07-Apr-2020].

Worldometer, “Spain Coronavirus,” 2020. [Online]. Available: [Accessed: 08-Apr-2020].

A. S. Ahmar and E. Boj, “SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain,” Sci. Total Environ., p. 138883, Apr. 2020.

A. S. Ahmar and E. Boj, “The date predicted 200.000 cases of COVID-19 in Spain,” J. Appl. Sci. Eng. Technol. Educ., vol. 2, no. 2, Jun. 2020.

A. S. Ahmar and E. Boj, “Will COVID-19 confirmed cases in the USA reach 3 million? A forecasting approach by using SutteARIMA Method,” Curr. Res. Behav. Sci., vol. 1, p. 100002, 2020.

A. S. Ahmar and R. Rusli, “Will COVID-19 cases in the World reach 4 million? a forecasting approach using SutteARIMA,” JOIV Int. J. Informatics Vis., vol. 4, no. 3, p. 159, 2020.

E. Yeboah and R. G. Kyeremeh, “COVID-19 in Ghana: An Overview of Government Contributions in Fighting this Pandemic,” Quant. Econ. Manag. Stud., vol. 1, no. 6, pp. 375–382, 2020.

S. R. Saha and D. M. M. H. Khan, “Prevalence and determinants of mental distress during COVID-19 outbreak in Bangladesh: evidence from an online survey,” J. Appl. Sci. Eng. Technol. Educ., vol. 3, no. 1, pp. 90–103, 2021.

R. E. Grewelle and G. A. De Leo, “Estimating the Global Infection Fatality Rate of COVID-19,” medRxiv, p. 2020.05.11.20098780, Jan. 2020.

R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2018.

J. P. A. Ioannidis, “Infection fatality rate of COVID-19 inferred from seroprevalence data,” Bull. World Health Organ., vol. 99, no. 1, pp. 19-33F, Jan. 2021.

A. S. Ahmar, “Forecast Error Calculation with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE),” JINAV J. Inf. Vis., vol. 1, no. 2, 2020.


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JOIV : International Journal on Informatics Visualization
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