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An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil

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  • Raydonal Ospina

    (Department of Statistics, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
    Department of Statistics, Universidade Federal da Bahia, Salvador 40170-110, Brazil)

  • João A. M. Gondim

    (Department of Mathematics, Universidade Federal de Pernambuco, Recife 50670-901, Brazil)

  • Víctor Leiva

    (School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Cecilia Castro

    (Centre of Mathematics, Universidade do Minho, 4710-057 Braga, Portugal)

Abstract

This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restrictions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in handling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions.

Suggested Citation

  • Raydonal Ospina & João A. M. Gondim & Víctor Leiva & Cecilia Castro, 2023. "An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil," Mathematics, MDPI, vol. 11(14), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3069-:d:1191831
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    References listed on IDEAS

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    Cited by:

    1. Raydonal Ospina & Jaciele Oliveira & Cristiano Ferraz & André Leite & João Gondim, 2023. "Ensemble Algorithms to Improve COVID-19 Growth Curve Estimates," Stats, MDPI, vol. 6(4), pages 1-18, September.
    2. Md Monjur Hossain Bhuiyan & Ahmed Nazmus Sakib & Syed Ishmam Alawee & Talayeh Razzaghi, 2024. "Fueling the Future: A Comprehensive Analysis and Forecast of Fuel Consumption Trends in U.S. Electricity Generation," Sustainability, MDPI, vol. 16(6), pages 1-30, March.
    3. Ahmed Nazmus Sakib & Talayeh Razzaghi & Md Monjur Hossain Bhuiyan, 2023. "Forecasting the Fuel Consumption and Price for a Future Pandemic Outbreak: A Case Study in the USA under COVID-19," Sustainability, MDPI, vol. 15(17), pages 1-26, August.

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