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Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh

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  • Md Siddikur Rahman
  • Arman Hossain Chowdhury
  • Miftahuzzannat Amrin

Abstract

Accurate predictive time series modelling is important in public health planning and response during the emergence of a novel pandemic. Therefore, the aims of the study are three-fold: (a) to model the overall trend of COVID-19 confirmed cases and deaths in Bangladesh; (b) to generate a short-term forecast of 8 weeks of COVID-19 cases and deaths; (c) to compare the predictive accuracy of the Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost) for precise modelling of non-linear features and seasonal trends of the time series. The data were collected from the onset of the epidemic in Bangladesh from the Directorate General of Health Service (DGHS) and Institute of Epidemiology, Disease Control and Research (IEDCR). The daily confirmed cases and deaths of COVID-19 of 633 days in Bangladesh were divided into several training and test sets. The ARIMA and XGBoost models were established using those training data, and the test sets were used to evaluate each model’s ability to forecast and finally averaged all the predictive performances to choose the best model. The predictive accuracy of the models was assessed using the mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The findings reveal the existence of a nonlinear trend and weekly seasonality in the dataset. The average error measures of the ARIMA model for both COVID-19 confirmed cases and deaths were lower than XGBoost model. Hence, in our study, the ARIMA model performed better than the XGBoost model in predicting COVID-19 confirmed cases and deaths in Bangladesh. The suggested prediction model might play a critical role in estimating the spread of a novel pandemic in Bangladesh and similar countries.

Suggested Citation

  • Md Siddikur Rahman & Arman Hossain Chowdhury & Miftahuzzannat Amrin, 2022. "Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh," PLOS Global Public Health, Public Library of Science, vol. 2(5), pages 1-13, May.
    • Handle: RePEc:plo:pgph00:0000495
    DOI: 10.1371/journal.pgph.0000495
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    1. Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Mizuho Nishio & Mitsuo Nishizawa & Osamu Sugiyama & Ryosuke Kojima & Masahiro Yakami & Tomohiro Kuroda & Kaori Togashi, 2018. "Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-13, April.
    3. Rosselló, Jaume & Sansó, Andreu, 2017. "Yearly, monthly and weekly seasonality of tourism demand: A decomposition analysis," Tourism Management, Elsevier, vol. 60(C), pages 379-389.
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    Cited by:

    1. Mst Noorunnahar & Arman Hossain Chowdhury & Farhana Arefeen Mila, 2023. "A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-15, March.
    2. Md Siddikur Rahman & Arman Hossain Chowdhury, 2022. "A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-14, September.

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