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A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers

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  • Md Siddikur Rahman
  • Arman Hossain Chowdhury

Abstract

COVID-19 pandemic has become a global major public health concern. Examining the meteorological risk factors and accurately predicting the incidence of the COVID-19 pandemic is an extremely important challenge. Therefore, in this study, we analyzed the relationship between meteorological factors and COVID-19 transmission in SAARC countries. We also compared the predictive accuracy of Autoregressive Integrated Moving Average (ARIMAX) and eXtreme Gradient Boosting (XGBoost) methods for precise modelling of COVID-19 incidence. We compiled a daily dataset including confirmed COVID-19 case counts, minimum and maximum temperature (°C), relative humidity (%), surface pressure (kPa), precipitation (mm/day) and maximum wind speed (m/s) from the onset of the disease to January 29, 2022, in each country. The data were divided into training and test sets. The training data were used to fit ARIMAX model for examining significant meteorological risk factors. All significant factors were then used as covariates in ARIMAX and XGBoost models to predict the COVID-19 confirmed cases. We found that maximum temperature had a positive impact on the COVID-19 transmission in Afghanistan (β = 11.91, 95% CI: 4.77, 19.05) and India (β = 0.18, 95% CI: 0.01, 0.35). Surface pressure had a positive influence in Pakistan (β = 25.77, 95% CI: 7.85, 43.69) and Sri Lanka (β = 411.63, 95% CI: 49.04, 774.23). We also found that the XGBoost model can help improve prediction of COVID-19 cases in SAARC countries over the ARIMAX model. The study findings will help the scientific communities and policymakers to establish a more accurate early warning system to control the spread of the pandemic.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0273319
    DOI: 10.1371/journal.pone.0273319
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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Fan Wu & Su Zhao & Bin Yu & Yan-Mei Chen & Wen Wang & Zhi-Gang Song & Yi Hu & Zhao-Wu Tao & Jun-Hua Tian & Yuan-Yuan Pei & Ming-Li Yuan & Yu-Ling Zhang & Fa-Hui Dai & Yi Liu & Qi-Min Wang & Jiao-Jiao , 2020. "Author Correction: A new coronavirus associated with human respiratory disease in China," Nature, Nature, vol. 580(7803), pages 7-7, April.
    3. 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.
    4. 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.
    5. Fan Wu & Su Zhao & Bin Yu & Yan-Mei Chen & Wen Wang & Zhi-Gang Song & Yi Hu & Zhao-Wu Tao & Jun-Hua Tian & Yuan-Yuan Pei & Ming-Li Yuan & Yu-Ling Zhang & Fa-Hui Dai & Yi Liu & Qi-Min Wang & Jiao-Jiao , 2020. "A new coronavirus associated with human respiratory disease in China," Nature, Nature, vol. 579(7798), pages 265-269, March.
    6. Long Yan & Hong Wang & Xuan Zhang & Ming-Yue Li & Juan He, 2017. "Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012)," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-13, August.
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    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.

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