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A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological
Md Siddikur Rahman1, Arman Hossain Chowdhury1
1Department of Statistics, Begum Rokeya University, Rangpur, Rangpur, Bangladesh.
Plos One
|September 13, 2022
Summary
Meteorological factors like temperature and surface pressure impact COVID-19 spread in SAARC nations. The eXtreme Gradient Boosting (XGBoost) model offers superior prediction accuracy for COVID-19 incidence compared to Autoregressive Integrated Moving Average (ARIMAX).
Area of Science:
- Environmental Science
- Epidemiology
- Data Science
Background:
- The COVID-19 pandemic poses a significant global public health challenge.
- Understanding meteorological risk factors is crucial for predicting disease incidence.
- Accurate forecasting models are needed to manage the pandemic effectively.
Purpose of the Study:
- To analyze the relationship between meteorological factors and COVID-19 transmission in SAARC countries.
- To compare the predictive performance of Autoregressive Integrated Moving Average (ARIMAX) and eXtreme Gradient Boosting (XGBoost) models for COVID-19 incidence.
- To identify key meteorological variables influencing COVID-19 spread.
Main Methods:
- A daily dataset of COVID-19 cases and meteorological data (temperature, humidity, pressure, precipitation, wind speed) was compiled for SAARC countries until January 29, 2022.
- The Autoregressive Integrated Moving Average (ARIMAX) model was used to identify significant meteorological risk factors.
- ARIMAX and eXtreme Gradient Boosting (XGBoost) models were trained and tested to predict COVID-19 confirmed cases using significant factors as covariates.
Main Results:
- Maximum temperature positively impacted COVID-19 transmission in Afghanistan and India.
- Surface pressure showed a positive influence on transmission in Pakistan and Sri Lanka.
- The XGBoost model demonstrated improved prediction accuracy for COVID-19 cases in SAARC countries compared to the ARIMAX model.
Conclusions:
- Meteorological factors are significant predictors of COVID-19 transmission in SAARC nations.
- The XGBoost model provides a more accurate approach for forecasting COVID-19 incidence.
- Findings can aid in developing robust early warning systems for pandemic control.
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