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

• Published in: PloS one 2022-09, Vol.17 (9), p.e0273319
• Main Authors: Rahman, Md Siddikur, Chowdhury, Arman Hossain
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Computer and Information Sciences; Coronaviruses; COVID-19; COVID-19 - epidemiology; Disease control; Disease transmission; Early warning systems; Earth Sciences; Fatalities; Forecasting; Generalized linear models; Humans; Humidity; Machine Learning; Maximum temperatures; Maximum winds; Medicine and Health Sciences; Meteorological Concepts; Meteorology; Modelling; Pandemics; People and places; Physical Sciences; Precipitation; Pressure; Public health; Relative humidity; Research and Analysis Methods; Respiratory diseases; Risk analysis; Risk factors; Severe acute respiratory syndrome coronavirus 2; Surface pressure; Time series; Training; Variables; Wind speed
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0273319

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.