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A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models.
Yasminah Alali1, Fouzi Harrou2, Ying Sun1
1Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Scientific Reports
|February 15, 2022
Summary
This study introduces a dynamic machine learning model for accurate COVID-19 forecasting. The dynamic Gaussian process regression (GPR) model significantly improved prediction accuracy for confirmed and recovered cases.
Area of Science:
- Epidemiology
- Data Science
- Machine Learning
Background:
- Accurate forecasting of COVID-19 spread is crucial for public health interventions.
- Traditional machine learning models often neglect temporal dependencies in epidemiological data.
Purpose of the Study:
- To develop an assumption-free, data-driven model for precise COVID-19 spread prediction.
- To enhance machine learning models by incorporating dynamic information for improved forecasting accuracy.
Main Methods:
- Utilized Bayesian optimization to tune Gaussian process regression (GPR) hyperparameters.
- Developed dynamic machine learning models by including lagged measurements to account for time dependency.
- Assessed feature importance using the Random Forest algorithm.
Main Results:
- Dynamic machine learning models demonstrated significant improvements in forecasting COVID-19 cases.
- The dynamic GPR model outperformed other models, achieving a mean absolute percentage error of approximately 0.1%.
- Predictions from the dynamic GPR model were validated within a 95% confidence interval.
Conclusions:
- The proposed dynamic machine learning approach offers a promising and simple method for predicting COVID-19 transmission.
- Dynamic GPR provides reliable forecasts with quantifiable confidence levels.
- Incorporating temporal dynamics enhances the predictive power of machine learning models for epidemiological data.
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