A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models.
Sci Rep
; 12(1): 2467, 2022 02 14.
Article
in English
| MEDLINE | ID: covidwho-1684106
ABSTRACT
This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and confirmed COVID-19 cases in two highly impacted countries, India and Brazil. However, machine learning models do not consider the time dependency in the COVID-19 data series. Here, dynamic information has been taken into account to alleviate this limitation by introducing lagged measurements in constructing the investigated machine learning models. Additionally, we assessed the contribution of the incorporated features to the COVID-19 prediction using the Random Forest algorithm. Results reveal that significant improvement can be obtained using the proposed dynamic machine learning models. In addition, the results highlighted the superior performance of the dynamic GPR compared to the other models (i.e., Support vector regression, Boosted trees, Bagged trees, Decision tree, Random Forest, and XGBoost) by achieving an averaged mean absolute percentage error of around 0.1%. Finally, we provided the confidence level of the predicted results based on the dynamic GPR model and showed that the predictions are within the 95% confidence interval. This study presents a promising shallow and simple approach for predicting COVID-19 spread.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Algorithms
/
Neural Networks, Computer
/
Machine Learning
/
Forecasting
/
COVID-19
Type of study:
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Country/Region as subject:
South America
/
Asia
/
Brazil
Language:
English
Journal:
Sci Rep
Year:
2022
Document Type:
Article
Affiliation country:
S41598-022-06218-3
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