Analysis of Window Sizes in Prediction of Daily COVID-۱۹ Cases using Machine Learning Models

The first case in Turkey of the COVID-۱۹ pandemic was reported on ۱۱ March ۲۰۲۰, with the number of confirmed cases rapidly rising to over ۶۰.۰۰۰ by April ۲۰۲۱. In the absence of effective treatment, an important tool of outbreak management is the modeling and predicting of the COVID-۱۹ pandemic’s future and behavior. The present study considered machine learning (ML) models to predict the daily confirmed cases of the COVID-۱۹ outbreak in Turkey. The daily confirmed cases of COVID-۱۹ data from November ۲۵, ۲۰۲۰, to June ۱۳, ۲۰۲۱, were obtained from the website of the Republic of Turkey, Ministry of Health. The predicted values were explored with a range of window sizes from ۲ to ۱۴ and four ML models: Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (k-NN), eXtreme Gradient Boosting (XGBoost). The results show that for the test dataset, the performance of window size ۶ is better than the other window sizes for the SVM and k-NN models. For the RF and XGBoost models, the performance of window sizes ۸ and ۱۰ is better than the other window sizes, respectively.

Keywords:

COVID-۱۹ , SVM , RF , k-NN , XGBoost , Turkey