COVID 19 Peak Time Prediction via a Gradient Boosting Method

Abstract

The outbreak of COVID-19 has caught humanity off guard. Peak-times differ in countries based on their characteristics and precautions taken by governments. In this study, we aimed to determine relative importance of indicators on the spread and to assist non-peaked countries to estimate their peak-times. Gradient Boosting Method was employed on 82 countries which reached peak-times. The findings indicate that hospital beds per thousand is the main predictor of peak-time estimation. Restrictions on gatherings and closing public transportation have the highest relative importance among governmental precautions. This model can be utilized and employed with various indices and alternative machine-learning algorithms.