The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges
It provides a taxonomy and suggestions for practitioners to improve predictions, but is incremental as it only reviews existing work.
The paper reviews existing machine learning methods for predicting COVID-19 confirmed cases, categorizing them into four groups and identifying challenges, but does not present new results or concrete numbers.
Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make a prediction about the event. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.