The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

TL;DR

This paper reviews machine learning approaches for predicting Covid-19 confirmed cases, categorizes existing research, discusses challenges, and offers suggestions to improve prediction accuracy for public health planning.

Contribution

It provides a comprehensive taxonomy of machine learning methods used for Covid-19 case prediction and discusses key challenges and improvement strategies.

Findings

Machine learning methods are effective for Covid-19 case prediction.

Challenges include data quality and model generalization.

Recommendations for practitioners to enhance prediction performance.

Abstract

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.