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 predictions about the events. 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.

Covid-19 是世界有史以来面临的最大健康挑战之一。公共卫生政策制定者需要对未来确诊病例的可靠预测来规划医疗设施。机器学习方法从历史数据中学习并对事件进行预测。机器学习方法已被用于预测 Covid-19 确诊病例的数量。在本文中，我们对这些研究论文进行了详细回顾。我们提出了一个分类法，将它们分为四类。我们进一步介绍了该领域的挑战。我们向机器学习从业者提供建议，以提高机器学习方法预测 Covid-19 确诊病例的性能。