In the past few months, several works were published in regards to the dynamics and early detection of COVID-19 via mathematical modeling and Artificial intelligence (AI). The aim of this work is to provide the research community with comprehensive overview of the methods used in these studies as well as a compendium of available open source datasets in regards to COVID-19. In all, 61 journal articles, reports, fact sheets, and websites dealing with COVID-19 were studied and reviewed. It was found that most mathematical modeling done were based on the Susceptible-Exposed-Infected-Removed (SEIR) and Susceptible-infected-recovered (SIR) models while most of the AI implementations were Convolutional Neural Network (CNN) on X-ray and CT images. In terms of available datasets, they include aggregated case reports, medical images, management strategies, healthcare workforce, demography, and mobility during the outbreak. Both Mathematical modeling and AI have both shown to be reliable tools in the fight against this pandemic. Several datasets concerning the COVID-19 have also been collected and shared open source. However, much work is needed to be done in the diversification of the datasets. Other AI and modeling applications in healthcare should be explored in regards to this COVID-19.

在过去的几个月里，通过数学建模和人工智能（AI）发表了几篇关于 COVID-19 的动态和早期检测的著作。这项工作的目的是为研究界提供这些研究中使用的方法的全面概述，以及有关 COVID-19 的可用开源数据集的概要。总共研究和审查了 61 篇有关 COVID-19 的期刊文章、报告、概况介绍和网站。研究发现，大多数数学建模都是基于易感-暴露-感染-移除 (SEIR) 和易感-感染-恢复 (SIR) 模型，而大多数人工智能实现是基于 X 射线和图像的卷积神经网络 (CNN)。 CT 图像。就可用数据集而言，它们包括疫情期间的汇总病例报告、医学图像、管理策略、医护人员、人口统计和流动性。数学模型和人工智能都被证明是对抗这一流行病的可靠工具。有关 COVID-19 的多个数据集也已被收集并开源共享。然而，在数据集的多样化方面还需要做很多工作。应针对这种 COVID-19 探索医疗保健领域的其他人工智能和建模应用。