The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming challenges and opportunities to data and domain-driven modeling. This paper makes a comprehensive review of the challenges, tasks, methods, gaps and opportunities on modeling COVID-19 problems and data. It constructs a research landscape of COVID-19 modeling, and further categorizes, compares and discusses the related work on modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and trends, medical treatments, non-pharmaceutical intervention effect, drug and vaccine development, psychological, economic and social impact, and misinformation, etc. The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, data-driven learning by shallow and deep machine learning, simulation systems, social science methods, and hybrid methods.
翻译：SARS-COV-2病毒和COVID-19疾病对数据和域驱动模型提出了前所未有的、巨大的挑战和机遇,本文件全面审查了COVID-19问题和数据建模方面的挑战、任务、方法、差距和机会,构筑COVID-19建模的研究景观,并进一步对COVID-19流行病传播过程和动态进行分类、比较和讨论建模COVID-19流行病传播过程和动态、案例识别和追踪、感染诊断和趋势、医疗治疗、非药物干预效应、药物和疫苗开发、心理、经济和社会影响以及错误信息等方面的相关工作。 建模方法包括数学和统计模型、流行病学区际模型的域建模、医疗和生物医学分析、浅深层机器学习驱动的数据学习、模拟系统、社会科学方法和混合方法。
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