To tackle the COVID-19 pandemic, massive efforts have been made in modeling COVID-19 transmission, diagnoses, interventions, pathological and influence analysis, etc. With the most comprehensive repository on COVID-19 research - some 160k WHO-collected global literature on COVID-19 produced since 2020, some critical question to ask include: What are the COVID-19 challenges? How do they address the challenges? Where are the significant gaps and opportunities in COVID-19 modeling?. Accordingly, informed by their statistics and a deep keyword-based similarity analysis of those references on COVID-19 modeling, this is the first to systemically summarize the disease, data and modeling challenges and the corresponding modeling progress and gaps. We come up with a transdisciplinary research landscape to summarize and match the business goals and tasks and their learning methods of COVID-19 modeling.
翻译：为了应对COVID-19大流行,在建立COVID-19传播、诊断、干预、病理学和影响分析等模型方面作出了巨大努力。由于自2020年以来编制了COVID-19研究最全面的储存库 -- -- 世卫组织收集的大约160k份关于COVID-19的全球文献,因此,需要问的一些关键问题包括:COVID-19的挑战是什么?它们如何应对挑战?COVID-19建模中的重大差距和机会在哪里?因此,根据它们的统计资料和关于COVID-19建模中这些参考资料的深层关键词相似性分析,这是首次系统总结疾病、数据和建模方面的挑战以及相应的建模进展和差距。我们提出了跨学科研究概况,以总结和匹配COVID-19建模中的业务目标和任务及其学习方法。
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