RAPID: COVID-19 Coronavirus Testbed and Knowledge Base Construction and Personalized Risk Evaluation

2019年新型冠状病毒病(新冠肺炎)是一种进化中的流行病。对于新冠肺炎的S疫情和传播模式，以及病毒演变、人口统计学、社会行为、文化差异和检疫政策对疫情的影响，人们知之甚少。随着与新冠肺炎的战斗仍在继续，信息洪流正在产生。学术界、新闻机构和政府不断发表对病毒临床病理、其基因组序列以及相关行政政策和采取的行动的了解进展。然而，在不同的地理位置、区域政策和文化群体方面，疫情暴发的巨大差异也引起了疾病暴发建模中的困惑、矛盾和不一致。因此，至关重要的是建立新冠肺炎的知识库，以了解不同因素在预测病毒传播中发挥的相互关系和作用，从而使个人和卫生保健官员能够实施适当的政策，以减轻疫情对公共卫生和整个社会的影响。该项目将创建一个新冠肺炎冠状病毒试验床和知识库，以及一个个性化的风险评估工具，供个人在动态环境中评估自己的感染风险。该项目的技术目标包括两个方面。第一个创建了一个试验床和知识库，其中包括用于模拟新冠肺炎爆发和突变的信息。这张试验床将作为公众建模和了解新冠肺炎传播的基准，最终缓解新冠肺炎对公众健康、社会和经济的负面影响。第二个重点是开发一种基于多源深度神经网络的预测工具，将人口统计数据、政策、地区感染和个人信息结合起来，进行个性化风险评估。因此，公众可以使用个性化信息来估计他们的感染风险水平，使用社会和行为信息(例如，家庭规模、购物模式和就餐模式)、地方当局政策(例如，学校、餐馆和电影院关闭以及夜间宵禁)、人口统计数据(例如，人口年龄、密度和收入)、健康状况(例如，心脏病发病率、癌症患病率和药物滥用)以及区域病毒状况(例如，这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The 2019 novel coronavirus disease (COVID-19) is an evolving epidemic. There is little knowledge about COVID-19’s outbreak and spread patterns, and the impact of viral evolution, demography, social behavior, cultural differences, and quarantine policies on the outbreaks. As the battle against COVID-19 continues, a deluge of information is being produced. Academia, news agencies, and governments continuously publish advances in the understanding of the virus clinical pathologies, its genome sequences, and relevant administrative policies and actions taken. Nevertheless, the dramatic outbreak differences with respect to diverse geographies, regional policies, and cultural groups also raise confusion, contradictions, and inconsistencies in disease outbreak modeling. It is therefore crucial to build a knowledge base of COVID-19 to understand the correlations and roles that different factors play in predicting the spread of the virus, thus enabling both individuals and health care officials to implement appropriate policies to mitigate the effects of the epidemic on public health and society at large. This project will create a COVID-19 coronavirus testbed and knowledge base, as well as a personalized risk evaluation tool for individuals to assess their infection risk in a dynamic environment. The technical aims of the project include two thrusts. The first creates a testbed and knowledgebase that includes information for modeling outbreak and mutation of COVID-19. This testbed will serve as a benchmark for the public to model and understand the spread of COVID-19, and eventually mitigate the negative effects of COVID-19 on public health, society, and the economy. The second thrust develops a multi-source deep neural network-based predictive tool to combine demographics, policies, regional infections, and individual information for personalized risk evaluation. As a result, the public can employ personalized information to estimate their infection risk level, using social and behavioral information (e.g., family size, shopping patterns, and dining patterns), local authority policies (e.g., school, restaurant, and movie theater closures as well as night time curfew), demographics (e.g., population age, density, and income), health condition (e.g., heart disease incidence, cancer prevalence, and substance abuse), and regional virus condition (e.g., number of infection cases in the region studied and infection rate).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.