NSF RAPID: Modeling Corona Spread Using Big Data Analytics

新型冠状病毒COVID-19是一种具有严重临床表现的病毒，包括死亡。尽管COVID-19的最终进程和影响尚不确定，但公共卫生工作在很大程度上取决于准确预测COVID-19如何在地球仪传播。在新的疫情爆发期间，当可靠的数据仍然稀缺时，研究人员转向数学模型，可以预测可能被感染的人的去向以及他们携带疾病的可能性。这个过程有时涉及对未知因素的假设，例如旅行模式。然而，通过插入每个输入的不同可能版本，研究人员可以在新信息可用时更新模型，并将其结果与观察到的疾病模式进行比较。在这个项目中，我们建议通过使用创新的大数据分析技术和工具来开发COVID-19传播模型。我们将利用埃博拉病毒传播建模研究的经验，成功地模拟电晕传播。我们希望获得新的结果，这将有助于减少感染a型病毒的患者和相关死亡人数。由于我们的合作伙伴的大型数据库（通过我们与LIGHTNEXIS的合作），我们正在提出“自动”流程，因此我们可以快速识别病毒在社区中的轨迹，以显着降低感染率和死亡人数。拟议的研究活动具有很大的潜力，可以在大数据分析领域以及包括医疗，医疗保健和公共应用在内的不同领域促进知识的发展。我们建议通过使用创新的大数据分析技术和工具来开发COVID传播模型，以了解电晕传播模式，并将其输入公共卫生系统的决策支持系统（DSS）。 根据传播模式，DSS将计算社会群体或地区感染Corona的概率。这些数据将以报告的形式提交给负责的州和政府机构，然后他们将立即采取行动测试和遏制病毒热点。我们将与美国领先的数据分析公司、NSF I/UCRC高级知识支持成员LIGHTNEXIS公司密切合作。Lancet Nexis致力于为我们的计算模型研究提供大量数据，以预测这种疾病的传播，利用正向模拟和感染传播到社区和反向模拟，追踪一些经验证的感染。在许多研究中，数学分区模型已成功地应用于疾病暴发行为的预测。这些模型旨在了解疾病传播过程的动态，并专注于将人口划分为几个健康状态。常见的假设包括：个体数量、感染概率、潜伏期、感染恢复时间等。这些现象学假设限制了模型的范围，同时保留了模型最真实的方面，但由于实际数据不存在，因此需要一些模型维度假设。 因此，在我们的研究中，我们计划使用所提出的新兴技术来加速美国疾病传播知识的积累。 在我们的研究中，我们计划计算与电晕传播相关的各种分数，包括：人口密度排名，家庭死亡风险，街道水平死亡风险和县死亡风险。该项目将帮助建立佛罗里达大西洋大学和LIGHTNEXIS之间的联盟，利用计算机科学、大数据分析、数据可视化技术、该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

The novel coronavirus COVID-19 is a virus with serious clinical manifestations, including death. Although the ultimate course and impact of COVID-19 are uncertain, public health efforts depend heavily on accurately predicting how COVID-19 spreads across the globe. During new outbreaks, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. This process sometimes involves making assumptions about unknown factors, such as travel patterns. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this project we propose developing of a model of COVID-19 spread by using innovative big data analytics techniques and tools. We will leverage experience from research in modeling Ebola spread to successfully model Corona spread. We expect to obtain new results, which will help in reducing the number of infected a patients and related deaths. Because of our partner's large database (through our collaboration with LexisNexis), we are proposing "automatic" process, so we can quickly identify the virus' trajectory in a community to significantly reduce the infection rate and the number of deaths. The proposed research activities have a great potential to advance knowledge within the field of big data analytics as well as across different fields including medical, healthcare, and public applications. We propose to develop a model of COVID spread by using innovative big data analytics techniques and tools to understand Corona spread patterns will be fed into a Decision Support System (DSS) for public health systems. Based on spread patterns, the DSS will then calculate probabilities for a social group or area will get infected with Corona. The data will be presented in the form of reports to responsible state and government agencies, who will then immediately take action of testing and containing virus hotspots. We will closely collaborate with LexisNexis Corporation, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. LexisNexis is committed to provide a large amount of data for our study of computational models to predict the spread of this disease utilizing both, forward simulation and the propagation of the infection into the community and backward simulation, tracing a number of verified infections. Mathematical compartmental models have been successfully applied to predict the behavior of disease outbreaks in many studies. These models aim to understand the dynamics of a disease propagation process and focus on partitioning the population into several health states. Common assumptions can include: number of individuals, infection probability, incubation period, infected recovery time, etc. These phenomenological assumptions limit the scope of the model while preserving the most realistic aspects of it, but some model dimension assumptions are necessary because actual data does not exist. Therefore, in our research we plan to use of the proposed emerging technologies could accelerate the accumulation of knowledge around disease propagation in the United States. In our research we plan to calculate various scores related to Corona spread including: Population density rank, Household mortality risk, Street level mortality risk, and County mortality risk. The project will help build a coalition between Florida Atlantic University and LexisNexis to jointly address public health problems of national and global significance using the state of the art in computer science, big data analytics, data visualization techniques, and decision support systems.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.