RAPID: Modeling Ebola Spread and Developing Decision Support System Using Big Data Analytics

该项目旨在通过促进计算机科学、大数据分析、数据可视化技术和决策支持系统的最新发展，帮助解决具有国家和全球意义的紧迫公共卫生问题(特别是埃博拉病毒的传播)。具体地说，这项工作是开发计算模型来预测埃博拉病毒的传播，该模型既利用给定患者的正向模拟，也利用感染向社区和向后的传播，旨在追踪一些已证实的感染病例。这项工作利用了大数据分析技术，关于潜在的个人关系、医疗中心位置的数据，以及埃博拉病毒传播的已知机制。该项目直接连接到佛罗里达国际大学的S地面飞行系统，这是一个网络使能系统，旨在帮助可视化空间和遥感数据。该系统允许用户飞行？利用地球表面的高分辨率，探索各种数据(例如，当地信息、街道地图、航空摄影、卫星图像等)。然后将埃博拉传播模式输入决策支持系统(DDS)。这些信息还包括关于社会团体或个人的信息。根据传播模式，DSS将计算某个社会群体或特定个人感染埃博拉病毒的概率。该系统将能够向响应热线电话的操作员和现场工作人员提供数据混搭，以应对患者并决定是否进行分诊。这些数据还将以报告形式提交给负责的政府机构。这一时间敏感的项目需要迅速收集和分析埃博拉病毒的传播情况，以便能够开发正确的模型。

This project aims to help address urgent public health problems (specifically the spread of the Ebola virus) of national and global significance by advancing the state of the art in computer science, big data analytics, data visualization techniques, and decision support systems. Specifically, the effort, developing computational models to predict the spread of Ebola utilizing both 'forward simulation' from a given patient and the propagation of the infection into the community and backwards, aims to trace a number of the verified infections to patient 'zero.' The work utilizes big data analytic techniques, data about underlying personal relationships, health center locations, and the known mechanisms for the spread of the Ebola virus.The project connects directly to the Florida International University (FIU)?s TerraFly system, a web-enabled system designed to aid in the visualization of spatial and remotely sensed data. The system allows users to ?fly? with fine resolution over the surface of the earth to explore various kinds of data (e.g., local information, street maps, aerial photography, satellite imagery, etc.). The Ebola spread patterns are then fed into a Decision Support System (DDS). These inputs also consist of information about social groups or individual persons. Based on spread patterns, the DSS will then calculate probabilities for a social group or a given person to get infected with Ebola. The system will be able to present data mashups to operators responding to hotline calls and field workers encountering patients and deciding about triage. The data will also be presented in report form to responsible government agencies. This time-sensitive project necessitates prompt collection and analysis of the spread of the Ebola virus in order to enable the development of the correct models.