Collaborative Research: RAPID: RTEM: Rapid Testing as Multi-fidelity Data Collection for Epidemic Modeling

合作研究：RAPID：RTEM：快速测试作为流行病建模的多保真度数据收集

• 批准号: 2026860
• 负责人: Giulia Pedrielli
• 金额: $ 12.3万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026860&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; RTEM; Rapid

The novel coronavirus (COVID-19) epidemic is generating significant social, economic, and health impacts and has highlighted the importance of real-time analysis of the spatio-temporal dynamics of emerging infectious diseases. COVID-19, which emerged out of the city of Wuhan in China in December 2019 is now spreading in multiple countries. It is particularly concerning that the case fatality rate appears to be higher for the novel coronavirus than for seasonal influenza, and especially so for older populations and those with prior health conditions such as cardiovascular disease and diabetes. Any plan for stopping the epidemic must be based on a quantitative understanding of the proportion of the at-risk population that needs to be protected by effective control measures in order for transmission to decline sufficiently and quickly enough for the epidemic to end. Different data collection and testing modalities and strategies available to help calibrate transmission models and predict the spread/severity of a disease, have variable costs, response times, and accuracies. In this Rapid Response Research (RAPID) project, the team will examine the problem of establishing optimal practices for rapid testing for the novel coronavirus. The result will be the Rapid Testing for Epidemic Modeling (RTEM), which will translate into science-based predictions of the COVID-19 epidemic's characteristics, including the duration and overall size, and help the global efforts to combat the disease. The RTEM will fill an important gap in data-driven decision making during the COVID-19 epidemic and, thus, will enable services with significant national economic and health impact. The educational impact of the project will be on mentoring of post-doctoral and PhD researchers and on curricula by incorporating research challenges and outcomes into existing undergraduate and graduate classes. Computational models for the spatio-temporal dynamics of emerging infectious diseases and data- and model-driven computer simulations for disease spreading are increasingly critical in predicting geo-temporal evolution of epidemics as well as designing, activating, and adapting practices for controlling epidemics. In this project, the researchers tackle a Rapid Testing for Epidemic Modeling (RTEM) problem: Given a partially known target disease model and a set of testing modalities (from surveys to surveillance testing at known disease hotspots), with varying costs, accuracies, and observational delays, what is the best rapid testing strategy that would help recover the underlying disease model? Several scientific questions arise: What is the value of testing? Should only sick people be tested for virus detection? What level of resources should be devoted to the development of highly accurate tests (low false positives, low false negatives)? Is it better to use only one type of test aiming at the best cost/effectiveness trade off, or a non-homogeneous testing policy? Naturally these questions need to be investigated at the interface of epidemiology, computer science, machine learning, mathematical modeling and statistics. As part of the work, the team will develop a model of transmission dynamics and control, tailored to COVID-19 in a way that accommodates diagnostic testing with varying fidelities and delays underlying a rapid testing regimen. The investigators will further integrate the resulting RTEM-SEIR model with EpiDMS and DataStorm for executing continuous coupled simulations.This project is jointly funded through the Ecology and Evolution of Infectious Diseases program (Division of Environmental Biology) and the Civil, Mechanical and Manufacturing Innovation program (Engineering).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.

新型冠状病毒（COVID-19）流行正在产生重大的社会、经济和健康影响，并突出了实时分析新发传染病时空动态的重要性。2019年12月在中国武汉市出现的新冠肺炎疫情目前正在多个国家蔓延。尤其令人担忧的是，新型冠状病毒的病死率似乎高于季节性流感，对于老年人群和既往有心血管疾病和糖尿病等健康问题的人群尤其如此。任何阻止这一流行病的计划都必须基于对需要得到有效控制措施保护的风险人口比例的定量了解，以便使传播充分和迅速地下降，从而结束这一流行病。用于帮助校准传播模型和预测疾病传播/严重程度的不同数据收集和测试模式和策略，其成本、响应时间和准确性各不相同。在这个快速反应研究（Rapid）项目中，该小组将研究建立新型冠状病毒快速检测最佳做法的问题。结果将是流行病模型快速测试（RTEM），这将转化为对COVID-19流行病特征（包括持续时间和总体规模）的科学预测，并有助于全球抗击该疾病的努力。RTEM将填补COVID-19疫情期间数据驱动决策方面的重要空白，从而实现对国家经济和健康产生重大影响的服务。该项目的教育影响将是对博士后和博士研究人员的指导，并通过将研究挑战和成果纳入现有的本科和研究生课程来改变课程。新发传染病时空动态的计算模型以及疾病传播的数据和模型驱动的计算机模拟在预测流行病的时空演变以及设计、启动和调整控制流行病的做法方面越来越重要。在这个项目中，研究人员解决了流行病模型快速测试（RTEM）问题：给定部分已知的目标疾病模型和一组测试模式（从调查到已知疾病热点的监测测试），具有不同的成本，准确性和观察延迟，什么是有助于恢复潜在疾病模型的最佳快速测试策略？出现了几个科学问题：测试的价值是什么？是否应该只对生病的人进行病毒检测？应该投入多少资源开发高度准确的测试（低假阳性和低假阴性）？是只使用一种类型的测试来达到最佳的成本/效率平衡，还是使用非同质测试策略？当然，这些问题需要在流行病学、计算机科学、机器学习、数学建模和统计学的界面上进行调查。作为工作的一部分，该团队将开发针对COVID-19量身定制的传播动态和控制模型，以适应快速检测方案基础上不同保真度和延迟的诊断检测。研究人员将进一步将得到的RTEM-SEIR模型与EpiDMS和DataStorm集成，以执行连续耦合模拟。该项目由传染病生态学和进化计划（环境生物学部）和土木、机械和制造创新计划（工程）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Tracking Disaster Footprints with Social Streaming Data

• DOI: 10.1609/aaai.v34i01.5372
• 发表时间: 2020-04
• 作者: Lu Cheng;Jundong Li;K. Candan;Huan Liu