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: 2026797
• 负责人: Gerardo Chowell-Puente
• 金额: $ 6.7万
• 依托单位: Georgia State University Research Foundation, Inc.
• 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=2026797&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.

新颖的冠状病毒（Covid-19）流行正在产生重大的社会，经济和健康影响，并强调了对新兴感染性疾病的时空动态实时分析的重要性。 2019年12月从中国武汉市出来的Covid-19现在正在多个国家蔓延。特别是，新型冠状病毒的病例死亡率似乎高于季节性流感，尤其是对于老年人群以及患有心血管疾病和糖尿病等健康状况的人群。任何停止流行病的计划都必须基于对需要受到有效控制措施保护的高危人群比例的定量理解，以便传播足够迅速地下降，以使流行病结束。可用的不同数据收集和测试方式和策略，以帮助校准传播模型并预测疾病的差异/严重性，具有可变成本，响应时间和准确性。在这项快速响应研究（快速）项目中，团队将研究建立最佳实践以快速测试新型冠状病毒的问题。结果将是对流行建模（RTEM）的快速测试，该测试将转化为基于科学的Covid-19流行病特征的预测，包括持续时间和整体规模，并帮助全球努力打击该疾病。 RTEM将填补Covid-19期间数据驱动决策的重要差距，因此，将实现具有重大国家经济和健康影响的服务。该项目的教育影响将是通过将研究挑战和成果纳入现有的本科生和研究生班来指导博士后和博士研究人员以及课程。新兴传染病的时空动力学以及用于疾病扩散的数据和模型驱动的计算机模拟的计算模型对于预测流行病的地理演变以及设计，激活和适应性实践以控制流行病而越来越重要。在该项目中，研究人员应对流行病建模的快速测试（RTEM）问题：给定部分已知的目标疾病模型和一组测试方式（从已知疾病热点的调查到监视测试），具有不同的成本，准确性，准确性和观察性测试策略，有助于恢复疾病模型的最佳快速测试策略是什么？出现了几个科学问题：测试的价值是什么？应该只对病毒检测进行测试？应该将哪些水平的资源用于开发高度准确的测试（低假阳性，低误否负面因素）？仅使用一种针对最佳成本/有效性权衡的一种测试，或者是非均匀的测试政策？自然，这些问题需要在流行病学，计算机科学，机器学习，数学建模和统计的界面上进行研究。作为工作的一部分，团队将开发出针对Covid-19的传输动力学和控制模型，该模型以适应诊断性测试的方式，具有不同的保真度和延误，并延迟了快速测试方案。研究人员将进一步将所得的RTEM-SEIR模型与Epidms和Datastorm进行了连续耦合模拟。该项目是通过传染病计划（环境生物学系）和民用，机械和制造创新计划（Engineering）（工程）共同资助的。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。