Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomes

利用废水监测数据研究 SARS-CoV-2 动态并预测 COVID-19 结果

• 批准号: 10645617
• 负责人: Wan Yang
• 金额: $ 24.68万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-10 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10645617
• 关键词: 2019-nCoV; Accounting; Address; Adopted; American; Awareness; Back; Biological Models; COVID-19; COVID-19 detection; COVID-19 monitoring; COVID-19 pandemic; COVID-19 surveillance; COVID-19 testing; Case Study; Cessation of life; Cities; Communities; County; Data; Data Set; Detection; Development; Disease Surveillance; Emergency department visit; Epidemiology; Evolution; Future; General Population; Goals; Home; Hospitalization; Immunity; Individual; Infection; Intervention; Location; Maps; Measures; Modeling; Neighborhoods; New York City; Outcome; Plants; Population; Predisposition; Prevalence; Public Health; RNA; Readiness; Reporting; Research Personnel; SARS-CoV-2 infection; SARS-CoV-2 transmission; Secondary Immunization; System; Testing; Time; Uncertainty; United States; Vaccination; Vaccines; Variant; Virus Shedding; Work; behavior test; community transmission; diverse data; flexibility; home test; improved; innovation; model building; model design; novel; pandemic disease; pilot test; predictive modeling; programs; research clinical testing; response; surveillance data; tool; transmission process; trend; user-friendly; wastewater surveillance

Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomes Due to the continued evolution of SARS-CoV-2 and emergence of new variants, COVID-19 will likely continue to impose a substantial public health burden in the United States in the future. Yet, the rollback of clinical testing programs and increased use of at-home tests nationwide will exacerbate under-detection of SARS- CoV-2 infections, hindering timely public health situation awareness and intervention. Thus, development of modeling tools to tackle this surveillance challenge is urgently needed and the goal of this application. We propose to use wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 cases, hospitalizations, and deaths 1 to 6 weeks in the future. The proposed core model-inference/prediction system will combine mechanistic models depicting SARS-CoV-2 transmission in the general population and the ensemble adjustment Kalman filter (EAKF) to incorporate SARS-CoV-2 wastewater surveillance data for inference. We will pilot-test this system using both rich data (wastewater surveillance and multiple COVID-19 outcomes) and detailed model estimates (e.g., infection prevalence) available for New York City (Aim 1). We will then expand and test the system on 50+ counties across the United States (Aim 2). Using these models, we will further create an easy-to-use modeling tool for public health officials (Aim 3). The proposed work is Innovative and Robust in that 1) SARS-CoV-2 concentration in wastewater represents a composite measure of SARS-CoV-2 presence in the population, regardless of individual testing behavior; 2) We will build prediction systems that go beyond the situation awareness afforded by wastewater surveillance alone. We will design the model-prediction system to be 3) flexible using modularized model components to accommodate diverse data availability across locations and 4) robust by leveraging detailed data and estimates for New York City and 50+ counties to test and improve various model forms and quantify the uncertainty and accuracy of each model. Further, the Investigator Team has synthesized expertise in wastewater surveillance and modeling, and will work closely with public health officials to tailor the modeling system to public health need. With SARS-CoV-2 wastewater surveillance widely adopted in many communities (currently representing 100+ million Americans), the model-prediction system developed here can support more proactive COVID-19 planning in the future.

利用废水监测数据研究 SARS-CoV-2 动态并预测 COVID-19 结果 由于 SARS-CoV-2 的持续进化和新变种的出现，COVID-19 可能会继续 未来会给美国带来巨大的公共卫生负担。然而，临床试验的倒退 检测计划和全国范围内家庭检测的增加将加剧 SARS 的漏检情况。 CoV-2 感染，阻碍了及时的公共卫生状况认知和干预。因此，发展 迫切需要解决这一监视挑战的建模工具，这也是本应用程序的目标。我们 提议使用废水监测数据来研究 SARS-CoV-2 动态并预测 COVID-19 病例， 未来 1 至 6 周内住院和死亡。提出的核心模型——推理/预测系统 将结合描述 SARS-CoV-2 在普通人群中传播的机制模型和 集成调整卡尔曼滤波器 (EAKF)，将 SARS-CoV-2 废水监测数据纳入其中 推理。我们将使用丰富的数据（废水监测和多个 COVID-19）对该系统进行试点测试 结果）和纽约市可用的详细模型估计（例如感染率）（目标 1）。我们 然后将在美国 50 多个县扩展并测试该系统（目标 2）。使用这些模型， 我们将进一步为公共卫生官员创建一个易于使用的建模工具（目标 3）。拟议的工作是 创新性和稳健性在于 1) 废水中的 SARS-CoV-2 浓度代表了一种综合衡量标准 无论个体检测行为如何，人群中是否存在 SARS-CoV-2； 2）我们将建立预测 系统超越了废水监测本身所提供的态势感知能力。我们将设计 模型预测系统3）灵活地使用模块化模型组件来适应不同的数据 跨地点的可用性，4) 通过利用纽约市和 50 多个城市的详细数据和估计来实现稳健 县测试和改进各种模型形式，并量化每个模型的不确定性和准确性。 此外，研究团队综合了废水监测和建模方面的专业知识，并将 与公共卫生官员密切合作，根据公共卫生需求定制建模系统。 SARS-CoV-2 许多社区广泛采用废水监测（目前代表 1 亿多美国人）， 这里开发的模型预测系统可以支持未来更主动的 COVID-19 规划。