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.

使用废水监视数据研究SARS-COV-2动力学并预测COVID-19的结果 由于SARS-COV-2的持续发展和新变体的出现，Covid-19可能会继续 将来在美国施加重大的公共卫生负担。但是，临床的回滚 在全国范围内进行测试计划和增加的家庭测试的使用将使SARS- COV-2感染，阻碍及时的公共卫生状况意识和干预。因此，发展 迫切需要建模以应对这一监视挑战的工具以及该应用程序的目标。我们 建议使用废水监视数据来研究SARS-COV-2动力学并预测COVID-19案例， 未来的住院和死亡1至6周。提出的核心模型推动/预测系统 将结合描述一般人群中SARS-COV-2传播的机械模型和 合奏调整Kalman滤波器（EAKF）以合并SARS-COV-2废水监视数据 推理。我们将使用丰富的数据（废水监视和多个COVID-19 结果和详细的模型估计（例如，感染率）可用于纽约市（AIM 1）。我们 然后，将在美国的50多个县进行扩展和测试该系统（AIM 2）。使用这些模型， 我们将为公共卫生官员创建易于使用的建模工具（AIM 3）。拟议的工作是 创新和健壮的1）废水中的SARS-COV-2浓度代表​​了一个复合度量 无论个人测试行为如何，SARS-COV-2的存在； 2）我们将建立预测 仅除废水监视所提供的局势意识而超出了局势意识。我们将设计 模型预测系统为3）使用模块化模型组件的灵活性来容纳多种数据 在各个位置的可用性和4）通过利用纽约市和50+的详细数据和估计来稳健 县测试和改善各种模型形式并量化每个模型的不确定性和准确性。 此外，调查员团队还综合了废水监视和建模方面的专业知识，并将 与公共卫生官员紧密合作，以根据公共卫生需求量身定制建模系统。与SARS-COV-2 在许多社区（目前代表100万美国人）广泛采用的废水监视， 此处开发的模型预测系统可以在将来支持更为主动的Covid-19计划。