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的持续演变和新变种的出现，COVID-19可能会继续 在未来给美国带来巨大的公共卫生负担。然而，临床的倒退 全国范围内的检测项目和家庭检测的增加将加剧SARS的检测不足， 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 建模工具，以解决这一监督的挑战是迫切需要的，这一应用的目标。我们 建议使用废水监测数据研究SARS-CoV-2动态并预测COVID-19病例， 住院治疗和未来1至6周内死亡。提出的核心模型-推理/预测系统 将联合收割机结合描述SARS-CoV-2在普通人群中传播的机制模型， 集合调整卡尔曼滤波（EAKF），以纳入SARS-CoV-2废水监测数据， 推论我们将使用丰富的数据（废水监测和多种COVID-19）对该系统进行试点测试 结果）和详细的模型估计（例如，感染流行率）。我们 然后将在美国50多个县扩展和测试该系统（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指标 SARS-CoV-2在人群中的存在，无论个人的测试行为; 2）我们将建立预测 这些系统超越了仅由废水监测提供的情况意识。我们将设计 3）使用模块化模型组件以适应不同数据的灵活性 4）通过利用纽约市和50多个城市的详细数据和估计， 县，以测试和改进各种模型的形式，并量化每个模型的不确定性和准确性。 此外，调查小组综合了废水监测和建模方面的专业知识， 与公共卫生官员密切合作，根据公共卫生需求定制模型系统。关于SARS-CoV-2 废水监测广泛应用于许多社区（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积极主动的COVID-19规划。