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.

利用污水监测数据研究SARS-CoV-2动态及预测新冠肺炎结局 由于SARS-CoV-2的持续进化和新变种的出现，新冠肺炎很可能会继续下去 在未来给美国带来巨大的公共卫生负担。然而，临床的回滚 全国范围内的检测计划和更多的家庭检测使用将加剧对SARS的漏检- CoV-2感染，阻碍了对公共卫生状况的及时了解和干预。因此，发展 迫切需要建模工具来应对这一监视挑战，这一应用程序的目标也是如此。我们 建议利用污水监测数据研究SARS-CoV-2动态，预测新冠肺炎病例， 住院，以及未来1至6周内的死亡。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械模型和 集成SARS-CoV-2废水监测数据的集合调整卡尔曼滤波(EAKF) 推论。我们将使用丰富的数据(废水监测和多个新冠肺炎)对该系统进行中试 结果)和纽约市现有的详细模型估计数(如感染率)(目标1)。我们 然后将在全美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一个易于使用的建模工具(目标3)。建议的工作是 创新性和健壮性：1)废水中SARS-CoV-2的浓度代表了一种综合衡量标准 SARS-CoV-2在人群中的存在，而不考虑个人的检测行为；2)我们将建立预测 系统超出了仅靠废水监测所能提供的情况感知能力。我们将设计 模型预测系统应灵活使用模块化的模型组件以适应不同的数据 跨地点可用性；4)通过利用纽约市和50+个城市的详细数据和估计，实现强大的可用性 各县对各种模型形式进行测试和改进，并量化每个模型的不确定性和准确性。 此外，调查员小组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模型系统。与SARS-CoV-2 废水监测在许多社区被广泛采用(目前代表1亿多美国人)， 本文开发的模型预测系统可以支持未来更加积极主动的新冠肺炎规划。