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
基本信息
- 批准号:10645617
- 负责人:
- 金额:$ 24.68万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:2019-nCoVAccountingAddressAdoptedAmericanAwarenessBackBiological ModelsCOVID-19COVID-19 detectionCOVID-19 monitoringCOVID-19 pandemicCOVID-19 surveillanceCOVID-19 testingCase StudyCessation of lifeCitiesCommunitiesCountyDataData SetDetectionDevelopmentDisease SurveillanceEmergency department visitEpidemiologyEvolutionFutureGeneral PopulationGoalsHomeHospitalizationImmunityIndividualInfectionInterventionLocationMapsMeasuresModelingNeighborhoodsNew York CityOutcomePlantsPopulationPredispositionPrevalencePublic HealthRNAReadinessReportingResearch PersonnelSARS-CoV-2 infectionSARS-CoV-2 transmissionSecondary ImmunizationSystemTestingTimeUncertaintyUnited StatesVaccinationVaccinesVariantVirus SheddingWorkbehavior testcommunity transmissiondiverse dataflexibilityhome testimprovedinnovationmodel buildingmodel designnovelpandemic diseasepilot testpredictive modelingprogramsresearch clinical testingresponsesurveillance datatooltransmission processtrenduser-friendlywastewater surveillance
项目摘要
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('Wan Yang', 18)}}的其他基金
UNCOVER: underlying novel causes of onset of very early cancer research
揭秘:极早期癌症研究开始的潜在新原因
- 批准号:
10675591 - 财政年份:2021
- 资助金额:
$ 24.68万 - 项目类别:
UNCOVER: underlying novel causes of onset of very early cancer research
揭秘:极早期癌症研究开始的潜在新原因
- 批准号:
10482393 - 财政年份:2021
- 资助金额:
$ 24.68万 - 项目类别:
UNCOVER: underlying novel causes of onset of very early cancer research
揭秘:极早期癌症研究开始的潜在新原因
- 批准号:
10303652 - 财政年份:2021
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$ 24.68万 - 项目类别:
Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination
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10435483 - 财政年份:2019
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$ 24.68万 - 项目类别:
Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination
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10199927 - 财政年份:2019
- 资助金额:
$ 24.68万 - 项目类别:
Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination
疾病持续性和人口动态:大规模疫苗接种下的麻疹建模
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9795652 - 财政年份:2019
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$ 24.68万 - 项目类别:
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