Wastewater data integration and modelling to accurately predict community and organizational outbreaks due to viral pathogens

废水数据集成和建模，以准确预测病毒病原体引起的社区和组织爆发

• 批准号: 10768053
• 负责人: Nathan L Tintle
• 金额: $ 5.5万
• 依托单位: SUPERIOR STATISTICAL RESEARCH, LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-11 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10768053
• 关键词: 2019-nCoV; Address; Biological; COVID-19; COVID-19 diagnosis; COVID-19 monitoring; COVID-19 outbreak; COVID-19 pandemic; Cessation of life; Communities; Complication; Contracts; Cost Savings; Data; Data Analyses; Data Science; Diagnosis; Disease; Disease Outbreaks; Economics; Effectiveness; Epidemic; Future; Growth; HIV; Head; Health; Hospitalization; Human; Influenza; International; Knowledge; Lead; Life; Location; Machine Learning; Michigan; Modeling; Municipalities; Norovirus; Pathogen detection; Pathway interactions; Persons; Phase; Population; Prevalence; Public Health; Public Policy; Recording of previous events; Reporting; Research; SARS-CoV-2 infection; SARS-CoV-2 variant; Sampling; Scientist; Service setting; Services; Severities; Small Business Innovation Research Grant; Statistical Models; Techniques; Test Result; Testing; Time; Translating; Trust; Variant; Virus; Work; commercialization; cost; data integration; data modeling; future outbreak; genomic signature; health care availability; health disparity; high reward; high risk; improved; innovation; interest; machine learning method; manufacture; next generation; novel; pathogen; pathogenic virus; policy recommendation; poor communities; population based; predictive modeling; prevent; programs; research and development; statistics; uptake; viral outbreak; wastewater monitoring; wastewater sampling; wastewater testing

Project Summary. The COVID-19 pandemic has magnified the need for enhanced ability to accurately anticipate future outbreaks due to novel and endemic viral pathogens. Without systematic surveillance, the ability to head off outbreaks before they occur is challenging: the data from positive human test results is often too late to prevent a major outbreak from occurring, despite substantial lockdown efforts. The key reason for this delay is that people are infectious for days before (and if) they are diagnosed positive. We can no longer rely on population-based testing, which (a) is delayed; (b) is non-random and expensive, exacerbating well- known and understood health disparities; and (c) relies on highly accurate, widely distributed test availability and use. Over the last fourteen months, our team of affiliated scientists has developed and implemented a wastewater-sampling approach to monitor for COVID-19 and other viral pathogens. Our approach utilizes unique genomic signatures of SARS-CoV-2 (the virus that causes COVID-19) to detect this pathogen in wastewater, providing inexpensive and unbiased real-time data on COVID-19 infections in communities and organizations. Our group has begun to contract with municipalities, academic entities and large manufacturing companies to provide real-time, unbiased data on the presence of COVID-19. Currently, however, wastewater COVID-19 data has primarily been used solely to determine the presence/absence of SARS-CoV-2 in samples. We see a highly innovative and impactful opportunity to leverage these data further to anticipate the timing, location, and severity of future outbreaks from SARS-CoV-2 and other novel and endemic viral pathogens. The Superior Statistical Research (SSR) R&D team is an internationally recognized group of wastewater and public health experts with cross-cutting expertise in statistics, data analysis, modelling, computing, wastewater monitoring, and the ability to translate wastewater and health information into actionable steps for organizations and communities. To address this opportunity, we propose a Phase I proof- of-concept SBIR project with two Aims. First, we will demonstrate that it is possible to anticipate locations and organizations with future outbreaks of COVID-19 with significant lead time. Second, we will demonstrate how model predictions can be optimized to be useful for municipalities and organizations. Feasibility will be determined by having models with excellent predictive ability (R2>0.90) (Aim 1) and by demonstrating the profitability of the commercialization pathway (Aim 2). Phase I feasibility will allow us to extend modelling capabilities beyond SARS-CoV-2 to other viral pathogens (e.g., influenza, norovirus, HIV): expanding wastewater testing capabilities for these additional pathogens, and further roll-out and improvement of the machine-learning/modelling effort in Phase II. Ultimately, we will have a full-service commercial set of predictive models (Phase III) that can be combined with wastewater-monitoring programs at the community and organizational level, leading to dramatic reductions in viral disease outbreaks.

项目摘要。 COVID-19 大流行加剧了对增强准确预测能力的需求 预测未来由于新型和地方性病毒病原体而爆发的疫情。如果没有系统的监控， 在疫情发生之前阻止疫情爆发的能力具有挑战性：来自阳性人体测试结果的数据通常是 尽管采取了巨大的封锁措施，但仍无法阻止重大疫情的爆发。关键原因是 这种延迟是指人们在被诊断为阳性之前（以及如果）几天内就具有传染性。我们不能再 依赖基于人群的测试，但 (a) 被延迟； (b) 是非随机且昂贵的，加剧了 已知和理解的健康差异； (c) 依赖于高度准确、广泛分布的测试可用性 并使用。在过去的十四个月里，我们的附属科学家团队开发并实施了 用于监测 COVID-19 和其他病毒病原体的废水采样方法。我们的方法利用 SARS-CoV-2（导致 COVID-19 的病毒）的独特基因组特征可检测这种病原体 废水，提供有关社区和社区中的 COVID-19 感染的廉价且公正的实时数据 组织。我们集团已开始与市政当局、学术机构和大型制造企业签订合同 公司提供有关 COVID-19 存在的实时、公正的数据。但目前，废水 COVID-19 数据主要仅用于确定 SARS-CoV-2 的存在/不存在 样品。我们看到了一个高度创新和有影响力的机会，可以进一步利用这些数据来预测 SARS-CoV-2 和其他新型地方性病毒未来爆发的时间、地点和严重程度 病原体。高级统计研究（SSR）研发团队是国际公认的团队 废水和公共卫生专家，在统计、数据分析、建模、 计算、废水监测以及将废水和健康信息转化为 组织和社区的可行步骤。为了抓住这个机会，我们提出了第一阶段的证明- SBIR 概念概念项目有两个目标。首先，我们将证明预测位置和 未来可能会爆发 COVID-19 的组织，并且需要很长的准备时间。其次，我们将演示如何 可以优化模型预测以对市政当局和组织有用。可行性将是 通过拥有具有出色预测能力的模型 (R2>0.90)（目标 1）并通过证明 商业化途径的盈利能力（目标 2）。第一阶段的可行性将使我们能够扩展建模 超越 SARS-CoV-2 的针对其他病毒病原体（例如流感、诺如病毒、HIV）的能力：扩展 针对这些额外病原体的废水检测能力，以及进一步推广和改进 第二阶段的机器学习/建模工作。最终，我们将拥有一套全方位服务的商业套件 可以与社区废水监测项目相结合的预测模型（第三阶段） 和组织层面，从而大幅减少病毒性疾病的爆发。