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: 10481536
• 负责人: Nathan L Tintle
• 金额: $ 25.96万
• 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/10481536
• 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; Consult; 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; Michigan; Modeling; Municipalities; Norovirus; Pathway interactions; Persons; Phase; Population; Prevalence; Public Health; Recording of previous events; Reporting; Research; SARS-CoV-2 infection; SARS-CoV-2 variant; Sampling; Scientist; Services; Severities; Small Business Innovation Research Grant; Statistical Models; Techniques; Test Result; Testing; Time; Translating; Trust; Variant; Viral; Virus; Virus Diseases; Work; base; commercialization; community organizations; cost; data integration; data modeling; genomic signature; health care availability; health disparity; high reward; high risk; improved; innovation; interest; machine learning method; machine learning model; next generation; novel; pathogen; pathogenic virus; policy recommendation; poor communities; population based; predictive modeling; prevent; programs; research and development; statistics; uptake; 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大流行已经放大了对准确能力的需求 预期由于新颖和地方性病毒病原体而引起的未来爆发。没有系统的监视， 在发生之前爆发爆发的能力是具有挑战性的：来自正面人类测试结果的数据通常是 尽管大量努力努力，但为时已晚，无法防止重大爆发发生。关键原因 这种延迟是人们在几天前（如果）被诊断为阳性。我们再也不能 依靠基于人群的测试，该测试延迟了； （b）非随机且昂贵，加剧了 已知并了解健康差异； （c）依靠高度准确，分布广泛的测试可用性 并使用。在过去的14个月中，我们的附属科学家团队已经开发并实施了 废水采样方法以监测COVID-19和其他病毒病原体。我们的方法利用 SARS-COV-2的独特基因组特征（导致COVID-19的病毒）检测这种病原体 废水，提供有关社区和社区的共同感染的廉价且公正的实时数据 组织。我们的小组已开始与市政当局，学术实体和大型制造 公司提供有关COVID-19的实时，公正的数据。但是，目前是废水 COVID-19数据主要用于确定SARS-COV-2在中的存在/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以预测 SARS-COV-2和其他小说和特有病毒的未来爆发的时间，位置和严重性 病原体。高级统计研究（SSR）R＆D团队是一个国际认可的小组 具有统计学，数据分析，建模，数据的跨裁切专业知识的废水和公共卫生专家 计算，废水监测以及将废水和健康信息转化为 为组织和社区采取可行的步骤。为了解决这个机会，我们提出了I阶段证明 - Concept Sbir项目具有两个目标。首先，我们将证明可以预见地点和 与未来的Covid-19的未来爆发的组织具有大量的交付时间。第二，我们将证明如何 模型预测可以优化，可对市政当局和组织有用。可行性将是 通过具有具有出色预测能力（R2> 0.90）（AIM 1）的模型来确定 商业化途径的盈利能力（AIM 2）。第一阶段的可行性将使我们能够扩展建模 SARS-COV-2超出其他病毒病原体的能力（例如流感，诺如病毒，艾滋病毒）：扩展 这些其他病原体的废水测试能力，以及进一步的推出和改进 II阶段的机器学习/建模工作。最终，我们将拥有全方位服务的商业集 预测模型（第三阶段）可以与社区的废水监测计划结合 和组织层面，导致病毒疾病暴发的大幅减少。