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.

项目总结。2019冠状病毒病（COVID-19）大流行放大了对提高准确识别能力的需求