CAREER: Wastewater Counts: Real-time de facto Population Estimation for Quantitative Wastewater-based Epidemiology

职业：废水计数：基于废水的定量流行病学的实时实际人口估计

• 批准号: 2047470
• 负责人: Fangqiong Ling
• 金额: $ 50万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047470&HistoricalAwards=false
• 关键词: CAREER; Wastewater; Counts; Real; time

Wastewater-based epidemiology (WBE) has emerged as a complementary approach to existing clinical tools to track the spread of infectious disease. WBE is based on using microbial biomarkers in wastewater to monitor the onset, spread, and community transmission of infectious diseases. Although WBE has provided valuable insight into the spread of SARS-CoV-2 virus during the COVID-19 pandemic, much work remains to be done to enable accurate quantitative estimates using this technique. The goal of this CAREER project is to address this critical challenge through the development of computational approaches, models, and tools to accurately predict the size of a disease outbreak from wastewater biomarker data. Successful completion of this project will benefit society through the development of new and experimentally validated computational tools to predict community disease spread. This will allow a fuller exploitation of the large amount of WBE data that are being collected in the United States to advance public health. Further benefits to society will be achieved through the mentoring of a doctoral student at Washington University in St. Louis. Additional benefits derive from the development of an education kit focused on environmental data sciences to advance science literacy in K-12 students.The overarching goal of this CAREER project is to advance the science and engineering of wastewater-based epidemiology (WBE) through the development and validation of new computational tools to estimate the size of affected population in real time from biomarkers in untreated wastewater. This will be achieved through specific research designed to (1) build a computational framework for wastewater microbiome-based population models, (2) develop and experimentally validate models and computational tools for estimating the size affected populations in infectious disease outbreaks using community wastewater surveillance data, and (3) apply these new models and tools to evaluate and improve the design of WBE sampling programs. The educational plan will (1) engage wastewater treatment and public health practitioners in refining and disseminating microbiome-based population models and design tools for WBE, 2) enhance the interdisciplinary training of environmental engineering undergraduate and graduate students by integrating data science into the educational curriculum, and 3) develop and distribute a K-12 educational kit on environmental data science based on Next Generation Science Standards. The successful completion of this project has strong potential to transform our ability to track disease outbreaks through the development of new computational tools for WBE surveillance programs. The broader impacts of this project will be enhanced by training practitioners and providing educational resources to diverse student populations at the K-12, college, and postgraduate levels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

以废水为基础的流行病学(WBE)已成为追踪传染病传播的现有临床工具的补充方法。WBE的基础是使用废水中的微生物生物标记物来监测传染病的发病、传播和社区传播。尽管世界经济论坛为新冠肺炎大流行期间SARS-CoV-2病毒的传播提供了宝贵的洞察力，但要使用这一技术进行准确的定量估计仍有许多工作要做。这个职业项目的目标是通过开发计算方法、模型和工具来解决这一关键挑战，以根据废水生物标记物数据准确预测疾病爆发的规模。该项目的成功完成将通过开发新的和经过实验验证的计算工具来预测社区疾病的传播，从而造福社会。这将允许更充分地利用美国正在收集的大量WBE数据，以促进公共健康。通过指导圣路易斯华盛顿大学的一名博士生，将进一步造福社会。这一职业项目的首要目标是通过开发和验证新的计算工具来根据未经处理的废水中的生物标记物实时估计受影响人口的规模，从而促进废水流行病学(WBE)的科学和工程。这将通过专门的研究来实现，其目的是(1)建立基于废水微生物组的种群模型的计算框架，(2)开发和实验验证模型和计算工具，以利用社区污水监测数据估计传染病暴发中受影响人口的大小，以及(3)应用这些新的模型和工具来评估和改进WBE抽样计划的设计。该教育计划将(1)让废水处理和公共卫生从业者改进和传播基于微生物组的种群模型和WBE设计工具，(2)通过将数据科学纳入教育课程来加强环境工程本科生和研究生的跨学科培训，以及(3)根据下一代科学标准开发和分发关于环境数据科学的K-12教育工具包。该项目的成功完成具有很强的潜力，可以通过为WBE监测项目开发新的计算工具来改变我们跟踪疾病爆发的能力。该项目的更广泛的影响将通过培训实践者和为不同的学生群体提供K-12、大学和研究生水平的教育资源而得到加强。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Emerging investigator series: meta-analyses on SARS-CoV-2 viral RNA levels in wastewater and their correlations to epidemiological indicators

• DOI: 10.1039/d2ew00084a
• 发表时间: 2022-05-04
• 期刊: ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY
• 影响因子: 5
• 作者: Mantilla-Calderon, David;Huang, Kaiyu (Kevin);Ling, Fangqiong
• 通讯作者: Ling, Fangqiong