COVID-19 wastewater-based epidemiology back calculation using hybrid modelling methods

使用混合建模方法进行基于 COVID-19 废水的流行病学反算

• 批准号: 554965-2020
• 负责人: Vanrolleghem, Peter
• 金额: $ 3.64万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705711
• 关键词: COVID; 19; wastewater; based; epidemiology

Following the first wave of the COVID-19 outbreak in Canada, provincial authorities are currently relaxing lockdown restrictions of various institutions and types of social gathering. Policies to mitigate the spread of COVID-19 are based on the state and forecast of SARS-CoV-2 infection prevalence, using various models and more or less granular population data. Current models mostly exploit COVID-19 clinical test results, hospital admissions, and deaths. However, the COVID-19 clinical test results only react with a delay of two to five days after a patient is infected and contagious. The proposed project aims at complementing existing COVID-19 infection models through the use of data collected from wastewater analysis. This technique has historically been effective in detecting different kinds of pathogens and recently proven to concur with COVID-19 population test results and precede those tests by as much as 7 days. In order to address current challenges posed by the usage at scale and in real-time of wastewater-based epidemiology (WBE) for COVID-19, this project proposes a research agenda that will lead to progress in the interpretation of viral signal data in the context of WBE. The proposed approach, based on a hybrid between current phenomenological WBE fate models and time series Machine Learning (ML) models is aimed at exploiting both publically available highly granular clinical testing results from the Canadian health institutions and other publically available information such as sewershed configurations, meteorological information, influent wastewater flows and compositions. We expect this project will lead to advances in WBE and the standardization of its protocol, which in turn will lead to the generation of federated models at the Canadian level, in order to provide a global, but granular forecasting model of a pandemic.

继加拿大第一波新冠肺炎爆发后，目前省政府正在放松对各种机构和类型社交聚会的封锁限制。缓解新冠肺炎传播的政策是基于SARS-CoV-2感染流行的状态和预测，使用各种模型和或多或少细粒度的人口数据。目前的模型大多利用新冠肺炎的临床测试结果、入院人数和死亡人数。然而，新冠肺炎临床检测结果只有在患者被感染和传染后两到五天才会做出反应。 拟议的项目旨在通过使用从废水分析中收集的数据来补充现有的新冠肺炎感染模型。从历史上看，这项技术在检测不同类型的病原体方面是有效的，最近被证明与新冠肺炎种群检测结果一致，并且比那些检测提前了多达7天。 为了应对当前新冠肺炎大规模和实时使用废水流行病学带来的挑战，该项目提出了一项研究议程，将在废水流行病学背景下对病毒信号数据的解释方面取得进展。该方法基于当前现象学WBE命运模型和时间序列机器学习(ML)模型的混合，旨在利用来自加拿大卫生机构的公共可用高颗粒临床测试结果和其他公共可用信息，如排污场配置、气象信息、进水污水流量和成分。 我们预计这个项目将导致WBE的进步及其协议的标准化，这反过来将导致在加拿大一级产生联合模型，以便提供全球但细粒度的大流行预测模型。