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.

在加拿大的Covid-19爆发爆发的第一波浪潮之后，省级当局目前正在放松对各种机构和社交聚会类型的锁定限制。减轻Covid-19的传播的政策基于SARS-COV-2感染患病率的状态和预测，使用各种模型和或多或少的颗粒人群数据。当前的模型主要利用COVID-199临床测试结果，住院和死亡。但是，COVID-19临床测试结果仅在感染并具有传染性后的两到五天的延迟中反应。 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COVID-19感染模型。从历史上看，该技术一直在检测不同种类的病原体方面有效，并且最近证明可以同意COVID-19的人口测试结果，并在这些测试之前进行了多达7天。 为了解决COVID-19的最高用法和实时的使用基于废水的流行病学（WBE）所提出的当前挑战，该项目提出了一个研究议程，该研究议程将在WBE背景下导致病毒信号数据的解释。所提出的方法基于当前现象学WBE命运模型与时间序列机器学习（ML）模型之间的混合方法，旨在利用公共可用的高度颗粒状临床测试来自加拿大卫生机构和其他公共可用信息的结果。 我们预计该项目将导致WBE的进步及其协议的标准化，这反过来又将导致在加拿大一级的联邦模型产生，以提供全球但细微的预测模型。