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.

在加拿大爆发第一波 COVID-19 疫情后，省级当局目前正在放宽对各种机构和社交聚会类型的封锁限制。减缓 COVID-19 传播的政策基于 SARS-CoV-2 感染流行率的状况和预测，使用各种模型和或多或少的精细人口数据。目前的模型主要利用 COVID-19 临床测试结果、住院情况和死亡情况。然而，COVID-19 临床检测结果仅在患者感染并具有传染性后延迟两到五天才会做出反应。 拟议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。该技术历来可有效检测不同种类的病原体，最近被证明与 COVID-19 人群检测结果一致，并且比这些检测早多达 7 天。 为了解决当前大规模和实时使用基于废水的流行病学 (WBE) 来检测 COVID-19 所带来的挑战，该项目提出了一项研究议程，该议程将导致在解释病毒信号数据方面取得进展。 WBE 的背景。所提出的方法基于当前现象学 WBE 命运模型和时间序列机器学习 (ML) 模型之间的混合，旨在利用加拿大卫生机构公开提供的高度精细的临床测试结果以及其他公开信息，例如下水道配置、气象信息、废水进水流量和成分。 我们预计该项目将推动 WBE 及其协议标准化的进步，进而导致加拿大层面联合模型的生成，以便提供全球性但精细的流行病预测模型。