Peng K Ken, Renouf Elizabeth M, Dean Charmaine B, Hu X Joan, Delatolla Robert, Manuel Douglas G
Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Dr, Burnaby, V5A 1S6, BC, Canada.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave W, Waterloo, N2L 3G1, ON, Canada.
Infect Dis Model. 2023 Sep;8(3):617-631. doi: 10.1016/j.idm.2023.05.011. Epub 2023 Jun 7.
Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19, especially during times of limited availability in testing. Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions. The association is likely nonlinear and time-varying. This project employs a distributed lag nonlinear model (DLNM) (Gasparrini et al., 2010) to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa, Canada. We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concentrations to COVID-19 hospitalizations. The expected reduction in hospitalization is adjusted for vaccination efforts. A correlation analysis of the data verifies that COVID-19 hospitalizations are highly correlated with wastewater viral signals with a time-varying relationship. Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospitalizations with wastewater viral signals.
监测废水中的病毒信号被认为是监测新冠疫情负担的一种有用工具，尤其是在检测资源有限的时候。研究表明，新冠住院病例与废水病毒信号高度相关，废水病毒信号的增加可为住院人数增加提供早期预警。这种关联可能是非线性且随时间变化的。本项目采用分布滞后非线性模型（DLNM）（加斯帕里尼等人，2010年），利用加拿大渥太华的相关数据，研究新冠住院病例与新冠病毒废水病毒信号之间的非线性暴露-反应延迟关联。我们考虑从新冠病毒N1和N2基因浓度平均值到新冠住院病例最多15天的时间滞后。住院人数的预期减少会根据疫苗接种情况进行调整。数据的相关性分析证实，新冠住院病例与废水病毒信号高度相关，且存在随时间变化的关系。我们基于DLNM的分析得出了对新冠住院病例的合理估计，并加深了我们对新冠住院病例与废水病毒信号关联的理解。
