Abstract: We used a distributed lag non-linear model to study the non-linear exposure-response delayed association of SARS-CoV-2 RNA concentrations in the wastewater surveillance system and the hospitalization rate in Ottawa, Ontario, Canada. Our model considered up to a 15-day time-lag of the average of SARS-CoV N1 and SARS-CoV N2 gene concentrations and also an adjusted daily vaccination contribution to the Covid-19 hospitalization. The correlation analysis for the data suggests that the wastewater virus signals are highly correlated with hospitalization rates with a time-varying relationship. The modeling analysis suggests that the DLNM provides reasonable estimate of Covid-19 hospitalizations and enhances our understanding of the association between wastewater viral signals and Covid-19 hospitalizations. SARS-CoV N1 and SARS-CoV N2 gene concentrations in wastewater seem promising surrogate markers for Covid-19 infection at the population level.

Ken Peng is a Ph.D. candidate majoring in Statistics at Simon Fraser University. Ken’s research interests are in Biostatistics, Bayesian statistics, and sports analytics. He is currently working on some wastewater/Covid-19 related research with Dr. Charmaine Dean’s lab at the University of Waterloo.