An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada.

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.

Waiting for liver transplantation in Canada: waitlist history 2000--2004 and sensitivity analysis for the future.

This study comprises a historical review of liver transplants performed in Canada during 2000--2004, and sensitivity analyses to assess the impact of possible changes in the operation of the waitlists. In the first part, overall statistics are reported, and the notable impact that blood type plays in affecting waiting time is discussed. Waiting times and numbers of transplants are also reported by gender, age, and geographic region (waitlist), and statistical analyses of the patient placement and cadaveric donations processes are performed. These analyses establish that the service times of an appropriate queuing model are closely approximated by an exponential distribution. Consequently, the resulting distribution for the waiting time from placement until transplant is well described by a different exponential distribution. The GI/M/1 queuing model is then used to perform a number of sensitivity analyses. The sensitivity analyses attempt to quantify the impact of no change in policy via a lottery system, and likely increases in cadaveric and/or living donor sources that would be needed to bring stability to the system. The results can be used by relevant authorities, together with information on success rates for publicity campaigns and living donor matching campaigns, to assess where further efforts should be made to reduce waiting times.