Project 458825

Infectious disease epidemics pose significant threats to the health of Canadians and the economy. Efficient control of outbreaks relies on timely epidemic intelligence, such as the effective reproduction number (Rt). The latter represents the expected number of secondary cases arising from a primary case infected at a given time. In the ongoing COVID-19 pandemic, Rt has been playing a key role in guiding decision-making. To track and "now-cast" the epidemic in near real-time, models estimating Rt have been developed following best practices. However, some limitations are recognized. First, current models have difficulty accounting for potential temporal changes in the infectivity profile of individuals (i.e., the generation interval). Second, some infections have yet to be observed (i.e., right truncation of events) and reporting delays (i.e., bottlenecks in testing capacity and reporting) make real-time estimation complicated. Finally, it is intricate to calculate Rt by age groups or by settings (e.g., schools, hospitals) despite the importance of such metrics for decision-making. Without lifelong immunity, COVID-19 is likely to become seasonally endemic, even with vaccines and potential boosters. As such, monitoring SARS-CoV-2 transmission will remain a priority in the coming years, and addressing the knowledge gaps is important. For this research, I propose to systematically review existing methods for Rt estimation, develop new tools to incorporate and adjust for the issues highlighted above, and build new models to enable the estimation of Rt by age and setting. Addressing these critical gaps will lead to a more nuanced understanding of the effectiveness of interventions and the prompt identification of outbreaks. It will allow for prioritizing the most vulnerable groups when implementing public health policies and allocating scarce resources. These methods could also be adapted to other infectious diseases threats such as seasonal influenza, SARS, and others.