Abstract

As we have seen from the COVID-19 pandemic, data on an Emerging Infectious Disease (EID) epidemic available during a pandemic is bound to be incomplete and biased. The proportion of infected individuals who are tested varies over time, geographically and by ethnic group, and by age and sex. Obtaining an accurate picture of the extent of an EID in the population, and the various sub-populations which are most severely affected, requires complex modelling of existing data sources and innovative data collection strategies. Even with the highest-quality and most representative data available the need to account for incompleteness and bias in the data and uncertainty in the eventual results is central to any analysis. Advanced biostatistical methods, both existing and as of yet undeveloped, will be an essential component of our toolbox for managing future EID’s and for understanding the COVID-19 pandemic itself.

This session assembles four of Canada’s top Biostatistical researchers with expertise and experience in methods for infectious disease epidemiology working in a variety of areas including: individual-level modelling of epidemics; seroprevalence studies; disease mapping and spatial statistics; using Bayesian evidence synthesis to estimate infection fatality rates from biased data.