As we have seen from the COVID-19 pandemic, the development of statistical methods for analyzing messy data which accompanies the emergence of an infectious disease epidemic is an area of vital importance for society. In this session, we propose to have four speakers at various stages of their career, who are working on the cutting edge of different methodological advances in this research area. 

These are, along with tentative talk titles:

Patrick Brown (University of Toronto) – Statistical issues in estimating seroprevalence

Cindy Feng (Dalhousie University) – Spatial-temporal modelling of COVID-19 mortality risk in Toronto, Canada

Raja Ben Hajria (University of Calgary) – Individual-level hidden Markov models of epidemics

Harlan Campbell (University of British Columbia) – Inferring the COVID-19 infection fatality rate in the community-dwelling population via Bayesian evidence synthesis

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.

Organiser: Prof. Rob Deardon 

Chair: Prof. Rob Deardon 

Speaker: Dr Patrick Brown 

Speaker: Cindy Feng 

Speaker: Rob Deardon

Speaker: Harlan Campbell