Statistical Methods for Managing Emerging Infectious Diseases

This project assembles the top biostatisticians in Canada working on infectious diseases, and joins them with epidemiologists developing novel methods for data collection during the COVID-19 pandemic. Our group is developing methods and tools to get an accurate picture of the nature and extent of infectious disease transmission in the population, relying on real-world data from administrative sources and surveys. The project is funded by the Natural Sciences and Engineering Research Council of Canada and the Public Health Agency of Canada.

Program description

Statistical Methods for Managing Emerging Infectious Diseases (SMMEID) is a project recently funded by the NSERC Emerging Infectious Disease Modeling (EIDM) program. Postdoctoral fellowships for Biostatisticians and specialists in related disciplines are available for projects at multiple Canadian universities, and are listed below.  The successful candidates will be co-supervised by at least two of the SMMEID investigators, and collaboration and interactions amongst the SMMEID postdocs will be encouraged (and expected).  In particular, software tools developed by the various sub-projects will be interoperable and where possible use a common set of coding standards. There will be opportunities for trainees to work with peers at other sites.

Salaries will depend on qualifications and policies at the host institution, with an option of additional income from teaching available in most circumstances.  The positions will commence as soon as possible, and are one year positions in the first instance with possible renewal for a second year subject to progress on the research.  Initially the work will happen remotely with relocation to the host institutions being expected as and when the situation permits. 

Applicants may apply for a single position, or for multiple positions ranked in order of preference.  Screening of applications will begin immediately and will continue until the position is filled. All applicants are thanked in advance; only those selected for further consideration will receive information about the interview process.  Several additional postdoctoral fellowships unrelated to the SMMEID project, but working in the research labs of the SMMEID investigators, are listed and can be applied for as part of this process.

The investigators of the SMMEID project are sensitive to the challenges faced by new researchers in Biostatistics from non-traditional backgrounds and marginalized communities, and are committed to providing a welcoming, respectful, and healthy work environment.  Any special circumstances affecting an applicant’s academic record or requests for accommodation for a personal situation can be stated in an application letter and will be carefully considered.  Preference must be given to applicants who currently have permission to work in Canada, although qualified applicants who would require work permits are also eligible and encouraged to apply.

Individual descriptions

Imperfect testing and serosurveys

Grace Y. Yi, Western,

Paul Gustafson, UBC,

We are seeking a postdoctoral fellow to be based in the Department of Statistical and Actuarial Sciences at Western University, under the joint supervision of Professor Grace Y. Yi (Western University) and Professor Paul Gustafson (University of British Columbia – Vancouver).   Specific projects in this particular node of the network involve developing statistical methods for analyzing infectious disease data, both in the context of the COVID-19 pandemic and other contexts.  Goals include addressing  various challenging issues associated with noisy data, including heterogeneity, varying incubation times, reporting errors, imperfect test specificity and sensitivity, and missing values.  The successful candidate is also expected to apply the developed methods to Canadian sero-survey data, as well as data from international studies.  This applied component of the work will afford opportunities to interact with heath scientists from varying disciplinary backgrounds.

COVID mortality forecasting

Patrick Brown, Toronto

A postdoctoral fellow is required for developing a forecasting tool for COVID-19 mortality.  An existing forecasting model is made up of multiple waves, each of which follows the shape of a skew-normal density function, with model fitting done with Bayesian MCMC.   The challenge working with this model is there is a large number of latent variables which are not Gaussian. The model will be extended to make spatio-temporal forecasts, and improved algorithms for handling waves of infections must be built.  Methods for handling censored, aggregated, and age-specific death counts are also required.  The project will involve producing tools and visualizations for use by decision makers and health professionals, in collaboration with partners in these areas, as well as manuscripts for Statistical journals.

The successful applicant will have good computational skills and knowledge of MCMC methods.  An interest in web-based visualizations is an asset. 

Modelling emerging infectious disease

Ed Susko, Dalhousie

Lam Ho, Dalhousie

We are seeking a postdoctoral fellow who will work with Drs. Lam Ho and Edward Susko to develop statistical methods for stochastic models of infectious disease epidemics. The project will primarily focus on stochastic compartmental models and their applications in phylodynamics. A fundamental challenge for stochastic compartmental models is that the likelihood function is intractable. Our goal is to develop efficient methods for making inferences under these models. 

Location: Department of Mathematics and Statistics, Dalhousie University, Halifax, NS


– PhD in statistics or related fields.

– Good communication skills in English.

– Strong background in stochastic models and statistical inference.

– Expertise in R and C/C++ is highly desirable.

Estimating COVID-19 prevalence in homeless populations

Gracia Dong, with Laura Cowen and Patrick Brown

Leveraging models typically used in ecological studies with data extracts from electronic healthcare records, I estimate the yearly population size of hidden human populations, such as the homeless. Specifically, we look at the size of the homeless population on Vancouver Island. This methodology can be extended to other at-risk and vulnerable populations as well, such as those at risk of opioid overdose. Extensions to this project include infectious disease modelling within these vulnerable populations, such as estimating COVID-19 prevalence in homeless populations.

This project is done in collaboration with the Vancouver Island Health Authority in British Columbia.

Hidden Markov Individual-level Models of Disease Transmission

Rob Deardon, Calgary

Alexandra Schmidt, McGill

Applications are invited for a postdoctoral fellowship in statistical methods for infectious disease modelling. 

Specifically, the successful candidate will develop a hidden Markov modelling framework for individual-level models of disease transmission. Such an approach allows for the incorporation of individual-level covariates, including spatial or network-based distance, as well as delayed and under-reporting of cases. Models developed will be used to gain a greater understanding of the epidemiology of diseases such as COVID-19, influenza and Ebola.

The position will be based at the Department of Mathematics and Statistics at the University of Calgary, Calgary, Alberta and will be under the supervision of Drs. Rob Deardon and Alexandra Schmidt (McGill).

The successful applicant will have a PhD in statistics, mathematical biology, theoretical ecology or a related field. They will have strong skills in statistical modelling and computing. Some experience with disease modelling, Bayesian model implementation and/or hidden Markov/state space modelling is desirable.  We are committed to enhancing equity and diversity and encourage members of equity seeking groups to apply.

Feel free to contact Dr. Deardon ( if you have any enquiries about the position. 

Daily air pollution and mortality

Patrick Brown, Toronto

Applications for this position are being handled by the same process as those above, although it is not funded by the Emerging Infectious Diseases network.

In collaboration with Health Canada, a team in Dr. Brown’s lab is studying the short-term effects of ambient air pollution on daily mortality in Canada.  Data are available from over 100 regions of Canada as daily mortality counts covering a time period of nearly 30 years.  Air pollution data is modelled hourly over the same time period.  The core of the method is the case-crossover model, a type of partial likelihood where air quality on each death day is compared to air quality on a number of previous control days.  This postdoctoral fellowship will extend the methodology by allowing for multiple pollutants, at different time lags, with non-linear effects.  The ultimate goal of the project is to create a new air quality warning system for Canada.

In addition to statistical methods development to be published in Statistics journals, the job will involve collaborating with health scientists on manuscripts for medical journals and taking the lead on preparing regular reports for Health Canada.  The methods will also be used with the Centre for Global Health Research’s data holdings of mortality in India, the US, and elsewhere.  The successful candidate will be joining a team which aims to become the foremost authority on statistical methods for daily mortality data.  Strong computational skills, knowledge of random effects models and survival data, and a desire for interdisciplinary collaborative work are essential.

Small-area estimation and serosurveys

Mahmoud Torabi, Manitoba

Applications are invited for a postdoctoral fellowship in biostatistics, with specific emphasis on correlated data (mixed models) with applications in infectious diseases. In particular, the successful candidate will develop novel statistical models for infectious diseases to address some challenges in terms of prevalence rate of infection in different small areas and domains in Canada due to COVID-19 pandemic. The position will be based at the Department of Community Health Sciences at the University of Manitoba, and will be under the supervision of Professor Mahmoud Torabi.

The successful applicant will have a PhD in Bio/statistics, or a related field. They will have strong skills in statistical modeling and computing, and a demonstrated record of research.

Feel free to contact Professor Torabi ( if you have any enquiries about the position.

Spatial-Temporal Modeling of COVID-19 Adverse Outcomes in Canada

Cindy Feng, Dalhousie

Patrick Brown, Toronto

Applications are invited for a postdoctoral fellowship for developing spatial-temporal models for modeling geo-referenced COVID-19 adverse outcomes in Canada. The aims of the research are to (1) identify localised risk factors for increased severe conditions for COVID-19 (2) identify localised areas in space–time at significantly higher risk, and (3) quantify the impact of changes in localised restriction policies on adverse outcomes and forecast the epidemic.

The position will be based at the Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University and will be under the supervision of Dr. Cindy Feng and Dr. Patrick Brown.