Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO)2023-07-18T14:51:34-04:00

Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO)

About us

CAMEO (CoVaRR-Net’s Computational Analysis, Modelling and Evolutionary Outcomes team)’s goal is to use computer modelling and simulations to evaluate the genetic evolution of SARS-CoV-2 variants and how quickly they propagate in the Canadian population. We also help to flag variants of Canadian origin, highlight current variants of interest, and investigate differences in selection acting upon various lineages among regions of the country. In addition, our group works to address emerging bioinformatic and computational tool needs.

CAMEO is composed of academic researchers and members of the Public Health Agency of Canada (PHAC). We collaborate with PHAC, the Canadian Public Health Laboratory Network (CPHLN), and academic researchers with diverse subject expertise from across the country. Our members conduct analyses on SARS-CoV-2 genome sequences and wastewater data from various jurisdictions.

Our Vision

CAMEO aims to achieve a unified Canadian network for the detection, molecular characterization, and epidemiological modelling of emerging pathogens within our borders. Our vision is to make genomic epidemiology actionable in real time, for the benefit of the research community and public health practitioners. Our team strives to build relationships, computational tools, and data standards, as well as train highly qualified personnel to realize this vision.

Policy guidance

In this presentation to the National Advisory Committee on Immunization (NACI) in March 2023, Dr. Sarah Otto summarized recent CAMEO work highlighting how SARS-CoV-2 variants have been evolving in Canada. While BQ.* variant lineages dominated in early 2023, the XBB.1.5 variant has been rising rapidly in frequency and now comprises the majority of COVID-19 cases.

XBB.1.5 is a member of the XBB* family of lineages that arose by recombination from two separate BA.2 lineages (“X” in the start of a lineage name refers to a recombined lineage). Lab studies have shown that the family of BQ.* and XBB* lineages are more immune evasive, but the fast spread of XBB.1.5 is thought to be due to its greater ability to bind to our cells’ ACE2 receptors, leading to higher transmissibility.

At present, Canada-wide immunity is high, so these variants no longer lead to big waves, but they do lead to wavelets and, more importantly, to long term rises in the “sea-level” of COVID-19 cases.  Dr. Otto explores models to show how variants are expected to influence future case numbers and how measures that we take can counteract the rising sea-level of COVID-19 (e.g., through vaccinations, improved ventilation, and masking).

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Current Projects

1. Mathematical and Phylogenetic Approaches to Characterize Variants

We are continuously analyzing SARS-CoV-2 genomic sequence data to identify and characterize the epidemiology of new and existing variants. These activities allow us to predict the variants’ rate of spread and their potential impact on health care systems across the country. We communicate this information across CoVaRR-Net pillars and to PHAC to assist in public health decision-making.

2. Duotang

Duotang is a continually updated web-based notebook that contains genomic epidemiology and mathematical modelling analyses. Its purpose is to explore SARS-CoV-2 evolution in Canada with the aim of spurring further research discussion across pillars, supporting discussions with public health authorities, and sharing with the science communication team for eventual public dissemination. The data for Duotang is compiled from the Canadian VirusSeq Data Portal and DNAStack’s Viral AI.

3. Data Sharing

CAMEO is committed to working with the Canadian VirusSeq Data Portal to ensure that Canadian SARS-CoV-2 genome sequences generated by the Canadian Public Health Laboratory Network (CPHLN) and regional health authorities are publicly available without restriction to encourage data re-use for scientific discovery and innovation. We will work with the VirusSeq Data Portal to explore more flexible and comprehensive ways of sharing contextual data in a trusted environment. This will be done by building on the Canadian COVID Genomics Network (CanCOGeN) and CHARGES (Canadian public Health Alliance for Research in Genomic Epidemiology and Surveillance – a CPHLN-led consortium to implement genomic based infectious disease surveillance) efforts to standardize and harmonize contextual information critical for genomics analysis. Our collaboration is focused on ensuring the Data Portal is as user-friendly as possible, while working to add features that are useful to researchers seeking to analyze the data in the Portal. We are also working towards supporting the analyses and visualization of wastewater data from across the country and national priority pathogens.


COVID-MVP is an interactive heatmap-based visualization tool for SARS-CoV-2.

It tracks the prevalence of SARS-CoV-2 mutations, offering annotations of the functional impact of these mutations in variants of concern (VOCs), variants of interest (VOIs), and user-defined subpopulations in near-real time.

COVID-MVP visualization is powered through an independent, scalable and reproducible genomics workflow using functional annotations manually curated in Pokay.

Pokay is also maintained by our team and is regularly updated by curating new functions from the literature.

5. SARS-CoV-2 Evolution in Animal Reservoirs

We are in the process of analyzing about 2000 SARS-CoV-2 genome sequences of animal origin and inferring their phylogenetic relationships with roughly 2000 matched human sequences. The number of animal-to-human transmission events that have happened during the pandemic will then be estimated, and a genome-wide association study will be conducted to identify any animal-associated mutations. We will then develop methods to track these mutations in wastewater.

6. Environmental Monitoring and Sequencing

In collaboration with the CUBE project and the CoVaRR-Net Wastewater Surveillance Research Group (WWSRG), we are working to develop, benchmark and apply methods to track SARS-CoV-2 mutations and variants in the built environment and in wastewater. These data streams are increasingly important in contexts with limited clinical sampling, and now is the time to validate and compare them to clinical sequence data while it is still available. Working with environmental mixtures of viruses, often at low biomass, poses computational challenges which we are working to address, and modelling how these data types track with cases and hospitalizations is also an active area of research.


Sarah (Sally) Otto

Sarah (Sally) Otto

Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO) Pillar Co-Lead

Killam University Professor and Canada Research Chair, University of British Columbia

Jesse Shapiro

Jesse Shapiro

Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO) Pillar Co-Lead
Member, Wastewater Surveillance Research Group

Associate Professor, McGill University & Genome Center

Fiona Brinkman


Distinguished Professor, Simon Fraser University

Caroline Colijn

Caroline Colijn


Professor, Simon Fraser University
Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health

Jörg H. Fritz

Jörg H. Fritz


Associate Professor, McGill University


Zohaib Anwar
Simon Fraser University

Erin Gill
Pillar Lead Coordinator

Paul Gordon
University of Calgary

William Hsiao
Simon Fraser University

Julie Hussin 
Université de Montréal

Rees Kassen
University of Ottawa

Justin (Baofeng) Jia
Pillar Co-Coordinator

Jeffery Joy
University of British Colombia

Carmen Lia Murall
Public Health Agency of Canada

Abayomi Olabode
Western University

Art Poon
Western University

Steven Sutcliffe
McGill University

Funded Research Results

Modelling Projections

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