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Genomic tools for post-elimination measles molecular epidemiology using Canadian surveillance data from 2018-2020
Joanne Hiebert1, Vanessa Zubach1, Helene Schulz1
1Measles, Mumps and Rubella Unit, National Microbiology Laboratory Branch, Public Health Agency of Canada, JC Wilt Infectious Diseases Research Centre, Winnipeg, MB, Canada.
Frontiers in Microbiology
|December 4, 2024
Summary
Whole genome sequencing (WGS-t) of measles virus (MeV) enhanced outbreak investigations in Canada. This advanced sequencing confirmed importations and identified previously unrecognized outbreaks, improving measles surveillance.
Area of Science:
- Virology and Molecular Epidemiology
- Infectious Disease Surveillance
- Public Health Genomics
Background:
- Measles virus (MeV) elimination requires robust surveillance, including tracking of circulating strains.
- Standardized sequencing (N450) is insufficient for comprehensive strain characterization due to reduced genotype diversity.
- Enhanced genomic data is crucial to corroborate epidemiological findings and identify transmission gaps.
Purpose of the Study:
- To evaluate the utility of MeV sequencing tools, including whole genome sequencing (WGS-t), for measles outbreak investigations in Canada.
- To compare genomic data with traditional epidemiological investigations for improved accuracy and completeness.
- To identify previously unrecognized measles outbreaks and transmission chains.
Main Methods:
- Application of MeV sequencing tools: N450, MF-NCR, and WGS-t to clinical specimens from measles cases over three years in Canada.
- Systematic analysis of sequence data, including Bayesian evolutionary analysis by sampling trees (BEAST) of WGS-t.
- Integration of genomic findings with epidemiological data for outbreak investigation.
Main Results:
- WGS-t and other sequencing methods successfully obtained data from a significant proportion of reported measles cases.
- Bayesian analysis confirmed repeated importations of dominant MeV lineages (B3 and D8).
- Genomic analysis corroborated 13 of 16 outbreaks, revealed expansion of two outbreaks, and identified three previously unrecognized outbreaks, with one unresolved due to lack of WGS-t data.
Conclusions:
- Measles WGS-t data significantly corroborates and expands upon traditional epidemiological outbreak investigations.
- Combining WGS-t with epidemiological data is essential for comprehensive measles outbreak investigations, particularly in elimination settings.
- Enhanced genomic surveillance strengthens the ability to detect and respond to measles transmission events.
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