COIOS
Weighing data and models Item new preprint

Gaussian process models fill gaps in Canada's patchy measles vaccine coverage data

A Canadian preprint uses Gaussian process models to impute missing measles vaccine coverage by province, territory and age, creating a complete modelled coverage dataset for outbreak response work and testing the approach against England's more complete records.

Canadian measles vaccine coverage data are collected cross-sectionally and are missing for many province, dose and year combinations, against roughly 1,100 reported cases between January and June 2026 compared with a previous annual average below 200. The authors curated a standardised coverage dataset from public sources and used Gaussian process models to impute first-dose coverage by province, territory and age, validating the approach against England's more complete data.

Why it is interesting: Shows how susceptibility inputs for outbreak models are constructed when coverage registries are incomplete, and how much of the immunity landscape is imputed rather than observed.

Source
medRxiv, 23 September 2026
DOI
10.64898/2026.07.16.26358151
Type
Preprint
Design
Methods preprint: Gaussian process imputation of measles vaccine coverage by province/territory and age, curated from public Canadian datasets, validated against English coverage data
Verdict
New finding
Driver
Forecasts and their record
Driver
Infection and pandemics