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# Gaussian process models fill gaps in Canada's patchy measles vaccine coverage data
- URL: https://www.coios.me/gaussian-process-models-fill-gaps-in-canada-s-patchy/
- Published: 2026-09-26T09:31:07.000Z
- Updated: 2026-09-26T10:36:07.000Z
- Description: 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.
- Author: Daniel Ryan
- Tags: Weighing data and models, Forecasts and their record, Infection and pandemics, Canada, United Kingdom, #preprint, #new, #item, #source-2026-09

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](https://doi.org/10.64898/2026.07.16.26358151?ref=coios.me)

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](https://www.coios.me/d-forecasting/)

Driver

[Infection and pandemics](https://www.coios.me/d-infectious-disease/)