Prediction of subnational-level vaccination coverage estimates using routine surveillance data and survey data
The findings suggest that routinely collected measles surveillance data can effectively predict subnational measles vaccination coverage. The approach addresses limitations of existing methods by providing yearly estimates that are more accurate than administrative data and more readily available than surveys. This enables timely identification of low-coverage areas and facilitates targeted interventions.
Authors
- Deepit Bhatia (ORCID: https://orcid.org/0000-0001-8168-6873)
- Anna A. Minta
- M. Carolina Danovaro‐Holliday (ORCID: https://orcid.org/0000-0001-7324-9198)
- Natasha S. Crowcroft (ORCID: https://orcid.org/0000-0002-6761-8783)
- Matthew J. Ferrari (ORCID: https://orcid.org/0000-0001-5251-8168)
- Sébastien Antoni (ORCID: https://orcid.org/0000-0002-0911-0711)
- Anindya Sekhar Bose (ORCID: https://orcid.org/0000-0002-7657-8648)
- Balcha Masresha (ORCID: https://orcid.org/0000-0003-3028-887X)
Institutions
- Pennsylvania State University (US)
- World Health Organization (CH)
- World Health Organization Regional Office for Africa (CG)
Publication Details
- Journal
- Vaccine
- Published
- 2025-05-27
- DOI
- https://doi.org/10.1016/j.vaccine.2025.127277
- Citations
- 1
- Primary Topic
- Vaccine Coverage and Hesitancy
- Type
- article
- Field-Weighted Citation Impact
- 2.17
Funders
- Bill and Melinda Gates Foundation
- Division of Environmental Biology