New Model Predicts Measles Vaccination Coverage Using Routine Clinical Data

Accurate, timely data on vaccination coverage is critical for managing measles outbreaks, yet many regions lack up-to-date information. A collaborative team from Penn State and the World Health Organization (WHO) has created a novel method to predict measles vaccination levels using routinely collected clinical data from suspected measles cases. Published in Vaccine, the model offers a more accessible and rapid alternative to traditional surveys, which are expensive, infrequent, or prone to bias.

Conventional vaccination data comes primarily from Demographic and Health Surveys (DHS)—considered the gold standard but conducted only every 3 to 5 years—and from administrative coverage estimates based on vaccine doses administered. The DHS surveys are costly and often outdated by the time results are available, especially in low- and middle-income countries where measles impacts are greatest.

The new method leverages three clinical indicators: the mean age of patients presenting with suspected measles, their reported vaccination status, and confirmation of actual measles infection versus other illnesses. Using these predictors, the researchers trained a regression model to estimate vaccination coverage, showing strong correlation with DHS data while outperforming administrative estimates.

“Since our method uses routinely collected information that is readily available to researchers and public health officials, it provides a cheap and more easily accessible methodology to estimate vaccination coverage for a region that can be done quickly and can help inform policy in a timelier way,” said Deepit Bhatia, first author and Penn State graduate student.

With DHS funding currently paused, this tool offers a timely stopgap for tracking vaccination coverage and guiding interventions to prevent outbreaks. The work was supported by the Bill & Melinda Gates Foundation and multiple US federal agencies.

Reference:

Bhatia D, Crowcroft N, Antoni S, et al. Prediction of subnational-level vaccination coverage estimates using routine surveillance data and survey data. Vaccine. 2025;60:127277. doi:10.1016/j.vaccine.2025.127277