At a glance
Model & tool development
This interactive tool was developed to support the 2025 measles response. The underlying model is based on the latest information from the literature on parameters that influence transmission of measles and input from CDC subject-matter experts. The model has both been reviewed by internal measles and modeling experts and shared with external experts for their individual input. We will continue to update and improve the tool as more information becomes available, and in support of ongoing response needs.
Resources
- The code for the tool can be found in .
- The key takeaways from the tool can be found in this downloadable summary.
- More detailed methods, results, and takeaways can be found in our Behind the Model - Interactive Measles Outbreak Simulator.
FAQs
Estimate the number of people vaccinated with MMR (1 or 2 doses) and the number of people previously infected with measles. Divide this by the population size. The model will account for the vaccine effectiveness (see VE of MMR for measles), and assume 3% (e.g., 100% - 97%) of those labeled as "immune" at baseline are still susceptible.
Based on observations from field investigations, if a person is quarantining after a potential exposure, they will also generally isolate upon rash onset. People who do not quarantine still may isolate, so in the model, adherence to isolation is always at least as high as adherence to quarantine, but may be higher.
If turned on, quarantine reduces transmission rates by 60% among infectious individuals who are adhering in the first half of their infectious periods, before they have symptoms of measles. Similarly, isolation reduces transmission rates to zero among infectious individuals who are adhering later in their infectious period, after they are symptomatic.
The vaccine is assumed to be “all or nothing”, meaning that it either confers perfect immunity or no immunity. The proportion with immunity is determined by the VE parameter.
The model calculates the rate of doses administered as the number of people expected to be vaccinated (given eligibility and the percent of eligible population who get vaccinated) divided by the duration of the vaccine campaign. If the user defines a campaign scheduled to end after the end of the simulation, then the campaign ends early (on the last day, day 365).
For example, a campaign to vaccinate 100% of eligible people in a population of 100,000 people and 95% baseline immunity would theoretically vaccinate 4,999 people. However, if this campaign is planned to start on day 180 in the model and last for 365 days, only approximately half (2,534) of those people will actually receive the vaccine during the simulation.