Growing skepticism toward vaccines has sparked a flareup of measles outbreaks affecting New York City neighborhoods, cruise ships, international airports, and even Google’s Mountain View headquarters. To help family physicians reach out to vaccine-hesitant parents, data scientists have shown how computer models can predict the likelihood that an individual child’s parents will not get him or her vaccinated.

Since 2016, the world has witnessed a resurgence in measles cases and deaths as more people choose not to vaccinate their children—a decision that is often influenced by misinformation spread online through social media platforms such as Facebook and YouTube. By identifying families at greatest risk of not getting vaccinated, computer models could enable health officials and physicians to talk with parents at the stage when they remain undecided about vaccines.

“The reason why this could be useful is that, while it’s very hard to persuade someone once they’ve made up their mind, it might be easier if we know early enough and approach them in a friendly manner explaining why it’s important that their children be vaccinated,” says Tin Oreskovic, a data scientist at IBM's Chief Analytics Office.

To help boost vaccination rates, Oreskovic initiated and coordinated a University of Chicago Data Science for Social Good project aimed at predicting the likelihood of Croatian children getting vaccinated by the end of their first-grade school year. Working with the Croatian Institute of Public Health, researchers from France, Portugal, and the United States worked together to train machine learning algorithms on the electronic health records of 48,000 children who entered the first grade between 2011 and 2018.

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