The Yale School of Public Health (YSPH) has some of the most influential modelers in the world. In addition to establishing the Public Health Modeling Concentration for MPH students, recent research developments, include a novel way to better analyze the impact of vaccines, using a statistical method initially developed by Google. The study research was approved on Jan 4 and published in the Proceedings of the National Academy of Sciences.
Pneumococcus, a bacterial pathogen, is one of the most significant causes of pneumonia around the world. According to the Centers for Disease Control and Prevention (CDC), pneumonia is the leading cause of death globally in children under the age of 5. Vaccines that prevent pneumococcal infection can decrease pneumonia rates, but quantifying the impact of the vaccine remains challenging.
A team led by Daniel Weinberger, assistant professor in the department of epidemiology of microbial diseases, used a method called “synthetic controls,” which was not previously applied in an epidemiology context, to analyze the impact of the pneumococcal vaccine. Created by Google to analyze web traffic, the method allowed the team to separate changes in pneumonia rates caused by the vaccine from other unrelated factors, providing a clearer picture of the vaccine’s impact.
The idea to use a method from outside the field of public health to analyze vaccine impact arose from a meeting Weinberger attended at the World Health Organization (WHO). At the meeting, “there was a discussion of how to adjust for changes in data that are unrelated to the vaccine,” he said. To accomplish that, “we felt we had to look outside the typical toolbox we were using.”
The team began to explore approaches used to analyze data in other fields, including economics and web analytics, and discovered a paper on Google’s method of synthetic controls. They determined the method could be applicable to vaccine evaluation.