About the Analytics and Modeling Track

The Public Health Analytics and Modeling Track is offered within the Steven M. Teustch Prevention Effectiveness Fellowship. It is a 2-year post-doctoral track with the goal of growing CDC’s capabilities around advanced analytics and infectious disease modeling.

Why Modeling?

Modeling is the application of specific predictive or scenario-based analytic methods, statistical tools, and data to public health problems. Modeling allows researchers to study problems not easily examined experimentally and problems where there are gaps in available data. Modeling can help researchers and policy-makers infer epidemiological characteristics, inform risk assessments, forecast future disease activity, inform intervention planning (such as social distancing, vaccine, or testing), or assess the impacts of interventions. Models being published in the scientific, peer-reviewed literature are becoming more and more complex and relevant to public health practice and policy. Mathematical modeling and advanced analytical approaches are diverse and include microsimulations, dynamic compartmental models, network models, Bayesian analyses, and machine learning.

The track aims to expand CDC’s capacity to employ modeling and other analytical approaches to grow our understanding of existing and emerging diseases, public health strategies, and response activities. This work helps ensure that science informs action to protect people from many health, safety, and security threats.

“As the coronavirus spread rapidly, people felt helpless, like there wasn’t much they could do. However, I had been developing these quantitative skills and wanted to use them to contribute to the virus effort. At CDC, through the Modeling Track, I can put myself in a position to have a direct impact on not only the coronavirus, but multiple diseases”

—Dr. Maile Phillips, Analytics and Modeling Track Fellow, Class of 2021

Fellows use the skills learned in this track to assist multiple CDC centers, institutes, and offices (CIOs) on projects such as forecast development and evaluation, intervention assessment, and epidemiological characterization. Fellows can expect to use these skills to satisfy the track’s performance requirements.

The inaugural class of the Modeling Track began August 2021, with a total number of 21 fellows. The majority of the fellows are assigned to the Atlanta CDC campuses, with the exception of one in Washington, D.C. and another in San Juan, Puerto Rico. The inaugural class is composed of a substantial number of women (67%), ethnic minorities (52%), and non-U.S. citizens (29%).


“Modeling—the use of modeling techniques, employing advanced analytics, and engaging big, non-traditional data—represents the future of CDC. This fellowship is a clear breakthrough in building our capacity in this important area.”

—Dr. Adam Skelton, Lead of the CDC Steven M.Teutsch Prevention Effectiveness Fellowship

Examples of Fellows’ Assignments in the Modeling Track

  • Modeling combinations of interventions to control malaria and further reduce mortality in Africa
  • Predictive modeling of successfully interrupting circulating vaccine-derived poliovirus type 2 (cVDPV2) transmission
  • Modeling effective COVID testing strategies in nursing homes
  • Modeling to understand and enhance influenza vaccination
  • Modeling the impact of human movement on dengue
  • Mathematical modeling to predict geographic scope, temporal periodicity, and intensity scale of pertussis outbreaks at local levels.