The Computational
Epidemiology Lab
The Computational Epidemiology Lab develops and applies mathematical, statistical, and computational approaches to study infectious disease dynamics. Our work spans modeling, simulation, and data-driven analyses to inform surveillance, prevention, and public health decision-making in the United States. Our interdisciplinary team leverages these methods to generate actionable insights that guide public health interventions.
Johns Hopkins Bloomberg School of Public Health & School of Medicine
US Research Network
In addition to national-level modeling, our models span 32 metropolitan areas most affected by the highest burden of HIV and syphilis epidemics
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Ryan White HIV/AIDS Program Evaluation
Our JHEEM model, informed by surveys of 180 clinic directors across 31 US cities, projected that ending Ryan White services could increase HIV infections by 49% over five years. This research provides critical evidence for policy decisions about essential HIV care programs.
Additional Infections
If Programs End
Projected Increase