Centerwide Programs

The lead institution is the Fred Hutchinson Cancer Research Center (FHCRC) in Seattle, led by Elizabeth Halloran.

The overall objective of this multi-institutional MIDAS Center of Excellence is to provide national and international leadership in computational, statistical, and mathematical research, education and outreach, and public health policy related to infectious diseases and interventions. 

Centerwide programs include:

  • Training, Outreach, and Diversity plans
  • Annual Workshops
  • Annual Symposia
  • Visitors Program
  • Seminar Speakers Program


Modeling, Spatial, Statistics

Lead: Prof. Ira Longini, University of Florida

Objective: to develop, validate, and implement mathematical and statistical models for the transmission of naturally occurring infectious diseases and bioterrorism agents. 


Dynamic Inference

Lead: Prof. Pejman Rohani, University of Georgia

Objective: development of cutting-edge statistical inference methods and their application to high-dimensional disease incidence data.


Understanding Transmission with Integrated Genetic and Epidemiologic Inference

Co-Leads: Dr. Eben Kenah, University of Florida; Dr. Trevor Bedford, FHCRC

Objective: to develop, implement and validate novel methods to perform joint inference using combined epidemiologic and genetic data. 


Dynamics and Evolution of Influenza Strain Variation 

Lead: Prof. Rustom Antia, Emory

Objective: to understand how diseases of public health relevance exhibit continual evolution by generating new strains which change key regions (epitopes) recognized by the immune system, allowing these new strains to infect some of the individuals with immunity to previous strains. 


Policy Studies

Lead: Prof. James Koopman, University of Michigan

Objective: to improve communication and understanding between key modelers so they can work more effectively together.


Software Development and Core Facilities

Lead: Prof. Alessandro Vespignani, Northeastern University

Objective: to develop a multi-scale Modeling Computational Platform (MCP) for Infectious disease simulations; to develop new features and GUIs for statistical tools developed by our research groups.