MIDAS Cooperative Agreements: Research Projects U01

​​​​​By Rich Haddock
  • Development and Dissemination of Operational Real-Time Respiratory Virus Forecast


    Principal Investigator: Jeffrey Shaman, Ph.D., Columbia University Mailman School of Public Health

    This research group is expanding the real-time flu forecasting system that built using methods from weather and climate modeling and applying it to additional infectious diseases, including other respiratory viruses and Ebola.

  • Dynamic Data-Driven Decision Models for Infectious Disease Control



    Principal Investigators: Alison Galvani, Ph.D., Yale University; Lauren Meyers, Ph.D., of Texas at Austin


    This research team is developing new models of disease spread with the goal of improving infectious disease tracking and optimizing intervention strategies.

  • Forecasting Tipping Points in Emerging and Re-Emerging Infectious Diseases






    Principal Investigators: John Drake, Ph.D., and Andrew Park, Ph.D., University of Georgia; Matthew Ferrari, Ph.D., Penn State University; Pejman Rohani, Ph.D., and Bogdan Epureanu, Ph.D., University of Michigan, Ann Arbor

    This research group focuses on developing modeling methods that could serve as an early warning system for infectious disease outbreaks.

  • Modeling Contact Investigation and Rapid Response


    Principal Investigator: Travis Porco, Ph.D., University of California, San Francisco


    This research group uses computational models to determine what kinds of information to collect from people exposed to an infectious disease and how to use the data to design effective intervention strategies.

  • Modeling Epidemic Infectious Diseases Using Sequence Analysis


    Principal Investigator: Sergei Pond, Ph.D., Temple University, Philadelphia


    This research group models the rapidly evolving genetic sequences of pathogens, studies how this information can be used to better understand disease transmission networks within communities and evaluates the impact of control and intervention measures.

  • Modeling the Effects of the Environment on Enteric Pathogen Dynamics


    Principal Investigator: Joseph Eisenberg, Ph.D., University of Michigan, Ann Arbor


    This research group models the contribution of environmental factors to disease outbreaks, including the movement of pathogens through water, air and food as well as how those pathogens become transmissible.

  • Models for Synthesizing Molecular, Clinical and Epidemiological Data, and Translation





    Principal Investigators: Neil Morris Ferguson, Ph.D., Christophe Fraser, Ph.D., and Steven Riley, Ph.D., Imperial College London; Simon Cauchemez, Ph.D., Institut Pasteur, Paris

    This international research team is developing new machine-learning methods to find patterns of disease transmission; integrate large, complex datasets generated by public health efforts; and derive new ways to improve the validity and predictive ability of infectious disease models.

  • Predicting Vector-Borne Virus Transmission Dynamics and Emergence Potential


    Principal Investigator: Christopher Mores, Sc.D., Louisiana State University


    This research group develops mathematical models of the spread of mosquito-borne diseases, including dengue fever. The goal of the work is to help researchers better understand how to interrupt transmission to prevent or slow outbreaks.

  • Quantifying Model Uncertainty for Forecasting the Spread of Infectious Diseases


    Principal Investigator: Sara Del Valle, Ph.D., Los Alamos National Laboratory


    This research group studies how to model changes in people's behavior in response to an infectious disease outbreak and the impact of uncertainty in the data, such as vaccination rates or number of infected people, on infectious disease models.

  • Synthetic Information Systems for Better Informing Public Health Policymakers



    Principal Investigators : Stephen Eubank, Ph.D., Madhav Marathe, Ph.D., Biocomplexity Institute of Virginia Tech


  • This research group designs, builds and validates models of disease spread and prediction systems based on activities in a social network. Using mathematical and computational methods, the group is exploring the effects of human contact patterns in urban areas on disease transmission dynamics and the effectiveness of particular response strategies.