Marisa Eisenberg

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  Connecting models with data: identifiability and parameter estimation of multiple transmission pathways
  Marisa Eisenberg

  Connecting dynamic models with data to yield predictive results often requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what is possible to estimate from a given model and data set, and help guide new data collection. In this talk, we will discuss approaches to both structural and practical identifiability analysis. Using a range of examples from cholera and the West Africa Ebola epidemic, we illustrate some of the potential difficulties in estimating the relative contributions of different transmission pathways, and show how alternative data collection may help resolve unidentifiability. We also illustrate how even in the presence of large uncertainties in the data and model parameters, it may still be possible to successfully forecast disease dynamics.

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  Identifiability and interacting scales in modeling disease dynamics
  Marisa Eisenberg

  Disease processes often involve interacting factors at multiple scales, which can affect both how we build models of these systems and the data sets needed to estimate model parameters. In this talk I will discuss some examples of disease transmission models that depend on processes at scales ranging from cellular to environmental, including cholera and human papillomavirus (HPV).