During an infectious disease outbreak, such as the 2014 outbreak of Ebola virus in West Africa, numerous mathematical models are created to predict the magnitude, breadth, and spatial extent of disease and to inform public health officials on how to best utilize and distribute resources. Depending on the models and the parameters of the data, they can predict vastly different outcomes. This poses a significant challenge for policy-makers to interpret and apply these tools. This research team will utilize the strengths of existing prediction models and will quantify the uncertainty in decision-making for each of the various models. The investigators will simulate alternative public health interventions using current published prediction models for the 2014 Ebola outbreak. This method will not only provide an immediate tool for the current outbreak, but it establishes a formal framework for comparing projections.
This research addresses scientific uncertainty across numerous published Ebola virus models and will evaluate the ability of each model to achieve its stated objective. The researchers will systematically identify the areas of uncertainty in the models to target key sources of disagreement in management recommendations. This will be done using value of information theory and applying value of perfect information analysis. The research team plans to expand the scope of their work to include new Ebola models as they become published. The results will not select or produce a single ?best? model, but will rather articulate and reduce the uncertainty that impedes success in each of the models.
|Effective start/end date||12/15/14 → 11/30/17|
- National Science Foundation: $200,000.00