I am a computational scientist with a methodological focus on developing ensemble forecasting algorithms and extracting statistical information from unstructured human judgment data. The areas of application that interest me most are building tools to combine forecasting and predictive models in the health sciences.
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PhD in Mathematical Sciences, 2017
University of Vermont
MS in Biostatistics, 2010
Georgetown University
BS Biomathematics, 2009
University of Scranton
Safe, efficacious vaccines were developed to reduce the transmission of SARS-CoV-2 during the COVID-19 pandemic. But in the middle of 2020, vaccine effectiveness, safety, and the timeline for when a vaccine would be approved and distributed to the public was uncertain.
From February to May 2020, experts in the modeling of infectious disease provided quantitative predictions and estimates of trends in the emerging COVID-19 pandemic in a series of 13 surveys. Data on existing transmission patterns were sparse when the pandemic began, but experts synthesized information available to them to provide quantitative, judgment-based assessments of the current and future state of the pandemic.
To build a heart team consensus distribution, a biostatistician would ask each member of the team to independently provide a probability distribution, for example a survival curve, of an adverse outcome (blue lines) for different treatments a single patient may decide to undergo.