A scenario-based modelling framework for projecting COVID-19 infections and deaths

The speed and extent of the COVID-19 pandemic has challenged our abilities, as forecasters, like never before. Early data on the disease's epidemiology is limited, records of cases and infections are incomplete, and the dynamics and scientific understanding of the disease are changing daily. Scientists from around the world have been quick to respond by developing a plethora of mathematical models to predict future COVID-19 infections and deaths. Delivering this science to decision makers in an actionable form, however, remains a challenge. Our solution to this challenge has been to develop a general software framework for providing real-time forecasts of COVID-19 infections and deaths that can be rapidly deployed for use anywhere in the world. Our framework allows end users to generate forecasts that are specific to their jurisdiction and questions. The result is a tool that generates locally responsive, meaningful, and ultimately actionable forecasts.

Intern: 
Tom Booker
Faculty Supervisor: 
Sarah (Sally) Otto
Province: 
British Columbia
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