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Project: A scenario-based modelling framework for generating locally relevant forecasts of COVID-19

Year: 2020-2021

Focus Areas: Population Dynamics (Disease Dynamics)

Tools: SyncroSim, Epi

Project Overview:

Decisions are being made every day, at the level of individuals to countries, in response to the outbreak of COVID-19. Ideally, these decisions are informed by the best possible forecasts: the better our forecasts, the better we can prepare for the future – whether it be mobilizing health care resources to deal with the anticipated number of future infections, or understanding, in advance, the potential consequences of changes in public health measures. Scientists from around the world have been quick to respond by developing a wide range 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 innovative 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. The framework allows users to generate daily forecasts for jurisdictions ranging in size from public health units to countries, based on real-time gathering of online COVID-19 data and access to the world’s best forecasting models. Built upon the SyncroSim software platform, our framework allows end users to generate COVID-19 forecasts that are specific to their jurisdiction and questions, by mixing-and-matching datasets and models from around the world with their own local data and models. Both the data and models within the system can be updated in real-time, allowing scientists to continually adapt and improve their forecasts as COVID-19 evolves over time; furthermore, all of the forecasts generated by the system include robust assessments of uncertainty. Finally, and perhaps most importantly, our framework allows policy makers to game with their own local “what-if” scenarios regarding possible future changes to public health measures, forecasting the resulting consequences in advance of on-the-ground changes.

 

Additional Information:

Academic collaborators: Sarah (Sally) Otto, University of British Columbia; Caroline Colijn, Simon Fraser University

Covid-19 Forecasting System Framework Overview Presentation (PDF)

Photo credit: M. Spiske
Covid-19 Framework Figure

The framework for our COVID-19 forecasting system. Using our SyncroSim software, users can combine online data and models with their own local data and models to generate alternative “what-if” scenario forecasts that can be summarized internally or published online.

Project:

A scenario-based modelling framework for generating locally relevant forecasts of COVID-19

Clients: Government of Canada, Innovation for Defence Excellence and Securities (IDEaS) Program

Year: 2020-2021

Focus Areas: Population Dynamics (Disease Dynamics)

Tools: SyncroSim, Epi

 

Project Overview:

Decisions are being made every day, at the level of individuals to countries, in response to the outbreak of COVID-19. Ideally, these decisions are informed by the best possible forecasts: the better our forecasts, the better we can prepare for the future - whether it be mobilizing health care resources to deal with the anticipated number of future infections, or understanding, in advance, the potential consequences of changes in public health measures. Scientists from around the world have been quick to respond by developing a wide range 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 innovative 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. The framework allows users to generate daily forecasts for jurisdictions ranging in size from public health units to countries, based on real-time gathering of online COVID-19 data and access to the world’s best forecasting models. Built upon the SyncroSim software platform, our framework allows end users to generate COVID-19 forecasts that are specific to their jurisdiction and questions, by mixing-and-matching datasets and models from around the world with their own local data and models. Both the data and models within the system can be updated in real-time, allowing scientists to continually adapt and improve their forecasts as COVID-19 evolves over time; furthermore, all of the forecasts generated by the system include robust assessments of uncertainty. Finally, and perhaps most importantly, our framework allows policy makers to game with their own local “what-if” scenarios regarding possible future changes to public health measures, forecasting the resulting consequences in advance of on-the-ground changes.

Covid-19 Framework Figure

The framework for our COVID-19 forecasting system. Users can combine online data and models with their own data and models to generate locally-relevant forecasts that can be summarized internally and made available to the public.

Additional Information:

Academic collaborators: Sarah (Sally) Otto, University of British Columbia; Caroline Colijn, Simon Fraser University

COVID-19 Forecasting System Framework Overview Presentation (PDF)

Photo credit: M. Spiske