Reference number : 032-2020-2021-Q3-00037, amendment number : , agreement type : C, recipient legal name : Apex Resource Management Solutions Ltd., recipient country : CA, recipient province : ON, recipient city : Ottawa|Ottawa, recipient postal code : K2A3K2, program name english : Innovation for Defence Excellence and and Security, program name french : Innovation pour la defense, l'exellence et la securite, program purpose english : nan, program purpose french : nan, agreement title english : nan, agreement title french : nan, agreement number : CPCA-0233, agreement value : 197385.0, agreement start date : 2020-12-01, agreement end date : 2021-06-01, description english : Decisions are being made every day, at the level of individuals to countries, in response to the outbreak of COVID-19. 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. Without an inhouse team of modelers ready on stand-by, most policy makers are unable to direct modeling efforts towards their daily questions and circumstances. Instead they are often forced to rely on projections made for different questions and/or jurisdictions, and often outdated by the time they are released. 

Our solution to this challenge has been to develop a software framework for providing real-time forecasts of COVID-19 infections and deaths that can be rapidly deployed for use anywhere in the world. Built upon our existing SyncroSim software platform, our framework will allow end users to generate forecasts that are specific to their jurisdiction and questions. Through a collaboration with researchers from the University of British Columbia and Simon Fraser University, the framework will provide access to the best of the world’s open-source forecasting models, along with real-time daily data, in a standardized and user-friendly format. Using the latest source control techniques, scientists will be able to continually adapt and improve the framework’s underlying models as understanding of COVID-19 evolves over time. The framework will allow decision makers to assess and compare alternative model projections for local accuracy and relevance, thus building confidence over time in their forecasts. Finally, the framework provides the flexibility for policy makers to introduce their own local “what-if” scenarios regarding the effects of possible future changes to public health measures. The result is a tool that generates locally responsive, meaningful, and ultimately actionable forecasts., description french : Des décisions sont prises chaque jour, au niveau des individus aux pays, en réponse à l'épidémie de COVID-19. Les scientifiques du monde entier ont rapidement réagi en développant un large éventail de modèles mathématiques pour prédire les futures infections et décès par COVID-19. Fournir cette science aux décideurs sous une forme exploitable reste cependant un défi. Sans une équipe interne de modélisateurs prêts à intervenir, la plupart des décideurs sont incapables d'orienter les efforts de modélisation vers leurs questions et circonstances quotidiennes. Au lieu de cela, ils sont souvent contraints de se fier aux projections faites pour différentes questions et / ou juridictions, et souvent dépassés au moment où ils sont publiés., expected results english : nan, expected results french : nan, additional information english : nan, additional information french : nan, owner organization : dnd-mdn, owner organization title : National Defence | Défense nationale