| epi base package for SyncroSim Resource1 #685193 A scenario-based modelling framework for generating locally relevant forecasts of COVID-19 |
- 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. [2]
- 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. [2]
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