Near Real-Time Forecasting of Epidemics Using Data Assimilation with Simple Models
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PublisherThe University of Arizona.
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AbstractWith the advent of expedient data sharing of epidemiological reports, efforts have been made to forecast ongoing epidemics in real-time. Throughout this work, we develop a real-time forecasting framework that can be applied to a variety of diseases. Specifically this framework pairs data assimilation with a simple mechanistic model in the form of an incidence vs cumulative cases (ICC) curve. We provide results in two different contexts: seasonal influenza and COVID-19. We demonstrate that the flexibility in this framework results in accurate forecasts even when disease dynamics change during the outbreak, such as was the case with COVID-19. We further provide the groundwork for an avenue of improvement of the flu forecasts by using epidemiological data available early on in the flu season to categorize the severity of the ongoing season. The presented framework can be applied to other diseases, locations, or population scales.
Degree ProgramGraduate College