PURPOSE: To visualize and interactively explore how public health data can illustrate trends in the incidence and severity of COVID-19 across jurisdictions; To help participants understand current disease burden and transmission parameters; To understand how these epidemiologic parameters can inform the effectiveness of COVID-19 prevention strategies and feed in to economic models to predict the value of upcoming interventions

DESCRIPTION: Routine public health data on COVID-19 presents opportunities to not only monitor trends in infections, but also to calculate transmission parameters for epidemiologic models and for informing economic modeling of potential interventions. This workshop will review the types of data typically reported to describe COVID-19 burden, and how these vary across jurisdictions. We will explore how these data are used to inform epidemiologic models developed to understand SARS-COV2 transmission and COVID-19 burden, using real-world examples from three North American jurisdictions with differing population sizes, densities, and intervention strategies. We will present dynamic visualizations with which audience members can interact to observe how manipulating input parameters (e.g., number of new cases, disease duration, mortality, and recoveries) affects factors such as the size of the affected population and R0 (or its real-time approximation, Rt).

Our workshop will also demonstrate how surveillance data and epidemiologic parameters can feed into economic models of COVID-19. Given the multi-faceted nature of COVID-19 interventions and their widespread impact, for economic models to be useful they will need to robustly address societal costs and benefits. We will explore how graphical visualizations and epidemiologic measures, such as Rt, may inform key steps in the economic evaluation of upcoming interventions like vaccines. The costs of such interventions can be contextualized by the implications on the spread of disease. This component will be framed as a Q&A session with economic modelers, and tips and pitfalls for COVID-19 modeling will be explored.