On Balance: Integrating Economics and Epidemiology in the COVID-19 Context (2 of 4)
One of the most popular sessions at the SBCA 2021 Annual Conference was on combining economics and epidemiology to understand COVID-19. Session speaker Natalie Dean shares a brief statement below.
Past studies of Ebola, HIV, dengue, and Zika by infectious disease epidemiologists provide a road map for the use of outbreak and contact tracing data to estimate transmission parameters for application in mathematical models. There are several primary goals for modeling efforts in this context.
First, we use models to conduct estimation and inference. Early on in the pandemic, this included estimating the basic reproduction number and the infection fatality ratio. Model-based estimation formalizes our assumptions about what is observed and what, importantly, is missing. Another major goal is to explore different strategies and their performance across scenarios. We can use modeling to explore how sensitive the optimal strategy is to uncertainty, given our best understanding of the epidemiology. Finally, we use models to make projections or forecasts. This is what people often think of with infectious disease modeling, even though it is just one piece of the field. It is important that modelers communicate the limitations of these projections clearly, and that consumers do not become overly reliant on a single model. Modelers must also embed continuous checks to evaluate real-time performance of projections. The results of models can be valuable inputs to decision-makers, but of course the ultimate decision relies on many more sources and a broader value judgment. Infectious disease experts could ideally work closely with economists, but infectious disease experts during a pandemic suffer from a lack of bandwidth. Making mathematical models open source and easily accessible could enable outside groups to benefit from the epidemiological insights and apply the models to interdisciplinary research.