Work Context and Industrial Composition Determine the Epidemiological Responses in a Multi-Group SIR Model

Key economic indicators, such as GDP and unemployment rates, provide a useful backdrop for assessing the state of the economy. However, more nuanced statistics can be of use during the COVID-19 pandemic. In this paper, we extend the domain of relevance for economic statistics by developing a multi-group SIR model that accounts for differences in work context and industrial composition. We show the model is useful for assessing how different economic scenarios affect the dynamics of the COVID-19 virus. Our model highlights how statistics on industry contact rates and the composition of economic output inform the dynamics of COVID-19 under different economic scenarios. Our study illustrates the importance of economic statistics for the numerical analysis of COVID-19 and describes how to use them to analyze different economic scenarios.