I present and analyze the case of COVID-19 modeling at the Public Health Agency of Sweden (FoHM) between February 2020 and May 2021. The analysis casts the case as a decision problem: modelers choose from a strategically prepared menu that model which they have reasons to believe will best serve their current purpose. Specifically, I argue that the model choice at FoHM concerned a trade-off between model-target similarity and model simplicity. Five reasons for choosing to engage in such a trade-off are discussed: lack of information, avoiding overfitting, avoiding fuzzy modularity, maintaining good communication, and facilitating error avoidance and detection. I conclude that the case illustrates that model simplicity is an epistemically important principle.
The epidemiological modelling toolbox has grown considerably over the last twenty years. The Public Health Agency of Sweden (FoHM) is a good illustration of that: it has systematically developed its menu of mathematical and computational modeling tools. But constructing a menu also forces a choice, and this is the focus of my case study in this paper. When Covid-19 came to Sweden, how did FoHM modelers choose their modeling tools from those menu options?…
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