The model crisis, or how to have critical promiscuity in the time of Covid-19

During the past forty years, statistical modelling and simulation have come to frame perceptions of epidemic disease and to determine public health interventions that might limit or suppress the transmission of the causative agent. The influence of such formulaic disease modelling has pervaded public health policy and practice during the Covid-19 pandemic. The critical vocabulary of epidemiology, and now popular debate, thus includes R-0, the basic reproduction number of the virus, 'flattening the curve', and epidemic 'waves'. How did this happen? What are the consequences of framing and foreseeing the pandemic in these modes? Focusing on historical and contemporary disease responses, primarily in Britain, I explore the emergence of statistical modelling as a 'crisis technology', a reductive mechanism for making rapid decisions or judgments under uncertain biological constraint. I consider how Covid-19 might be configured or assembled otherwise, constituted as a more heterogeneous object of knowledge, a different and more encompassing moment of truth - not simply as a measured telos directing us to a new normal. Drawing on earlier critical engagements with the AIDS pandemic, inquiries into how to have 'theory' and 'promiscuity' in a crisis, I seek to open up a space for greater ecological, sociological, and cultural complexity in the biopolitics of modelling, thereby attempting to validate a role for critique in the Covid-19 crisis.

Automated summary

Statistical modeling and simulation have played a crucial role in shaping perceptions of epidemic disease and determining public health interventions during the Covid-19 pandemic. This article explores how statistical modeling as a 'crisis technology' influences health policy and practice, and how Covid-19 can be configured as a more heterogeneous object of knowledge, thereby providing a space for critique in the pandemic crisis.