Fairness in infectious disease modeling

The concept of fairness has been extensively examined within the domains of Machine Learning and Artificial Intelligence more broadly. It remains, however, largely underexplored in the field of Computational Epidemiology. Considering the substantial influence that epidemic models exert on public health policy, particularly in the context of outbreak preparedness and response, this shortcoming is of great relevance. Here, we propose a mathematical framework, grounded in core principles from Social Epidemiology, for evaluating the fairness of computational epidemic models. We begin by applying our framework to a range of epidemic modeling approaches and simulation scenarios, such as the initial spread of COVID-19 in London, New York, and Santiago de Chile, as well as the 2016 Zika virus outbreak in Colombia, demonstrating its consistent capacity to assess model fairness across diverse disease dynamics. Subsequently, we illustrate how our definition of fairness can be incorporated into the design of immunization strategies as a way to enhance health equity while simultaneously improving the overall effectiveness of such interventions. Overall, our results offer a new systematic methodology for quantifying fairness in computational epidemiology.