There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk

TL;DR

This paper critiques current epidemiological models for neglecting social and structural factors driving health disparities during pandemics and advocates for integrating these factors into more holistic modeling frameworks.

Contribution

It analyzes reasons for the exclusion of disparity drivers in infectious disease models and proposes principles for incorporating social determinants into future models.

Findings

Models often ignore structural drivers of disparities.

Historical and political factors influence model design.

Blueprints from other disease systems can inform inclusive modeling.

Abstract

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform non-pharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models and, by consequence, modelers guiding global, national, and local responses to SARS-CoV-2. However, these models have systematically failed to account for the social and structural factors which lead to socioeconomic, racial, and geographic health disparities. Why do epidemiologic models of emerging infections ignore known structural drivers of disparate health outcomes? What have been the consequences of this limited framework? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this Perspective, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) as a potential blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.