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

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 nonpharmaceutical 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 largely not accounted for the social and structural factors, which lead to socioeconomic, racial, and geographic health disparities. In this piece, we raise and attempt to clarify several questions relating to this important gap in the research and practice of infectious disease modeling: Why do epidemiologic models of emerging infections typically ignore known structural drivers of disparate health outcomes? What have been the consequences of a framework focused primarily on aggregate outcomes on infection equity? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this review, 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) and successes in including social inequity in models of acute infection transmission as a 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.

Fig 1. Illustration of the impact of fundamental causes on inequity in infection through multiple intervening mechanisms and multiple outcomes.

The figure illustrates key relationships between high-level fundamental causes of social inequality (parallelograms) on risks of infection and disease progression (rectangles) via their impact on more-proximal risks for exposure, severe disease upon infection and death (diamonds). Solid lines represent flows between disease states, while dotted lines illustrate relationships between risk factors and their impacts on susceptibility to infection acquisition and the rate of progression through escalating phases of disease severity. For visual clarity, only a subset of potential relationships is illustrated. For example, racism impacts vulnerability and access to care directly as well as indirectly, and SES and wealth often contributes to residential segregation. SES, socioeconomic status.