  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.
