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Mathematical models have come to play a key role in global pandemicpreparedness and outbreak response: helping to plan for disease burden,hospital capacity, and inform non-pharmaceutical interventions. Such modelshave played a pivotal role in the COVID-19 pandemic, with transmission modelsand, by consequence, modelers guiding global, national, and local responses toSARS-CoV-2. However, these models have systematically failed to account for thesocial and structural factors which lead to socioeconomic, racial, andgeographic health disparities. Why do epidemiologic models of emerginginfections ignore known structural drivers of disparate health outcomes? Whathave been the consequences of this limited framework? What should be done todevelop a more holistic approach to modeling-based decision-making duringpandemics? In this Perspective, we evaluate potential historical and politicalexplanations for the exclusion of drivers of disparity in infectious diseasemodels for emerging infections, which have often been characterized as "equalopportunity infectors" despite ample evidence to the contrary. We look toexamples from other disease systems (HIV, STIs) as a potential blueprint forhow social connections, environmental, and structural factors can be integratedinto a coherent, rigorous, and interpretable modeling framework. We conclude byoutlining principles to guide modeling of emerging infections in ways thatrepresent the causes of inequity in infection as central rather than peripheralmechanisms.

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