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

Jon Zelner,Sanyu A Mojola,Ramya Naraharisetti,Merlin Chowkwanyun,Ryan Malosh,Nina B Masters,Jason A Papin

doi:10.1371/journal.pcbi.1009795

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.

Highlights

In March 2020, a prescient news item in Science proclaimed that infectious disease transmission models had taken on “life or death importance” [1] as tools in the fight against Severe Acute Respiratory Syndrome Coronavirus 2 (SARSA-CUoV: -P2l)e.aDseenspoitteethtahteSpAiRvoStaÀ l rCooleVthÀ ey2havsbeeendefined played, most mechanistic models used to guide the global response to SARS-CoV-2 paid little direct attention to the causes of the massive socioeconomic and racial inequities that have characterized the pandemic in the United States and around the world [2,3,4,5,6,7]
We argue that the FC approach provides a useful set of principles that can be used to guide both the goals and data collection necessary to build the foundations of equity-forward transmission modeling
We echo the argument made by Bertozzi and colleagues in the early days of the pandemic [61]: Rather than stumbling over attempts at hyperrealism, transmission models should focus on characterizing broad trends in inequity, the mechanisms that generate them, and multilevel interventions that might work to ameliorate infection inequities

Summary

Introduction

In March 2020, a prescient news item in Science proclaimed that infectious disease transmission models had taken on “life or death importance” [1] as tools in the fight against Severe Acute Respiratory Syndrome Coronavirus 2 (SARSA-CUoV: -P2l)e.aDseenspoitteethtahteSpAiRvoStaÀ l rCooleVthÀ ey2havsbeeendefined played, most mechanistic models used to guide the global response to SARS-CoV-2 paid little direct attention to the causes of the massive socioeconomic and racial inequities that have characterized the pandemic in the United States and around the world [2,3,4,5,6,7] This reflects the absence of a theoretical and methodological framework needed to deploy equity-oriented models with the same speed and rigor as those focused on understanding and forecasting population-level outcomes.

Objectives

Conclusion