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.

Addressing the socioeconomic divide in computational modeling for infectious diseases

“…In recent years, researchers have advocated for the extended use of computational and digital tools to tackle the emergence of novel infectious diseases and to rapidly face new outbreaks 18 , 19 . More recently, the importance of including social aspects in infectious disease modeling has been highlighted by numerous authors 20 – 22 . In this comment, we argue that the field of digital and computational epidemiology may remedy some of the challenges of socioeconomic inequalities in outbreak science.…”

“…Modeling frameworks that include SES at their core are largely missing and urgently needed 22 . When thinking about possible solutions, it is important to realize how one-fits-all approaches are hardly conceivable.…”

Estimating and explaining cross-country variation in the effectiveness of non-pharmaceutical interventions during COVID-19

“…Such characteristics have been used previously to explore variation in the severity of the COVID-19 pandemic across countries 14 – 17 , but not for comparing the effectiveness of NPIs across countries. Analyzing the latter is motivated by findings that the individual socioeconomic status can determine the risk of infection and can also influence the effectiveness of NPIs 18 .…”

Social divisions and risk perception can drive divergent epidemic dynamics and large second and third waves

“…We define separated awareness as greater in-versus out-group awareness in a split population and predict that, by producing behavioral responses more reflective of each group’s risk, it may reduce differences between groups in disease burden (50). Understanding the impacts of separation with respect to mixing and awareness on disease dynamics may be important for characterizing differences in epidemic burden and effectively intervening to mitigate population inequities (37, 39, 40, 50, 51).…”

“…Physical barriers (e.g., geographic boundaries, schools, residential segregation, and incarceration) and preferential mixing with members of one's own group may reduce contact and subsequent transmission between groups, a characteristic we describe as separated mixing (19,(33)(34)(35)(36). Infectious disease models that account for differences in vulnerability within subgroups of a population and separated mixing can help to illustrate the emergence of health inequities and justify structural interventions to reduce these disparities (37)(38)(39)(40). However, such models may miss an important behavioral dimension by failing to account for variation in awareness-based behavior changes among groups.…”