Social inequalities in vaccine coverage and their effects on epidemic spreading

Vaccinations are fundamental public health interventions. Yet, inequalities in vaccine uptake across socioeconomic groups can significantly undermine their impact. Moreover, heterogeneities in vaccination coverage across socioeconomic strata are typically neglected by epidemic models and considered, if at all, only at posteriori. This limitation reduces their ability to predict and assess the effectiveness of vaccination campaigns. Here, we study the impact of socioeconomic inequalities in vaccination uptake on disease burden, measured as attack rate. We consider a modeling framework based on generalized contact matrices that extend traditional age-stratified approaches to incorporate socioeconomic status (SES) variables. We simulate epidemic dynamics under two scenarios. In the first, vaccination campaigns are concurrent with epidemics. In the second, instead, vaccinations are completed before the onset of infection waves. By using both synthetic and empirical generalized contact matrices, we find that inequalities in vaccine uptake can lead to non-linear effects on disease outcomes and exacerbate disease burden in disadvantaged groups of the population. We demonstrate that simpler models ignoring SES heterogeneity produce incomplete or biased predictions of attack rates. Additionally, we show how inequalities in vaccine coverage interact with non-pharmaceutical interventions (NPIs), compounding differences across subgroups. Overall, our findings highlight the importance of integrating SES dimensions, alongside age, into epidemic models to inform more equitable and effective public health interventions and vaccination strategies.Author summary: We investigate how social inequalities in vaccination uptake influence epidemic outcomes. To this end, we adopt a generalized modeling framework to incorporate socioeconomic status (SES) with age in the description of the population and their contacts. We simulate epidemic scenarios under different vaccination distributions and timings. Our findings reveal that inequalities in vaccine coverage can lead to disproportionate disease burdens among disadvantaged groups, especially when compounded by possible heterogeneities in the adoption of non-pharmaceutical interventions (NPIs). By analyzing both synthetic and real-world data from Hungary, we demonstrate that traditional age-structured epidemic models underestimate the effects of SES inequalities, potentially leading to biased predictions and suboptimal public health responses. Our study highlights the critical importance of integrating social inequalities in vaccination into epidemic models to ensure accurate descriptions and equitable interventions. These results provide actionable insights for policymakers and public health officials to improve population health outcomes.