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.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-10-13
DOI
https://doi.org/10.1371/journal.pcbi.1013585
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
2.74
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article

Social inequalities in vaccine coverage and their effects on epidemic spreading

Nicola Perra, Adriana Manna, Márton Karsai
2 citations
PLoS Computational Biology
COVID-19 epidemiological studies
2.74
article

Social inequalities in vaccine coverage and their effects on epidemic spreading

Nicola Perra, Adriana Manna, Márton Karsai
article en
2 citations

Abstract

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.

PLoS Computational BiologyVol. 21(10)
HUN-REN Alfréd Rényi Institute of Mathematics (HU), Queen Mary University of London (GB), Central European University (AT), The Alan Turing Institute (GB)
Openalex Percentile: Top 9%
COVID-19 epidemiological studies
2.74
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Social inequalities in vaccine coverage and their effects on epidemic spreading — Nicola Perra, Adriana Manna, et al. · PLoS Computational Biology (2025) | TGRS Research Map | TGRS