TY - JOUR
T1 - Social inequalities in vaccine coverage and their effects on epidemic spreading
AU - Manna, Adriana
AU - Karsai, Márton
AU - Perra, Nicola
N1 - Publisher Copyright:
Copyright: © 2025 Manna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2025/10
Y1 - 2025/10
N2 - 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.
AB - 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.
KW - Computational Biology
KW - Computer Simulation
KW - Epidemics/statistics & numerical data
KW - Humans
KW - Social Class
KW - Socioeconomic Factors
KW - Vaccination Coverage/statistics & numerical data
KW - Vaccination/statistics & numerical data
UR - https://www.scopus.com/pages/publications/105019273572
U2 - 10.1371/journal.pcbi.1013585
DO - 10.1371/journal.pcbi.1013585
M3 - Article
C2 - 41082566
AN - SCOPUS:105019273572
SN - 1553-734X
VL - 21
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 10
M1 - e1013585
ER -