CEU Electronic Theses and Dissertations, 2025
| Author | Manna, Adriana |
|---|---|
| Title | The interplay between social inequalities and the spread of epidemic processes |
| Summary | Mathematical models of infectious disease transmission are essential for predicting and analyzing epidemic outbreaks. During the COVID-19 pandemic, these models have played a critical role in identifying the mechanisms behind virus spread and evaluating control measures such as school closures, lockdowns, and mobility restrictions. In recent years, traditional models have evolved to incorporate real-life heterogeneities, recognizing that the spread of diseases is influenced not only by pathogen-specific factors but also by social, demographic, and economic variables. Social scientists have shown that individuals’ socio-demographic and economic characteristics not only shape the spread of diseases but also affect the severity of outcomes among those infected. Despite extensive research on health inequalities, there has been limited focus on how these pre-existing inequalities influence the epidemic process itself. Current epidemiological models typically stratify populations by age but often overlook other critical heterogeneities within socio-economic strata. For example, contact patterns are usually simplified into age-stratified matrices (Cij), which encode the average number of interactions between age groups. Factors like vaccination rates, susceptibility, and fatality rates are also primarily considered by age, neglecting other socio-economic dimensions that could alter the course of an epidemic. This thesis aims to fill this gap by investigating the interplay between social inequalities and epidemic spread. The analysis focuses on two main areas. First, from a computational social science perspective, we identify key socio-economic characteristics—such as education and employment—that influence contact rates and vaccination uptake. Using data from the MASZK study in Hungary, we demonstrate that these socio-economic factors, like education and employment strongly shape individuals' contact patterns and their likelihood of getting vaccinated. Second, we propose an extended mathematical framework incorporating generalized contact matrices, which account for multiple socio-economic dimensions beyond age. Through simulations based on both synthetic and real-world data, we show that neglecting socio-economic status (SES) leads to underestimating the basic reproductive number (R0) and misrepresenting epidemic dynamics. Our results reveal that SES-based segregation in social networks, along with varying activity levels and vaccination rates, can result in localized outbreaks and disproportionate disease burdens in lower-SES groups. Additionally, the effectiveness of non-pharmaceutical interventions (NPIs) is shown to vary significantly across different socio-economic strata. This work contributes to a deeper understanding of how social inequalities shape epidemic outcomes, emphasizing the need for public health policies that address these disparities in both prevention and response. |
| Supervisor | Karsai Márton |
| Department | Network Science PhD |
| Full text | https://www.etd.ceu.edu/2025/manna_adriana.pdf |
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