Modeling social interactions between socioeconomic groups for epidemic analysis

Social interactions between socioeconomic groups play a crucial role in the spread of infectious diseases. This thesis aims to model such interactions to analyze epidemic dynamics and identify potential disparities in health outcomes across different socioeconomic population segments. In epidemic models, contact matrices are a widely used tool for representing interactions between different groups. Typically, these matrices rely only on age as the key dimension for stratifying contacts. However, there is growing interest in extending these models to include additional dimensions beyond age, in order to better capture social heterogeneity. This thesis addresses this challenge by exploring the theoretical background and methodological development of multidimensional contact matrices. Two-dimensional synthetic contact matrices (income-age) for households were extracted from an existing synthetic population (GLOPOP). Building on this, a synthetic school population for Italy was generated, from which the two-dimensional synthetic contact matrix (income-age) for the school population was derived. This approach enabled a quantitative assessment of how contact intensity varies across different income groups, offering new insights for epidemic modeling and public health interventions.