Speaker
Description
Social interactions and disease transmission are tightly intertwined, with behavioural responses and risk perception evolving during an epidemic. Capturing these feedbacks remains a major challenge in epidemic modeling. In social networks, the “ego” denotes the focal individual, while “alters” are individuals directly connected to the ego. We propose a type-configuration algorithm based on node attributes, including demographic and contact information, to assess individual infection risk in a network. Individual infection risk is defined as a function of epidemiological parameters – relative susceptibility, infectiousness, contact frequency, and baseline disease transmission rate, with values depending on social factors such as education level, residence area, gender, and age. Model inputs are derived from sociodemographic data from a representative Hungarian population sample and contact surveys. Our framework captures the heterogeneous impact of sociodemographic variables on individual risk during epidemics. This data-driven approach reveals how social characteristics and contact patterns shape the local vulnerability landscape, providing insights on both individual- and population-level risk. Ultimately, individuals can use this framework as a personalized risk predictor by providing sociodemographic and contact information, allowing the algorithm to assign a type-configuration and compute an interpretable estimate of infection risk under a given epidemiological scenario.
