Social Status: Another Key Factor Affecting Survival During a Pandemic

Until now, mathematical models used to describe epidemics have not made it possible to study the combined effects of multiple individual characteristics in an epidemic simulation. Researchers at the HUN-REN Alfréd Rényi Institute of Mathematics addressed this issue in recent papers published in the leading journals Nature Communications and Science Advances.

During the recent pandemic in Hungary, Dr Márton Karsai (HUN-REN Rényi Institute, CEU), Dr Júlia Koltai (HUN-REN Centre for Social Sciences), and Dr Gergely Röst (University of Szeged) and their teams, as members of the National Laboratory for Health Security, focused on collecting as much data as possible on how people adapted their social interactions to avoid infection. Their primary aim was to track changes in age-contact matrices used in epidemic models.These matrices describe the likelihood of an individual of a given age interacting — for example, a retiree in their 70s —interacting with someone from a specific age group, such as their grandchild, on a given day.

Their study, involving PhD student Adriana Manna, found that in addition to age, factors such as income level, education, employment status, and settlement size also influence the number of social contacts an individual has. For example, the way people adjust the number of their connections differs depending on whether they are employed or currently unemployed, as well as whether they live in a larger city or a smaller rural settlement. However, until now, no mathematical method has been available to incorporate these observed characteristics into epidemic models, as previous approaches only considered a single factor—age—when describing contact patterns.

The Hungarian researchers, in collaboration with Dr Nicola Perra from Queen Mary University, addressed this mathematical challenge using multidimensional contact matrices. They demonstrated that this multidimensional approach provides a more accurate model of epidemic dynamics and outcomes.

Epidemiological models based on real data enable much more accurate analyses than previous approaches. They serve as a tool for mapping and understanding inequalities in epidemic outcomes across different social groups. According to the researchers, these findings are not only of scientific value but can also inform public health decisions by improving infection prediction, risk assessment, and the development of intervention plans.