Scientists bring socioeconomic status at the forefront of epidemic modelling

"Epidemic models typically focus on age-stratified contact patterns, but that's only part of the picture," said Dr Perra. "Our new framework acknowledges that other factors -- like income and education -- play a significant role in how people interact and respond to public health measures. By including these SES variables, we're able to create more realistic models that better reflect real-world epidemic outcomes."

Dr Perra and his collaborators have addressed this critical oversight with a framework that uses "generalised contact matrices" to stratify contacts across multiple dimensions, including SES. This allows for a more detailed and realistic representation of how diseases propagate through different population groups, especially those facing socioeconomic disadvantage. The study demonstrates how failing to account for these variables can lead to large misrepresentations in epidemic predictions, undermining both public health strategies and policy decisions.

The team's approach draws on both formal mathematical derivations and empirical data. Their study establishes that ignoring SES dimensions can lead to underestimations of key parameters, such as the basic reproductive number (R?), which measures the average number of secondary infections caused by a single infected individual. Using synthetic data and real-world data from Hungary, collected during the COVID-19 pandemic, the researchers show how including SES indicators provides more accurate estimates of disease burden and reveals crucial disparities in outcomes across different socioeconomic groups.

"The COVID-19 pandemic was a stark reminder that the burden of infectious diseases is not borne equally across the population," said Dr Perra. "Socioeconomic factors played a decisive role in how different groups were affected, and yet most of the epidemic models we rely on today still fail to explicitly incorporate these critical dimensions. Our framework brings these variables to the forefront, allowing for more comprehensive and actionable insights."

The researchers demonstrated how their framework could quantify variations in adherence to non-pharmaceutical interventions (NPIs) such as social distancing and mask-wearing across different SES groups. They found that neglecting these factors in models not only misrepresents the spread of diseases but also obscures the effectiveness of public health measures. Their analysis of Hungarian data further highlighted how SES-driven heterogeneities in contact patterns can lead to substantial differences in disease outcomes between groups, underscoring the need for more targeted interventions.

"Our findings suggest that future contact surveys should expand beyond traditional variables like age and include more nuanced socioeconomic data," Dr Perra added. "The inclusion of these factors could dramatically improve the precision of epidemic models and, by extension, the effectiveness of health policies."

The study underscores an urgent need for more comprehensive epidemic modeling frameworks as societies continue to grapple with the lingering impacts of COVID-19 and prepare for future pandemics. By expanding beyond the conventional focus on age and context, this new approach opens the door to a more detailed understanding of disease transmission and offers a powerful tool for addressing health inequities.

This work was conducted in collaboration with Adriana Manna (Central European University), Dr Lorenzo D'Amico (ISI Foundation), Dr Michele Tizzoni (University of Trento), and Dr Márton Karsai (Central European University and Rényi Institute of Mathematics).