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An international team of researchers has developed an innovative approach to epidemic modeling that could reshape how scientists and policymakers predict the spread of infectious diseases. Spearheaded by Dr. Nicola Perra, Reader in Applied Mathematics, the study published in Science Advances introduces a groundbreaking framework that integrates socioeconomic status (SES) factors—such as income, education, and ethnicity—into epidemic models.
“Epidemic models typically focus on age-stratified contact patterns, but that’s only part of the picture,” Dr. Perra explained. “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.”
Bringing SES into Epidemic Modelling
Traditional epidemic models, which have largely relied on age-based contact matrices, often overlook the critical role of socioeconomic disparities in shaping disease dynamics. Dr. Perra and his team sought to address this by incorporating SES factors into their models, offering a more nuanced understanding of how diseases propagate through different population groups, particularly those facing socioeconomic disadvantages.
Their framework, based on “generalised contact matrices,” allows researchers to stratify population contacts across multiple dimensions, including SES. This method creates a more detailed representation of how diseases spread and helps identify how failing to account for SES factors can skew epidemic predictions, potentially leading to misguided public health strategies.
The study emphasizes that ignoring SES dimensions can result in underestimating key epidemiological parameters, such as the basic reproductive number (R₀), which estimates the average number of secondary infections generated by a single infected individual. Dr. Perra and his collaborators used both synthetic data and real-world data from Hungary during the COVID-19 pandemic to validate their findings, revealing how incorporating SES indicators leads to more accurate disease burden estimates and exposes critical disparities in health outcomes across different socioeconomic groups.
Uncovering Disparities and Improving Public Health Response
“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, 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 also explored how SES-driven differences in adherence to non-pharmaceutical interventions (NPIs), such as social distancing and mask-wearing, affect epidemic outcomes. Their analysis showed that neglecting these factors not only misrepresents the spread of diseases but also conceals the true effectiveness of public health measures. The team’s examination of Hungarian data further underscored how SES-related variations in contact patterns can lead to substantial differences in disease outcomes, highlighting the need for more targeted public health interventions.
A Call for More Comprehensive Epidemic Models
“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.”
As the world continues to grapple with the long-term effects of the COVID-19 pandemic and anticipates future pandemics, the study highlights the urgent need for more comprehensive epidemic modeling frameworks. By expanding beyond the conventional focus on age, this new approach offers a more detailed understanding of disease transmission and presents a powerful tool for addressing health inequities.
Dr. Perra’s work was carried out in collaboration with Adriana Manna from Central European University, Dr. Lorenzo D’Amico from the ISI Foundation, Dr. Michele Tizzoni from the University of Trento, and Dr. Márton Karsai from both Central European University and the Rényi Institute of Mathematics.
This innovative research opens new avenues for understanding how socioeconomic factors influence epidemic spread and offers a critical step toward more equitable public health policies.
