Researchers have introduced a groundbreaking method for modeling epidemics that could revolutionize how scientists and policymakers forecast the spread of infectious diseases. This study presents a new framework that takes into account factors related to socioeconomic status (SES) — including income, education, and ethnicity — in epidemic models. The findings highlight an urgent need for more inclusive epidemic modeling frameworks as societies deal with the ongoing effects of COVID-19 and prepare for future outbreaks. By moving beyond the traditional emphasis on age and context, this innovative methodology enables a deeper comprehension of disease transmission and serves as an effective instrument for tackling health disparities.
An international team of researchers has designed a new approach to epidemic modeling that has the potential to change how scientists and policymakers estimate the spread of infectious diseases. The initiative, led by Dr. Nicola Perra, Reader in Applied Mathematics, is detailed in a study published in Science Advances, which unveils a framework that includes socioeconomic status (SES) factors—like income, education, and ethnicity—into epidemic models.
“Traditional epidemic models often focus primarily on age-related contact patterns, but that’s just one part of the whole picture,” explained Dr. Perra. “Our new framework recognizes that additional factors—such as income and education—are crucial in shaping how individuals interact and comply with public health initiatives. By incorporating these SES variables, we can build models that more accurately represent the actual outcomes of epidemics.”
Dr. Perra and his team have tackled this important gap by employing “generalised contact matrices” to analyze interactions across various factors, including SES. This approach allows for a more nuanced and realistic depiction of how diseases spread among different demographic groups, especially those at an economic disadvantage. The research indicates that ignoring these factors can lead to significant inaccuracies in epidemic forecasts, which can hamper public health strategies and policy-making.
The team’s method is based on both formal mathematical equations and real-world evidence. Their study reveals that overlooking SES dimensions can result in serious underestimations of vital metrics, like the basic reproductive number (R?), which indicates the average number of secondary infections generated by one infected person. Using both synthetic and real data from Hungary, collected during the COVID-19 pandemic, the researchers demonstrate that incorporating SES indicators leads to a better understanding of disease impact and exposes important inequalities in health outcomes across different socioeconomic groups.
“The COVID-19 pandemic starkly illustrated that the burden of infectious diseases is not distributed evenly in society,” noted Dr. Perra. “Socioeconomic factors were pivotal in determining the impact on various groups; however, many epidemic models we currently depend on still fail to integrate these essential aspects. Our framework emphasizes these variables, offering richer and more actionable insights.”
The researchers also showcased how their framework can measure differences in compliance with non-pharmaceutical interventions (NPIs), like social distancing and mask wearing, among various SES groups. They discovered that ignoring these elements in models not only distorts the understanding of disease spread but also clouds the perceived effectiveness of public health measures. Their analysis of Hungarian data brought to light how socioeconomic disparities in contact behaviors can create notable differences in health outcomes between groups, reinforcing the need for more focused interventions.
“Our results indicate that future contact surveys should encompass not only traditional factors like age but also more detailed socioeconomic information,” Dr. Perra emphasized. “Incorporating these elements could significantly enhance the accuracy of epidemic models and, consequently, the success of health policies.”
This research highlights an immediate need for more comprehensive epidemic modeling frameworks, particularly as societies continue to face the ongoing repercussions of COVID-19 and gear up for potential future pandemics. By broadening the conventional focus on age and context, this novel approach paves the way for a deeper understanding of disease spread and provides a potent tool to address health inequities.
This work involved 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).
