New Delhi, Oct 14 (PTI) A study has proposed a new framework that incorporates socioeconomic factors, such as income, education, and ethnicity into epidemic models, which help predict the spread of infections in a population.

Researchers have explained that when analysing disease transmission in society, epidemic models should look beyond traditional factors like age of an individual.

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The framework, described in the journal Science Advances, acknowledges that socioeconomic factors play a significant role in how people interact and respond to public health measures, the researchers said.

The approach offers a tool for addressing health inequities and provides more comprehensive understanding of infection dynamics, they said.

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"Epidemic models typically focus on age-stratified contact patterns, but that's only part of the picture," lead author Nicola Perra, a reader in applied mathematics, Queen Mary University of London, UK, said.

This new framework acknowledges other factors -- like income and education -- that play a significant role in how people interact and respond to public health measures, Perra said.

She said that by including these (socioeconomic) variables, they were able to create more realistic models that better reflect real-world epidemic outcomes.

As societies continue to deal with the lingering effects of COVID-19 and prepare for future pandemics, the results highlighted the need for more comprehensive epidemic modelling frameworks, the authors said.

"The COVID-19 pandemic was a stark reminder that the burden of infectious diseases is not borne equally across the population," Perra said.

"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," Perra said.

The researchers showed that the framework could quantify adherence to public health measures such as social distancing and mask-wearing across different socioeconomic groups.

Neglecting socioeconomic factors in models could thus misrepresent how an infection can spread and render public health measures as ineffective, they said.

"We highlight the importance of integrating (socioeconomic) traits into epidemic models, as neglecting them might lead to substantial misrepresentation of epidemic outcomes and dynamics," the authors wrote.

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