Current models used to predict the emergence and evolution of pathogens within host populations did not include social behaviour.
But adding dynamic social interactions to the new model could allow scientists to better prevent undesirable outcomes, such as more dangerous mutant strains from evolving and spreading.
"We tend to treat disease systems in isolation from social systems, and we often don't think about how they connect to each other or influence each other," said Chris Bauch, Professor at Waterloo University in Canada.
"This gives us a better appreciation of how social reactions to infectious diseases can influence which strains become prominent in the population," Bauch added.
In the study, published in the Journal of Theoretical Biology, the team used computer simulations to analyse how the mathematical model behaved under various possible scenarios.
They observed that human behaviour often changes dramatically during the outbreak, for instance, they might start using face masks.
Also, fear of public pathogens may end up driving the wrong type of behaviour if the public's information is incorrect.
The new modelling could help public responses navigate and better channel these kinds of population responses, the researchers said.
(This story has not been edited by Social News XYZ staff and is auto-generated from a syndicated feed.)
