Turns out, mathematics can help public health workers understand and influence human behaviors that lead to the spread of infectious diseases.
According to a study from the University of Waterloo, current models used to predict the emergence and evolution of pathogens within populations do not include social behavior.
Co-author Chris Bauch said, "We tend to treat disease systems in isolation from social systems, and we don't often think about how they connect to each other or influence each other. This gives us a better appreciation of how social reactions to infectious diseases can influence which strains become prominent in the population."
This new addition to disease modeling could allow scientists to better prevent undesirable outcomes, such as more dangerous mutant strains from evolving and spreading.
By adding dynamic social interactions to the models already used for disease outbreaks and evolution, researchers could better anticipate how a pathogen strain may emerge based on how humans attempt to control the spread of the disease.
The social modeling could impact public health responses to emerging infectious diseases like Ebola and Severe Acute Respiratory Syndrome (SARS). Human behavior during these outbreaks often changes dramatically.
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People may start using face masks, or stop using them prematurely. Also, public fear of the pathogens may end up driving the wrong type of behavior if the public's information is incorrect.
Bauch and his co-author Joe Pharaon formulated the new mathematical model to study the influence of social behavior on the competition between pathogen strains with different virulence.
Using computer simulations, they analyzed how the model behaved under various possible scenarios that might occur to populations to explore the logic of the hypothesis that social behavior plays a role in the evolution of the strain.
The full findings are present in the Journal of Theoretical Biology.