Mathematical modelling could make efforts to predict and control disease outbreaks much easier and more reliable, according to a new study conducted by researchers at the University of Waterloo, University of Maryland, and Yale’s School of Public Health.
“Mathematical models of disease spread can be hugely beneficial in understanding and controlling infectious diseases,” Chris Bauch, a professor in Waterloo’s Department of Applied Mathematics, said. “There are certain challenges that have to be overcome when attempting to use mathematical modelling, for example, if you want to impact policy, you have to involve the policymakers at every step in the process.”
Data is always helpful, but these researchers said that cooperating with multiple medical and public health sources could help achieve modelling objectives and control infectious diseases, and for that matter, help get the required data in the first place. However, it would need to be provided to public health planners working on intervention strategies to have a true effect.
Understanding data’s usefulness goes beyond the diseases themselves, however, and extends to the human factor.
“Another area in which mathematical modelling can prove useful is in combating vaccine hesitancy,” Bauch said. “As the access to vaccines become less of a problem worldwide, vaccine hesitancy will perhaps become the most important barrier to ensuring high vaccine uptake.”
As has been seen in the spread of anti-vaccination beliefs — which has helped revive diseases like measles — social networks help fuel misinformation every bit as much as fact. The researchers say that mathematical models could help understand how ideas, opinions, and beliefs about vaccines spread through such networks and help experts intervene to protect vaccination.