A framework developed by a Purdue mathematics professor and collaborators at the
Centers for Disease Control and Prevention
could have implications in preventing disease outbreaks.
Zhilan Feng worked on the math side
of a study that revisited the 2008 measles
outbreak in San Diego County, California,
in collaboration with John W. Glasser and
Lance E. Rodewald, epidemiologists for the
Centers for Disease Control and Prevention.
Twelve children in the area were
infected with the virus, which causes fever
and an unmistakable body rash. Measles
was deemed eradicated from the United
States in 2000 until a 7-year-old boy infected
in Switzerland returned to southern
California. Eleven more children were soon
infected. Most of those were not vaccinated
or were too young for the MMR vaccination,
which immunizes against measles, mumps
and rubella. Children have to be at least 12
to 15 months old before they get the shot.
The case was sobering on many levels.
A disease thought long gone from the country came back quickly. It spread at a time
where more children are not being vaccinated due to political or religious beliefs.
Feng and Glasser’s mathematical
model not only documented the San Diego
County measles outbreak but was developed
to show trends and how to prevent outbreaks
of more common vaccine-preventable diseases like influenza. Such work can help
reduce the spread of deadly viruses like
Ebola and Zika and emerging or re-emerg-ing infectious diseases like SARS
and swine flu.
“This is more focused on how
targeted vaccination strategies
can increase population immunity,” Feng says. “The population
could be schools, cities, countries
or the entire world — as long as
you can specify the mixing structure and contact pattern. The
modeling framework for measles is just
one example. This approach can be
applied to many different vaccine-preventable diseases.
Feng and Glasser published their
results in the paper titled “The Effect of
It’s apparent that the more vaccinated
children are, the less chance for an outbreak. But 100 percent of the population will
never be vaccinated due to choice and age.
Feng and Glasser were able to look at which
parts of San Diego County were more susceptible due to rates of “preferential mixing”
“Preferential mixing means that students from one school will mix with others
in the same school more than other schools,”
Feng says. “Of course, different populations
have different geographical structure, age
structure and behavior.”