Public outreach campaigns can prevent the spread of devastating yet
treatable diseases such as tuberculosis (TB), malaria and gonorrhea.
But ensuring these campaigns effectively reach undiagnosed patients, who may
unknowingly spread the disease to others, is a major challenge for
cash-strapped public health agencies. Now, a team of USC Viterbi School of
Engineering researchers has created an algorithm that can help policymakers
reduce the overall spread of disease. The algorithm is also optimized to make the
most of limited resources, such as advertising budgets.
To create the algorithm, the researchers used data, including behavioral,
demographic and epidemic
disease trends, to create a model of disease spread that captures underlying
population dynamics and contact patterns between people.
Using computer simulations, the researchers tested the algorithm on two
real-world cases: tuberculosis (TB) in India and gonorrhea in
the United States. In both cases, they found the algorithm did a better job at
reducing disease cases than current health outreach policies by sharing
information about these diseases with individuals who might be most at risk.
The study was published in the AAAI Conference on Artificial
Intelligence. The authors are Bryan Wilder, a candidate for a PhD in computer
science, Milind Tambe, the Helen N. and Emmett H. Jones Professor in
Engineering, a professor of computer science and industrial and systems
engineering and co-founder of the USC Center for AI in Society and Sze-chuan
Suen, an assistant professor in industrial and systems engineering.
"Our study shows that a sophisticated algorithm can substantially
reduce disease spread overall," says Wilder, the first author of the
paper. "We can make a big difference, and even save lives, just by being a
little bit smarter about how we use resources and share health information with
the public."
Revealing disease dynamics
The algorithm also appeared to make more strategic use of resources. The
team found it concentrated heavily on particular groups and did not simply
allocate more budget to groups with a high prevalence
of the disease. This seems to indicate that the algorithm is leveraging
non-obvious patterns and taking advantage of sometimes-subtle interactions
between variables that humans may not be able to pinpoint.
The team's mathematical models also consider that people move, age, and
die, reflecting more realistic population
dynamics than many existing algorithms for disease control. For instance,
people may not be cured instantly, so reducing prevalence at age 30 could mean
creating targeted public health communications for people at age 27.
"While there are many methods to identify patient populations for
health outreach campaigns, not many consider the interaction between changing
population patterns and disease dynamics over time," says Suen, who also
holds an appointment as assistant professor in the Leonard D. Schaeffer Center
for Health Policy and Economics.
"Fewer still consider how to use an algorithmic approach to
optimize these policies given the uncertainty of our estimates of these disease
dynamics. We take both effects into account in our approach."
Since transmission patterns for infection vary with age, the research
team used age-stratified data to determine the optimal targeted audience
demographic for public health communications. But the algorithm could also
segment populations using other variables, including gender and location.
In the future, the study's insights could also shed light on health
outcomes for other infectious disease interventions, such as HIV or the
flu.
Source:
Explore more about the advancements
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