The list of communicable global health
threats such as HIV, SARS, Ebola, H1N1, Zika seems ever growing. And all too often, the limited resources
available to fight these diseases must be picked up and redeployed,
often haphazardly, as the next new threat emerges.
But what if there were ways to wage a more effective war against all
communicable diseases, using new combinations of proven complex
sociological and statistical mathematic models to tell where an outbreak
might occur, how it might spread, and how it could best be rolled back
or even eliminated by a more tactical application of resources?
‘As societies grow more complex and interconnected, a mathematical biologist has called for a similar evolution in models to combat communicable disease.’
That's exactly what Carlos Castillo-Chavez, a regent's professor of
mathematical biology at Arizona State University's School of Human
Evolution and Social Change, and colleagues propose the need for in a new article published this month in the Proceedings of the National Academy of Sciences of the United States of America
As executive director of the Simon A. Levin Mathematical and
Computational Modeling Sciences Center, Castillo-Chavez isn't satisfied
with the traditional mathematical epidemiological approach to tracking
these diseases, which relies heavily on the amount of per capita points
of "collisions" between those with a disease and others not yet
infected. These models often fall short because they fail to take into
account the unique complicating factors of what is referred to in the
study as a "patch" (or a zone of shared socioeconomic, geographic or
other traits) where those interactions occurred.
Instead, Castillo-Chavez is now looking at and prompting others in
his field to consider the intersection of two evolving approaches that
could be used to better address the problem.
The first is economic epidemiological modeling (EEM), which includes
examining information flow in affected areas and the financial
risk/reward perceptions that may drive movement of individuals to, from,
and within affected "patches." An example might be someone having to
choose between self-quarantine as a protection strategy versus leaving
home during an outbreak to go to work and receive income - which can
also be a matter of life and death.
The second is the Lagrangian approach, which also assists with
projecting human crowd movement and behavior, but broadens the scope of
patches considered related to a disease and allows them be assigned
their own associated risk of infection per residency time. This
information can then be layered over EEM-driven population mobility
calculations for more accurate transmission projections.
"The Lagrangian perspective has helped increase our understanding of
the consequences of the deliberate release of biological agents in 2003
and most recently in the study of Ebola in West Africa and Zika in the
Americas," Castillo-Chavez notes.
Both the development and the application of these types of new,
complex mathematical and social theories would be no easy feat. But it
might be a first step towards the goal of identifying consistent
patterns - such as those starting to emerge in the study of
host-parasite systems - that can account for not just known and
recurrent variables, but also emergent natural and social shifts in a
world where people can move anywhere, at nearly any time.
"These efforts emerged as the result of multi-institutional
collaborators that met regularly at NIMBioS in Tennessee with the
support of NSF for more than two years," Castillo-Chavez says. "The
research has been carried out with my former students and postdoctoral
associates Benjamin Morin, now at Vassar and Derdei Bichara, now at Cal