In the months since Zika first emerged as a major health crisis, severely limiting brain development for children in utero, science has struggled to keep pace with the spread of the disease.

Cases of microcephaly have skyrocketed in Brazil, the World Health Organization officially designated Zika a Public Health Emergency of International Concern, and active transmission of the virus has been reported in nearly 60 countries. While the medical community rushes to develop an effective preventative treatment, governments, non-profits, and other health organizations have undertaken independent efforts to track new Zika cases as they proliferate across the Americas.

With vital information on this virus dispersed across agencies around the world, how can researchers get an accurate picture of where Zika is likely to spread and how it might be contained?

The answer may be at the Biocomplexity Institute of Virginia Tech where researchers are developing a toolset to forecast the global transmission of Zika.

This effort builds on over a decade of modeling and simulation software development, which has positioned the institute to provide sustained, real-time epidemic decision support, using predictive models to inform effective relief efforts.

Researchers at the Biocomplexity Institute are currently putting these powerful simulation engines to work in service of the public health response to Zika, aiming to shed light on how the virus may be mitigated in regions inhabited by the Aedes aegypti and Aedes albopictus mosquitoes—the virus’s primary means of transmission.

The agility of these predictive models has been honed over years of development and testing. “Our Comprehensive National Incident Management System (CNIMS) suite is mature enough that any analyst with minimal training can run complex computational experiments,” said Bryan Lewis, computational epidemiologist. “Through years of supporting real world-driven demonstration studies, we’ve been able to continuously improve and refine our simulations, allowing us to provide support to several branches of the federal government .”

The synthetic information system embedded in CNIMS is a novel approach to simulations. It allows researchers to study potential crises at the level of their primary, secondary, and even tertiary effects, providing analysts with an informed course of action. In the case of the current Zika epidemic, this could provide a means of predicting both the scale of an outbreak and the changes in social behavior that might be triggered by its spread. 

“A distinguishing feature of the modeling environment is the ability to represent a broad range of interventions and behaviors,” said Madhav Marathe, director of the Biocomplexity Institute’s Network Dynamics and Simulation Science Laboratory. “Individual and collective adaptive behaviors can be represented, giving the researchers an extremely detailed portrait of how individuals function in the face of disaster both on their own and as part of a larger society.”

Over 40 independent layers of data are tied together within the CNIMS platform—even factors like family status and daily routines. This allows scientists and policy makers to align practical analyses of where and when to dispatch medical supplies during an epidemic with the social dynamics of the community being served.

For agencies charged with coordinating the response to an international outbreak like Zika, the level of insight provided by CNIMS could help public health resources be directed toward intervention efforts with the highest likelihood of success. “Through this elegant suite of simulation systems, our research is providing us with the answers decision makers need to move forward,” said Bryan Lewis. 

This work is part of an ongoing effort at the Biocomplexity Institute to support large, complex event simulations that require situational assessment and decision support systems. Research in this area is focused on developing novel systems that can be used to plan and respond during natural or human-made crises. In addition to serious health problems, large-scale pandemics cause a host of economic, social, and infrastructural issues. The consequences of these attendant effects can cascade into feedback loops which exacerbate or alter the course of the original problem.

The institute’s approach stems from the development of four interrelated components: detailed synthetic information systems; dynamic models that help to understand contagion within systems; data management systems; and applications that make this information accessible to decision makers, analysts, and citizens. 

Published by Tiffany Trent, June 15, 2016
Tags: Big Data  Public Health  Zika