According to the researchers, initial computer simulations have suggested the importance of human mobility in the rapid spread of Zika and they hope to use data to develop disease risk maps.
The researchers say their global synthetic information database is one of a kind and can be quickly reconfigured to provide situated decision-making capabilities.
The technology has been developed over two decades and has already been used by analysts during the H1N1, MERS and the recent Ebola outbreak.
“We are developing novel analytical and decision-support tools that provide epidemiologists unprecedented access and information to plan, respond and control pandemics,” said Executive Director of the Biocomplexity Institute, Christopher Barrett.
The computer models of the Institute’s Network Dynamics and Simulation Science Laboratory are the result of decades of research in developing computer models for epidemics and other large, complex systems.
“Through years of supporting real-world-driven demonstration studies, we’ve been able to continuously improve and refine our increasingly powerful simulations, allowing us to provide support to several branches of the federal government,” said Computational Epidemiologist at the Biocomplexity Institute, Bryan Lewis.
The laboratory uses a novel approach based on the concept of synthetic information to develop highly resolved computer simulations.
“The approach combines big data methods with social and behavioural theories to develop realistic and highly detailed representations of epidemic spread through social networks. They capture both the local human habitat, including built infrastructure, as well as that of the disease vector,” said Deputy Director of the Biocomplexity Institute, Madhav Marathe.
“Combined with high-performance computer-generated simulations of disease dynamics, the laboratory provides innovative approaches for situation assessment, planning and course of action during an epidemic outbreak,” continued Marathe.
In the case of Zika virus, this allows the researchers to forecast the spread of the disease through understanding the roles of a multitude of factors, especially human mobility and the natural environment. These models can then help analysts develop and assess various courses of action to combat the disease.
The researchers are also studying the social and economic impacts of the disease. They recently worked with Brazilian health authorities to complete a detailed synthetic representation of Brazil, a highly resolved and realistic representation based on geospatial and population data, including census data, maps, travel and mobility patterns, and built and natural environments.