The National Institutes of Health awarded $3.6 million, five-year grant to a University of California, Berkeley School of Public Health research team, UC Berkeley announced Wednesday.
The funding will support the researcher team's project to develop a method for simulating and optimizing surveillance networks that detect infectious diseases, officials said. The team will partner with both the U.S. and Chinese Centers for Disease Control and Prevention.
Researchers will use big data to eliminate the issues of monitoring infectious diseases on a global level, such as tracking disease elimination campaigns, detecting co-infections and increasing rare disease detection in high-risk populations. The focus will be high-priority global infectious diseases, like tuberculosis and malaria.
Further, the research team will develop statistical techniques for integrating complex data from multiple surveillance systems, improve surveillance informatics and create algorithms able to predict the way these systems work under different configurations.
The National Institute of Allergy and Infectious Diseases will provide project funding, under NIH's Spatial Uncertainty funding opportunity. Beijing Institute for Microbiology and Epidemiology, Emory University and the University of Florida will collaborate on the project.
Justin Remais, associate professor of environmental health sciences at UC Berkeley School of Public Health, will lead the project.
"Targeted and efficient surveillance systems are critical to detecting outbreaks, tracking emerging infections and supporting infectious disease control efforts, particularly in low- and middle-income countries where estimating the distribution of disease is a major challenge," said Remais said in a statement.
"We need to take advantage of new, vast health datasets to identify surveillance strategies that are effective under changing epidemiological and environmental conditions," he added.
Twitter: @JessieFDavis
Email the writer: jessica.davis@himssmedia.com