Researchers at the University of California, Berkeley are turning to Big Data to more effectively monitor infectious diseases. According to a university press release, the National Institutes of Health has awarded a $3.6 million, five-year grant to a Berkeley School of Public Health research team to develop new approaches for simulating and optimizing surveillance networks that detect infectious diseases.
The grant funds a project that will apply Big Data to major challenges in monitoring infectious diseases like tuberculosis and malaria, including tracking progress of disease elimination campaigns, detecting coinfections and maximizing rare-disease detection.
According to the university, the research team will “develop algorithms that predict how surveillance systems perform under different configurations, and can estimate the optimal allocation of surveillance resources under various constraints.” Through the data collected, the project will “develop statistical techniques for integrating complex data from multiple surveillance systems, provide critical insights into how surveillance systems function and lead to key advances in surveillance informatics,” the press release states:
“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 project leader Justin Remais, an associate professor of environmental health sciences at the School of Public Health. “We need to take advantage of new, vast health datasets to identify surveillance strategies that are effective under changing epidemiological and environmental conditions.”