UC Berkeley researchers: New framework needed to track infectious diseases

A recent paper published in PLOS Computational Biology proposes the Disease Surveillance Informatics Optimization and Simulation (or DIOS) framework as a stronger approach to track infectious disease spread throughout populations. The authors of the paper—which include researchers from UC Berkeley, the University of Michigan, the University of Florida, Emory University, and the China Centers for Disease Control and Prevention—say that DIOS provides the foundations for stronger infectious disease control policies and can help health agencies better achieve specific surveillance goals with limited resources.

Infectious disease surveillance systems differ significantly in how frequently they survey populations, who is targeted, and how populations are diagnosed. These differences can vastly affect early detection of outbreaks, tracking of emerging infections, and our ability to control the spread of disease.

“Across US states and around the globe, surveillance systems vary widely in their design,” said Justin Remais, senior author of the study and Professor and Chair of the Division of Environmental Health Sciences at Berkeley Public Health. “As COVID-19 has shown us, we are badly in need of modern information systems that provide reliable and timely estimates of disease occurrence, particularly among high-risk groups, in order to protect populations and control disease spread.”

DIOS is a computational platform that public health researchers and practitioners can use to predict how new surveillance system designs will perform, and to identify the optimal allocation of surveillance resources. Modest changes—such as establishing reporting at unmonitored sites, targeting important subpopulations, and changing the diagnostics used—can lead to more rapid detection of cases and more accurate monitoring of the spread of diseases.

“Efficient surveillance systems are critical in low and middle income countries where monitoring the spread of infectious diseases is a major challenge,” Remais said. “DIOS can now be used to tailor real-world systems to better estimate the probability of outbreaks, target testing, forecast disease dynamics, identify vulnerable populations, and inform targeted disease control measures.”