Researchers develop framework for infectious disease surveillance

Photo of the Berkeley Way West building
Karen Chow/File
Researchers from the Remais Lab at Berkeley have developed a framework for surveilling infectious diseases using mathematical representations. According to Qu Cheng, a postdoctoral scholar at Remais Lab, the framework can be used for novel emerging diseases, including COVID-19.

Related Posts

A research article published Dec. 4 highlights a new framework for infectious disease surveillance developed by researchers from the Remais Lab at Berkeley.

Disease surveillance systems allow scientists to estimate risk factors, disease trends and other topics of interest to determine how to respond to infectious diseases such as COVID-19, the Zika virus, tuberculosis and malaria, according to the article. To optimize surveillance systems, the Remais Lab launched a research effort to use mathematical representations and integrate existing data.

“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,” said Justin Remais, campus division of environmental health sciences professor and chair, in an email.

Qu Cheng, a postdoctoral scholar at the Remais Lab and lead author of the article, discussed how surveilling diseases can provide data about which demographic has the highest disease burden, if incidence rates are increasing, whether there are multiple pathogens and if an intervention is effective.

Cheng also discussed how the framework can be used for novel emerging diseases, such as COVID-19.

According to Cheng, in early phases where parameters are more uncertain and there is limited information, the goal is to rule out the worst options rather than to output the best. For example, a broader estimate of an incubation period for a disease could be used until more accurate information is gathered to provide a better estimate.

Cheng added that this research differs from previous surveillance systems based on operational constraints, budget and logistics. The proposed framework promotes a method based on goals that may vary from place to place and from disease to disease.

“For me, the biggest challenge is, ‘What’s a good question to answer,’” Cheng said. “We need input from those surveillance experts and those people working the field to help us identify which topic will be interesting for them and will be helpful for their surveillance work.”

Though the researchers are still in the early phases of applying this framework, Cheng noted that the framework is flexible and collaborating with others working on surveillance systems can also help priority-setting for implementation.

The final goal, according to Cheng, may be a software or web platform that can be used by people in resource-limited areas to target their own needs.

“Using this framework brings the possibility that we can design a surveillance system targeted for specific areas and specific diseases, and we will be able to use resources more efficiently,” Cheng said.

Contact Catherine Hsu at [email protected] and follow her on Twitter at @catherinehsuDC.