Whether it be ventilators or vaccines, allocating limited supplies during the COVID-19 pandemic has been a persistent problem, especially in underserved, minority communities that have been affected disproportionately by the coronavirus. Now, a study by Stanford scholars has developed a promising new way to reach vulnerable populations and deliver resources more equitably.

Daniel Ho (Image credit: Rod Searcey)

Working in collaboration with public health officials in Santa Clara County and community leaders in East San Jose, Stanford scholars borrowed a simple concept from machine learning to prototype a new way to distribute COVID-19 diagnostic tests that were critical to better understanding the transmission of the coronavirus.

Their findings, published Aug. 27 JAMA Health Forum, showed that machine learning, when combined with local insights from community health workers working on the ground, broadened testing capacity, decreased demographic disparities in testing and caught clusters of infections early on.

“This intervention demonstrates a critical need for academics to work in partnership with community health workers and public health agencies to reach and disseminate information to vulnerable communities,” said Daniel E. Ho, senior author of the study and the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School.

Ho is the founder and faculty director of the RegLab where he and other Stanford researchers work with government agencies on a pro bono basis to adapt and leverage advances in data science and machine learning for public policy.

When COVID-19 hit, Ho and his team – including study co-author Derek Ouyang who has helped lead the lab’s response to the pandemic, as well as with the Stanford Future Bay Initiative – began working with public health officials to investigate how they could assist with their pandemic response.

One of those officials was Dr. Analilia Garcia, the racial and health equity senior manager for the Santa Clara County public health department.

Garcia and her colleagues were concerned by how COVID-19 was disproportionately impacting the county’s Latinx residents. While this demographic makes up just over one quarter (25.8 percent) of the county’s population, they have accounted for more than half of all its COVID-19 cases (50.3 percent).

As COVID-19 spread throughout the area, ensuring that this population had access to resources, including testing, became a…

Continue reading: https://news.stanford.edu/2021/08/27/using-data-science-allocate-covid-19-resources-address-demographic-disparities/

Source: news.stanford.edu