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Machine Learning Improves COVID-19 Tracing by Closing Language Gaps

Among the challenges posed by the pandemic were language barriers public health agencies faced as they struggled to trace infection spread among Latino communities. In California’s Santa Clara County, only 25% of the population is Latino, but it accounted for more than 56% of the state’s COVID cases. That put Spanish-speaking contact tracers – who call patients with diagnoses, identify and notify their contacts and assist with isolation and quarantine – in high demand.

These Spanish-speaking contact tracers have been key to reaching potentially infected individuals as quickly as possible, but with thousands of cases per day – and limited numbers of Spanish speakers and interpreters – it can take days to alert a patient’s contacts. An additional challenge is that Spanish-speaking residents may be reluctant to talk with government employees asking for complex, personal information – especially through someone not fluent in the language.

When we connect with people in their preferred language, it makes a huge difference in their willingness to share information about themselves, their health, and their families and friends.

– Dr. Sarah Rudman, Director of Contact tracing, County of Santa Clara Public Health Department

Recently, experts from Stanford’s RegLab — a group that designs and evaluates programs, policies, and technologies to modernise government — have come to the aid of the Santa Clara County Public Health Department. In a study detailed in Proceedings of the National Academies of Science, the RegLab team describes how it applied machine learning to transform contact tracing in Santa Clara County — and narrowed the health gap between the county’s Latino and other communities.

Contact tracers usually start with only the most basic information about the people they call, such as the patient’s name, address, date of birth and test result. Researchers combined that bare-bones data with demographic information from the census and other administrative data.

A machine-learning algorithm analysed and weighed data like census block group, age and name-based race and ethnicity information from census and mortgage data and identify patterns that would predict a language preference. Contacts were scored as to which language they would likely prefer before they were assigned to a tracer.

To test the algorithm’s effectiveness, the RegLab worked with Santa Clara County to conduct a test that randomly routed half of the cases to a “language speciality team” with bilingual speakers and treated the other half with the county’s typical process. In just two months, the benefits became clear. In the test group, the time it took to complete cases dropped by nearly 14 hours over the control. Same-day completions rose by 12%, and the number of people refusing to be interviewed dipped by 4%.

Based on the results and success of this trial, Santa Clara County has expanded language matching to all of Santa Clara Public Health Department’s Case Investigation and Contact Tracing, and the state of California is contemplating adoption in the statewide system. The new approach has not only improved people’s willingness to engage in the process, but it has ensured the county’s bilingual tracers could be assigned to the contacts most likely to need them.

Before the algorithm tracers are frustrated when they would get mismatched with a contact. After the algorithm, there would be the talk of the families they had connected with, many of whom stayed on the phone only because the tracer spoke Spanish and pronounced their name correctly. When every missed contact can mean additional infections, these are significant improvements. There’s much worry in the AI community about whether machines will displace human judgment. But, this case is a model for how machines and people can integrate in complex ways that make both better.

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