AI Assists Pooled Covid-19 Testing

MORE THAN A YEAR into the Covid-19 pandemic, efficient testing for the coronavirus remains relevant as variants spread and vaccinations have been slow to roll out in many parts of the world. That is why some academic groups and companies have been using a combination of math and artificial intelligence to improve pooled testing, which began as a proposal to screen the U.S. military for syphilis during World War II, and has since been used for blood donations and to conserve sometimes scarce testing supplies in HIV surveillance.

Pooled testing for Covid-19 enables such efficiency by taking the diluted samples from nasal swabs of two or more people and screening all the samples together using a single test kit. If the pool comes back negative, then every sample included in the pool can be assumed to be negative. If the pool comes back positive, the lab must usually go back and retest each sample individually to figure out who is infected.

At first glance, pooled testing seems like a no-brainer during a pandemic. Getting more tests done with fewer supplies could prove handy — for instance, at times like last January, when more than half of labs surveyed in the United States still reported testing supply shortages. Pooled testing could also make mass testing faster — China has already used it to screen millions of people during smaller Covid-19 outbreaks. But pooled testing’s efficiency drops off significantly as positivity rates rise and there are more contaminated pools.

One way around that may be to use what some researchers call smart pooled testing, which uses mathematically sophisticated techniques — sometimes augmented by artificial intelligence — to boost the efficiency of pooled testing. Many research groups around the world have published papers about how such smart pooling can identify those likely to be infected to reduce the number of positive pools and potentially even sidestep the need for retesting altogether. But most labs still don’t use pooled testing, let alone smart pooled testing.

The story is different in Israel, where several labs began using smart pooled testing based on both mathematical and AI techniques last winter. The mathematical technique was developed by Israeli researchers just several weeks after the World Health Organization declared Covid-19 a pandemic in March 2020. By spreading individual samples across multiple pools to create unique combinations, the researchers showed they could identify positive samples by simply comparing the pattern of the positive pools.

Turning that academic exercise into something that labs would adopt was another matter. “We already had proof-of-concept data that this is useful,” says Tomer Hertz, a computational immunologist at Ben-Gurion University in Israel. “But to get to a point where a lab is actually going to run what we’re doing took about nine months.”

One commercial lab operated by the biotech company Ilex Medical has since been using this combinatorial pooling approach to reduce the need for individual retesting. Two other labs operated by Clalit Health Services, Israel’s largest state-mandated health maintenance organization, are also using it together with an AI pre-screening technique that helps to prevent high-risk samples from contaminating the pools. Altogether, six robots programmed to implement the pooling strategy are helping them process up to 7,000 tests each day in Israel and more than 400,000 tests had been performed by mid-April.

Such operations could yield useful lessons for many countries — including the U.S., where some labs have used standard pooling, and Colombia, where a homegrown smart pooling effort is looking to take hold — in dealing with both Covid-19 and future pandemics.

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