Machine Learning for COVID-19 Testing

Last June, the FDA authorized the use of “pooled testing” for identifying COVID-19 infections. The method allows up to four swabs to be tested at once. A “bundled sample.” This strategy is expected to expand testing to larger sections of the population. If the sample comes back positive, then all the individuals in that sample will need to be tested separately.  If, however, the sample comes back clean, that’s four people who do not need to be tested further, saving public health officials time and money.

The FDA said it expects pooled testing to allow virus identification with fewer tests, which means more tests could be run at once, fewer testing supplies would be consumed and patients could likely receive the results more quickly. This strategy will be most efficient in areas where the outbreak is under control, meaning where only a small percentage of test subjects are expected to be infected, the FDA acknowledged.

Researchers from the University of California, Berkeley, think even more people can be tested by combining pooling with machine learning tools that estimate the risk of COVID for each person tested, according to an article published in the MIT Technology Review.

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