New COVID-19 testing strategy could lead to fewer infections

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In a new study, researchers developed a new algorithm that could help leaders make better decisions on how many symptomatic and asymptomatic people to test with rationed daily tests, and at what stage of the pandemic.

The model’s simulated testing strategies resulted in approximately 40% fewer infections.

The research was conducted by a team at Penn State’s College of Information Sciences and Technology.

When the COVID-19 pandemic spread across the globe, governments and institutions worldwide faced hard decisions about who to test for the virus—and when—with limited testing supplies.

In the study, the team used an artificial intelligence model to develop a sequential policy for distributing tests among a population.

Their model, called Design of Optimal COVID-19 Testing Oracle, or DOCTOR, was measured against other existing testing strategies used by governments and institutions.

Many of these other strategies are static and nonadaptive, potentially causing significant shortcomings in their effectiveness in containing COVID-19.

In a two-phased approach, DOCTOR first suggests spending more effort in testing symptomatic individuals, allocating approximately 65% of its available testing kits for individuals presenting symptoms.

Over time, as the number of symptomatic individuals diminishes due to these patients moving to quarantine or hospital settings, DOCTOR shifts its attention to asymptomatic testing, gradually increasing the number of testing kits allocated to asymptomatic individuals as decision points proceed.

When applied in a simulation to the city of Santiago in Panama—a country with the world’s highest rate of COVID-19 infections per capita—the model’s testing strategy outperformed state-of-the-art baselines by achieving approximately 40% fewer COVID-19 infections.

This illustrates the benefit of having an adaptive strategy, and even more so with new variants of the virus emerging.

The team also underscored at what stage of the pandemic an AI-driven testing strategy for COVID-19 would be most beneficial.

The research shows that the use of AI is most beneficial when the pandemic spread is intermediate—meaning it’s not too severe and it’s not too slow.

As COVID-19 is currently in an intermediate stage in many places worldwide, it is an optimal time for governments and institutions to consider an AI-driven testing strategy.

Additionally, the model could be useful in guiding decision-makers in the event of a future pandemic.

One researcher of the study is Amulya Yadav, PNC Technologies Career Development Assistant Professor at the College of IST.

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