To catch the most COVID-19 cases, testing policies should vary based on demand, study finds

Cars line up for COVID-19 testing outside Hard Rock Stadium in Miami Gardens, Fla., on Tuesday, Jan. 5, 2021. (AP Photo/Wilfredo Lee)

With months to go in the mass rollout of COVID-19 vaccines, testing is still critical to control the surging pandemic. Yet with testing capacity still limited in some areas, health authorities have struggled with how to catch the most cases: Test on a first-come, first-served basis, or prioritize testing for those with symptoms? Provide free testing for all?

In a recent working paper, Berkeley Haas Asst. Prof. Luyi Yang found that free testing for all—with no prioritization—can backfire when demand gets too high. Along with co-authors Shiliang Cui and Zhongbin Wang, Yang developed a model to project how testing facilities should schedule and price COVID testing to reduce infection rates.

“Long lines may discourage people from getting tested promptly, which of course can be dangerous from a public health standpoint,” Yang says. “My co-authors and I figured there had to be a way to make testing lines more efficient.”

Long lines may discourage people from getting tested promptly, which of course can be dangerous from a public health standpoint.

Testing centers in some cities, like San Francisco or Washington DC, have chosen to give people equal priority regardless of whether they have any symptoms. Other agencies, like the French government, have given priority to symptomatic people.

Yang and his co-authors’ model predicted that free testing without prioritizing those more at risk does not identify the most cases if testing demand is high. That’s because when testing facilities become overly congested, free testing results in too many asymptomatic people getting tested at the expense of symptomatic people, who are most likely to carry the virus. They concluded that when demand is higher than capacity, charging a fee increases the likelihood of testing more symptomatic people, because asymptomatic people’s decisions are more sensitive to price.

Conversely, they found that if a priority testing policy is used, testing should be free. When people with symptoms are given priority, the decisions of asymptomatic people are no longer a factor in their decision whether to get tested—even if lines are long, they’ll get to move to the front. As a result, charging a fee will only discourage both symptomatic and asymptomatic people from getting tested. This policy is exactly what the French government does: Testing is free for all, even those with no symptoms, but symptomatic or high-risk people are given priority.

Policies to detect the most cases

This analysis raised the question of which approach testing facilities should take. Yang said that the most important move facilities should make is to be flexible with their scheduling policy.

When testing demand is low and there’s no wait time, the researchers’ model predicts that either policy will be effective. As testing demand surges, priority testing for symptomatic people catches more cases.

However, when there is a moderate level of demand, the researchers’ findings suggest that it does not make sense to give priority to symptomatic people. High-risk people are still likely to get tested, but the hassle of waiting might push low-risk people away, reducing the chance of identifying asymptomatic carriers. In those situations, the best policy is in fact to prioritize people without symptoms.

“All else equal, people without symptoms are less motivated to seek a test, so if there is enough capacity, we would like to create an incentive for more of them to get tested,” Yang says.

All else equal, people without symptoms are less motivated to seek a test, so if there is enough capacity, we would like to create an incentive for more of them to get tested.

Though there are certainly other factors that determine the spread of the disease, Yang believes there are actionable items that testing facilities can take depending on the demand for testing.

“The high-level takeaway is that the optimal testing policy depends on the level of testing demand relative to testing capacity, so it should vary by region and evolve over time. That is, one should caution against a one-size-fits-all approach,” Yang says.

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