Story at a glance

  • A new modeling study suggests that testing frequency could be more important than how sensitive the tests are in the context of COVID-19 surveillance.
  • Delay in receiving test results back could also be important to consider.
  • Although more data would be needed to inform policy, this study helps to increase understanding of the factors at play during an outbreak.

How often people get tested for coronavirus may be more important than the sensitivity of those tests, suggests a new study posted to a preprint server. Although the study is based on models and simulations, if this proves to be true in the real world it could affect how leaders and health experts decide to move forward with coronavirus surveillance.

In the study, researchers developed models to understand how differences in coronavirus testing frequency and sensitivity of those tests could affect detection of coronavirus cases as part of a surveillance strategy. They posted a preliminary version of the study on a preprint server, which is an online repository for researchers to post their papers ahead of peer-review and publication in an academic journal. This means that the research hasn’t been fully vetted yet by other researchers who were not involved with the work.

Dan Larremore, the lead author on the paper, explains the results in this thread:

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One of the main findings from the models is that there is a short window of time for when a polymerase chain reaction (PCR) diagnostic test with a higher sensitivity is better than one with lower sensitivity. In this case, if the viral load in the person’s body is increasing exponentially you are better off testing them once a week and reducing the delay for receiving test results.

That’s not to say that higher sensitivity tests should not be used, says Larremore in a tweet.


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The researchers also look at what effect removing infectious individuals from the population has on the progression of the outbreak. If they are infectious and isolated early, then that could lower the population’s R0, the reproduction number or the average number of new cases from a single case.

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Although it shouldn’t be used to predict the future or build policies, a study like this is useful for understanding how key factors may interact and influence each other. It’s helpful for learning more about the potential trade-offs and what we could focus on more closely for coronavirus surveillance. For example, experts could look at this study and then decide to collect data that would let us know more about what testing frequency would be ideal. The frequency and sensitivity of tests could be different based on the situation, like at a university or a retail store.

For up-to-date information about COVID-19, check the websites of the Centers for Disease Control and Prevention and the World Health Organization. For updated global case counts, check this page maintained by Johns Hopkins University.

You can follow Chia-Yi Hou on Twitter.


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Published on Jul 07, 2020