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More Testing = Fewer Cases of COVID-19

February 16, 2021

Early in the COVID-19 (COVID) pandemic, rising numbers of domestic COVID cases were often explained by an increase in testing for the disease.  

Business people wearing masks

Indeed, performing a larger number of COVID tests will logically result in a higher number of positive test results by testing a larger percentage of the population. However, this explanation caused some people to believe that COVID testing actually creates more cases of the disease.

COVID testing does not create more cases of the disease. Instead, COVID testing merely detects the disease in people that are infected with SARS-CoV-2 (CoV-2), the virus that causes COVID, and does not cause people to become infected. In fact, a recent study shows that increased COVID testing actually decreases future COVID cases within a population, likely by identifying infected people early in the course of their infection and decreasing the likelihood they spread the disease to others.1

Detecting CoV-2 infection during the pandemic is complicated by the fact that an estimated 40% of people with COVID are asymptomatic and may be infectious, meaning that checkpoint screenings of employees or students will only detect symptomatic cases.2 Pre- or asymptomatic cases must instead be detected through organizational COVID testing to limit the spread of the disease in the population, while testing costs can quickly become prohibitive. Many organizations, including businesses and educational institutions, have struggled in determining the optimal COVID testing strategy for their workplaces and classrooms.

One testing strategy, sentinel testing, provides a viable alternative to organizations that do not have the financial means or ability to test every employee or student on-site every day. Sentinel testing regularly tests a different random sample of a population to detect and isolate COVID-infected individuals and estimate the overall infection rate in the community. This testing strategy decreases the number of infected people in the population and allows organizational leaders to assess the effectiveness of current mitigation strategies without regularly testing every person in the population.

The most efficient sentinel testing strategy for each population can depend on the size, public exposure, community transmission and virus mitigation measures, among other variables, of each organization. It can be somewhat surprising, however, the extent to which the disease burden on a population can be reduced when only a fraction of the population is regularly tested.

Two researchers from Mexico recently developed an efficient COVID testing strategy to detect pre- and asymptomatic COVID cases in any workplace. The study authors measured the COVID burden on workforces and other populations in terms of infected-person-days, or IPDs. In their model, an organization uses Sanitary Checkpoints to detect COVID symptoms in people entering the premises, removing all symptomatic individuals from the site. Once the population is limited to only asymptomatic people, five percent of the population is randomly selected each day for COVID testing. In the model’s more efficient testing protocol, however, each testing group omits people that have tested negative for COVID in the past 15 days and replaces them with randomly chosen people that have not tested negative in the past 15 days. &Based on the study calculations, testing in this manner can reduce the number of IPDs in the population by up to 30%. Likewise, if only 10% of the asymptomatic population is tested each day, and COVID-negative individuals are omitted from the testing pool for 15 days, the number of IPDs can be reduced up to 50% within the organization.  

For smaller testing populations that cannot test a full five or ten percent of their group after omitting COVID-negative individuals, an organization can instead omit people that tested negative for COVID in the past 14 days, 13 days, and progressively lower, until a full five or ten percent of the population is tested. Additionally, this particular testing model makes some large assumptions in their calculations, which the study authors readily admit: They assume all COVID infections last 15 days and that COVID testing will never result in a false-negative, or miss a person with active CoV-2 infection. In reality, these assumptions cannot always be true, but for the purposes of modeling and determining testing efficiency in a best-case-scenario, the authors maintained these constants throughout their study.3

No one ever said that workplace COVID testing during the pandemic would be easy. Fortunately, employing a straightforward testing strategy, such as sentinel testing of individuals that haven’t tested negative for COVID in the past 15 days, can significantly increase the efficiency of testing, and may reduce the infection burden within the organization more than an entirely random testing approach. And importantly, this simple strategy can be implemented with both molecular-based (PCR) and rapid COVID testing methods to improve the cost-efficiency of either testing platform during these less-than-simple times.

Kailos Genetics has designed a fully customizable COVID-19 sentinel testing program, Assure SentinelTM, to allow organizations of any size to operate safely during the pandemic.  Click here to learn more about the program or contact us with any questions you may have regarding COVID-19 testing.

 

1Ahmed, A.  Does COVID-19 create more cases? An empirical evidence on the importance of mass testing during a pandemic.  medRxiv.  December 26, 2020.  https://www.medrxiv.org/content/10.1101/2020.12.23.20248740v1.full.pdf 
2Centers for Disease Control and Prevention. COVID-19 pandemic planning scenarios. September 10, 2020.  https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html

3Hernandez, X. and Valentinotti, S.  On and optimal testing strategy for workplace settings operating during the COVID-19 pandemic. medRxiv.  December 24, 2020.  https://www.medrxiv.org/content/10.1101/2020.12.22.20248752v1.full.pdf