Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020

Background: The COVID-19 pandemic has led to an unprecedented daily use of RT-PCR tests. These tests are interpreted qualitatively for diagnosis, and the relevance of the test result intensity, i.e. the number of quantification cycles (Cq), is debated because of strong potential biases. Aim: We explored the possibility to use Cq values from SARS-CoV-2 screening tests to better understand the spread of an epidemic and to better understand the biology of the infection. Methods: We used linear regression models to analyse a large database of 793,479 Cq values from tests performed on more than 2 million samples between 21 January and 30 November 2020, i.e. the first two pandemic waves. We performed time series analysis using autoregressive integrated moving average (ARIMA) models to estimate whether Cq data information improves short-term predictions of epidemiological dynamics. Results: Although we found that the Cq values varied depending on the testing laboratory or the assay used, we detected strong significant trends associated with patient age, number of days after symptoms onset or the state of the epidemic (the temporal reproduction number) at the time of the test. Furthermore, knowing the quartiles of the Cq distribution greatly reduced the error in predicting the temporal reproduction number of the COVID-19 epidemic. Conclusion: Our results suggest that Cq values of screening tests performed in the general population generate testable hypotheses and help improve shortterm predictions for epidemic surveillance.

Automated summary

This study explores the use of Cq values from SARS-CoV-2 screening tests to better understand the spread of an epidemic and the biology of the infection. The results show significant associations between Cq values and patient age, number of days after symptom onset, and the state of the epidemic. The study suggests that Cq values can improve short-term predictions for epidemic surveillance.