Research Open Access
Like 0



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.


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.


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.


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.


Our results suggest that Cq values of screening tests performed in the general population generate testable hypotheses and help improve short-term predictions for epidemic surveillance.


Article metrics loading...

Loading full text...

Full text loading...



  1. Hasell J, Mathieu E, Beltekian D, Macdonald B, Giattino C, Ortiz-Ospina E, et al. A cross-country database of COVID-19 testing. Sci Data. 2020;7(1):345.  https://doi.org/10.1038/s41597-020-00688-8  PMID: 33033256 
  2. He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020;26(5):672-5.  https://doi.org/10.1038/s41591-020-0869-5  PMID: 32296168 
  3. Néant N, Lingas G, Le Hingrat Q, Ghosn J, Engelmann I, Lepiller Q, et al. Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort. Proc Natl Acad Sci USA. 2021;118(8):e2017962118.  https://doi.org/10.1073/pnas.2017962118  PMID: 33536313 
  4. Michalakis Y, Sofonea MT, Alizon S, Bravo IG. SARS-CoV-2 viral RNA levels are not ‘viral load’. Trends Microbiol. 2021;29(11):970-2.  https://doi.org/10.1016/j.tim.2021.08.008  PMID: 34535373 
  5. French Microbiology Society (SFM). Avis du 25 septembre 2020 de la Société Française de Microbiologie (SFM) relatif à l’interprétation de la valeur de Ct (estimation de la charge virale) obtenue en cas de RT-PCR SARS-CoV-2 positive sur les prélèvements cliniques réalisés à des fins diagnostiques ou de dépistage. [Opinion on 25 September 2020 of the French Society of Microbiology (SFM) relating to the interpretation of the Ct value (estimate of the viral load) obtained in the event of a positive SARS-CoV-2 RT-PCR on clinical samples taken for diagnostic or screening purposes]. Paris: SFM; 2021. French. Available from: https://www.sfm-microbiologie.org/wp-content/uploads/2021/01/Avis-SFM-valeur-Ct-excre%CC%81tion-virale-_-Version-def-14012021_V4.pdf
  6. Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol. 2013;178(9):1505-12.  https://doi.org/10.1093/aje/kwt133  PMID: 24043437 
  7. Thompson RN, Stockwin JE, van Gaalen RD, Polonsky JA, Kamvar ZN, Demarsh PA, et al. Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics. 2019;29:100356.  https://doi.org/10.1016/j.epidem.2019.100356  PMID: 31624039 
  8. Caswell H. Matrix population models: construction, analysis and interpretation. Sunderland: Sinauer Associates, Inc.; 1989.
  9. Hay JA, Kennedy-Shaffer L, Kanjilal S, Lennon NJ, Gabriel SB, Lipsitch M, et al. Estimating epidemiologic dynamics from cross-sectional viral load distributions. Science. 2021;373(6552):eabh0635.  https://doi.org/10.1126/science.abh0635  PMID: 34083451 
  10. Sofonea MT, Reyné B, Elie B, Djidjou-Demasse R, Selinger C, Michalakis Y, et al. Memory is key in capturing COVID-19 epidemiological dynamics. Epidemics. 2021;35:100459.  https://doi.org/10.1016/j.epidem.2021.100459  PMID: 34015676 
  11. Salje H, Tran Kiem C, Lefrancq N, Courtejoie N, Bosetti P, Paireau J, et al. Estimating the burden of SARS-CoV-2 in France. Science. 2020;369(6500):208-11.  https://doi.org/10.1126/science.abc3517  PMID: 32404476 
  12. Walker AS, Pritchard E, House T, Robotham JV, Birrell PJ, Bell I, et al. Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time. eLife. 2021;10:e64683.  https://doi.org/10.7554/eLife.64683  PMID: 34250907 
  13. Finkel Y, Mizrahi O, Nachshon A, Weingarten-Gabbay S, Morgenstern D, Yahalom-Ronen Y, et al. The coding capacity of SARS-CoV-2. Nature. 2021;589(7840):125-30.  https://doi.org/10.1038/s41586-020-2739-1  PMID: 32906143 
  14. Yang Y, Zhao Y, Zhang F, Zhang L, Li L. COVID-19 in elderly adults: clinical features, molecular mechanisms, and proposed strategies. Aging Dis. 2020;11(6):1481-95.  https://doi.org/10.14336/AD.2020.0903  PMID: 33269102 
  15. Euser S, Aronson S. Manders, I Lelyveld Sv, Herpers B, Sinnige J, et al. SARS-CoV-2 viral load distribution reveals that viral loads increase with age: a retrospective cross-sectional cohort study. medRxiv. 2021.01.15.21249691. preprint.  https://doi.org/10.1101/2021.01.15.21249691  https://doi.org/10.1101/2021.01.15.21249691 
  16. Jones TC, Mühlemann B, Veith T, Biele G, Zuchowski M, Hofmann J, et al. An analysis of SARS-CoV-2 viral load by patient age. medRxiv. 2020.06.08.20125484. preprint.  https://doi.org/10.1101/2020.06.08.20125484  https://doi.org/10.1101/2020.06.08.20125484 
  17. Ogando NS, Dalebout TJ, Zevenhoven-Dobbe JC, Limpens RWAL, van der Meer Y, Caly L, et al. SARS-coronavirus-2 replication in Vero E6 cells: replication kinetics, rapid adaptation and cytopathology. J Gen Virol. 2020;101(9):925-40.  https://doi.org/10.1099/jgv.0.001453  PMID: 32568027 
  18. Dearlove B, Lewitus E, Bai H, Li Y, Reeves DB, Joyce MG, et al. A SARS-CoV-2 vaccine candidate would likely match all currently circulating variants. Proc Natl Acad Sci USA. 2020;117(38):23652-62.  https://doi.org/10.1073/pnas.2008281117  PMID: 32868447 
  19. Haim-Boukobza S, Roquebert B, Trombert-Paolantoni S, Lecorche E, Verdurme L, Foulongne V, et al. Detecting Rapid Spread of SARS-CoV-2 Variants, France, January 26-February 16, 2021. Emerg Infect Dis. 2021;27(5):1496-9.  https://doi.org/10.3201/eid2705.210397  PMID: 33769253 
  20. Alizon S, Haim-Boukobza S, Foulongne V, Verdurme L, Trombert-Paolantoni S, Lecorche E, et al. Rapid spread of the SARS-CoV-2 Delta variant in some French regions, June 2021. Euro Surveill. 2021;26(28):2100573.  https://doi.org/10.2807/1560-7917.ES.2021.26.28.2100573  PMID: 34269174 
  21. Davies NG, Jarvis CI, Edmunds WJ, Jewell NP, Diaz-Ordaz K, Keogh RH, et al. Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7. Nature. 2021;593(7858):270-4.  https://doi.org/10.1038/s41586-021-03426-1  PMID: 33723411 
  22. Faria NR, Mellan TA, Whittaker C, Claro IM, Candido DDS, Mishra S, et al. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Science. 2021;372(6544):815-21.  https://doi.org/10.1126/science.abh2644  PMID: 33853970 
  23. Roquebert B, Haim-Boukobza S, Trombert-Paolantoni S, Lecorche E, Verdurme L, Foulongne V, et al. SARS-CoV-2 variants of concern are associated with lower RT-PCR amplification cycles between January and March 2021 in France. medRxiv. 2021.03.19.21253971. preprint.  https://doi.org/10.1101/2021.03.19.21253971  https://doi.org/10.1101/2021.03.19.21253971 
  24. Brown CM, Vostok J, Johnson H, Burns M, Gharpure R, Sami S, et al. Outbreak of SARS-CoV-2 infections, including COVID-19 vaccine breakthrough infections, associated with large public gatherings - Barnstable County, Massachusetts, July 2021. MMWR Morb Mortal Wkly Rep. 2021;70(31):1059-62.  https://doi.org/10.15585/mmwr.mm7031e2  PMID: 34351882 
  25. Blanquart F, Abad C, Ambroise J, Bernard M, Cosentino G, Giannoli J-M, et al. Characterisation of vaccine breakthrough infections of SARS-CoV-2 Delta and Alpha variants and within-host viral load dynamics in the community, France, June to July 2021. Euro Surveill. 2021;26(37):2100824.  https://doi.org/10.2807/1560-7917.ES.2021.26.37.2100824  PMID: 34533119 
  26. Kraemer MUG, Hill V, Ruis C, Dellicour S, Bajaj S, McCrone JT, et al. Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence. Science. 2021;373(6557):889-95.  https://doi.org/10.1126/science.abj0113  PMID: 34301854 
  27. Pullano G, Di Domenico L, Sabbatini CE, Valdano E, Turbelin C, Debin M, et al. Underdetection of cases of COVID-19 in France threatens epidemic control. Nature. 2021;590(7844):134-9.  https://doi.org/10.1038/s41586-020-03095-6  PMID: 33348340 
  28. Selinger C, Choisy M, Alizon S. Predicting COVID-19 incidence in French hospitals using human contact network analytics. Int J Infect Dis. 2021;111:100-7.  https://doi.org/10.1016/j.ijid.2021.08.029  PMID: 34403783 

Data & Media loading...

Supplementary data

Submit comment
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error