Characterizing the effective reproduction number during the COVID-19 epidemic: Insights from Qatar’s experience

  1. Raghid Bsat
  2. Hiam Chemaitelly
  3. Peter Coyle
  4. Patrick Tang
  5. Mohammad R. Hasan
  6. Zaina Al Kanaani
  7. Einas Al Kuwari
  8. Adeel A. Butt
  9. Andrew Jeremijenko
  10. Anvar Hassan Kaleeckal
  11. Ali Nizar Latif
  12. Riyazuddin Mohammad Shaik
  13. Gheyath K. Nasrallah
  14. Fatiha M. Benslimane
  15. Hebah A. Al Khatib
  16. Hadi M. Yassine
  17. Mohamed G. Al Kuwari
  18. Hamad Eid Al Romaihi
  19. Mohamed H. Al-Thani
  20. Abdullatif Al Khal
  21. Roberto Bertollini
  22. Laith J. Abu-Raddad
  23. Houssein H. Ayoub
1 evaluations Published on
Read the full article Related papers
This article on Sciety

Abstract

Background

The effective reproduction number, R t , is a tool to track and understand epidemic dynamics. This investigation of R t estimations was conducted to guide the national COVID-19 response in Qatar, from the onset of the epidemic until August 18, 2021.

Methods

Real-time “empirical” <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline1.gif"/> </alternatives> </inline-formula> was estimated using five methods, including the Robert Koch Institute, Cislaghi, Systrom-Bettencourt and Ribeiro, Wallinga and Teunis, and Cori et al. methods. R was also estimated using a transmission dynamics model <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline2.gif"/> </alternatives> </inline-formula> . Uncertainty and sensitivity analyses were conducted. Agreements between different R t estimates were assessed by calculating correlation coefficients.

Results

<inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline3.gif"/> </alternatives> </inline-formula> captured the evolution of the epidemic through three waves, public health response landmarks, effects of major social events, transient fluctuations coinciding with significant clusters of infection, and introduction and expansion of the B.1.1.7 variant. The various estimation methods produced consistent and overall comparable <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline4.gif"/> </alternatives> </inline-formula> estimates with generally large correlation coefficients. The Wallinga and Teunis method was the fastest at detecting changes in epidemic dynamics. <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline5.gif"/> </alternatives> </inline-formula> estimates were consistent whether using time series of symptomatic PCR-confirmed cases, all PCR-confirmed cases, acute-care hospital admissions, or ICU-care hospital admissions, to proxy trends in true infection incidence. <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline6.gif"/> </alternatives> </inline-formula> correlated strongly with <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline7.gif"/> </alternatives> </inline-formula> and provided an average <inline-formula> <alternatives> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="21264599v1_inline8.gif"/> </alternatives> </inline-formula> .

Conclusions

R t estimations were robust and generated consistent results regardless of the data source or the method of estimation. Findings affirmed an influential role for R t estimations in guiding national responses to the COVID-19 pandemic, even in resource-limited settings.

Related articles

Related articles are currently not available for this article.