Estimation of the test to test distribution as a proxy for generation interval distribution for the Omicron variant in England
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Abstract
Background
Early estimates from South Africa indicated that the Omicron COVID-19 variant may be both more transmissible and have greater immune escape than the previously dominant Delta variant. The rapid turnover of the latest epidemic wave in South Africa as well as initial evidence from contact tracing and household infection studies has prompted speculation that the generation time of the Omicron variant may be shorter in comparable settings than the generation time of the Delta variant.
Methods
We estimated daily growth rates for the Omicron and Delta variants in each UKHSA region from the 23rd of November to the 23rd of December 2021 using surveillance case counts by date of specimen and S-gene target failure status with an autoregressive model that allowed for time-varying differences in the transmission advantage of the Delta variant where the evidence supported this. By assuming a gamma distributed generation distribution we then estimated the generation time distribution and transmission advantage of the Omicron variant that would be required to explain this time varying advantage. We repeated this estimation process using two different prior estimates for the generation time of the Delta variant first based on household transmission and then based on its intrinsic generation time.
Results
Visualising our growth rate estimates provided initial evidence for a difference in generation time distributions. Assuming a generation time distribution for Delta with a mean of 2.5-4 days (90% credible interval) and a standard deviation of 1.9-3 days we estimated a shorter generation time distribution for Omicron with a mean of 1.5-3.2 days and a standard deviation of 1.3-4.6 days. This implied a transmission advantage for Omicron in this setting of 160%-210% compared to Delta. We found similar relative results using an estimate of the intrinsic generation time for Delta though all estimates increased in magnitude due to the longer assumed generation time.
Conclusions
We found that a reduction in the generation time of Omicron compared to Delta was able to explain the observed variation over time in the transmission advantage of the Omicron variant. However, this analysis cannot rule out the role of other factors such as differences in the populations the variants were mixing in, differences in immune escape between variants or bias due to using the test to test distribution as a proxy for the generation time distribution.
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SciScore for 10.1101/2022.01.08.22268920: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our analysis had several key limitations. Firstly, the surveillance data sources we used to estimate daily growth rates had the potential to bias our analysis both because SGTF may be an imperfect proxy for Omicron and because we used public case count data which do not include reinfections. We made no adjustment for the background rate of SGTF observed for Delta or other factors that might lead to SGTF, or a confirmed detected S-gene …
SciScore for 10.1101/2022.01.08.22268920: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our analysis had several key limitations. Firstly, the surveillance data sources we used to estimate daily growth rates had the potential to bias our analysis both because SGTF may be an imperfect proxy for Omicron and because we used public case count data which do not include reinfections. We made no adjustment for the background rate of SGTF observed for Delta or other factors that might lead to SGTF, or a confirmed detected S-gene observed with Omicron. The exclusion of known reinfections could also lead to bias in our estimates if the proportion of infections that are reinfections varies over time and between variants. Changes in testing practice may also impact our results, especially if this occurs differentially in populations where different proportions of each variant are circulating. In our first study[5], we explored the potential for sampling bias for cases with known S-gene status and found little evidence that this was present. Our generation time and transmission advantage estimates for Omicron also have limitations. A major limitation is that our estimated generation time distributions are in fact the distributions from a positive test to resulting positive tests rather than the true generation time distribution which would be based on infection times. This means that our estimates may be biased if those secondary infections test positive prior to the original (primary) infected case, or may be biased by the epidemic phase[13]. In addition to this, our model ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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Results from scite Reference Check: We found no unreliable references.
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