Inferring variant-specific effective reproduction numbers from combined case and sequencing data
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Accurately estimating relative transmission rates of SARS-CoV-2 variants remains a scientific and public health priority. Recent studies have used the sample proportions of different variants from genetic sequence data to describe variant frequency dynamics and relative transmission rates, but frequencies alone cannot capture the rich epidemiological behavior of SARS-CoV-2. Here, we extend methods for infer- ring the effective reproduction number of an epidemic using confirmed case data to jointly estimate variant-specific effective reproduction numbers and frequencies of co-circulating variants using cases and sequences across states in the US from January 2021 to March 2022. Our method can be used to infer structured relationships be- tween effective reproduction numbers across time series allowing us to estimate fixed variant-specific growth advantages. We use this model to estimate the effective reproduction number of SARS-CoV-2 Variants of Concern and Variants of Interest in the United States and estimate consistent growth advantages of particular variants across different locations.
Article activity feed
-
-
-
SciScore for 10.1101/2021.12.09.21267544: (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
Software and Algorithms Sentences Resources Inference: The model is implemented in NumPyro [26] in Python and approximate Bayesian inference was conducted using Stochastic Variational Inference [27] using the ADAM optimizer [28] with a learning rate of 0.01. NumPyrosuggested: NonePythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:With this mind, this work is not without limitations. The underlying transmission model is deterministic and does not account for demographic …
SciScore for 10.1101/2021.12.09.21267544: (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
Software and Algorithms Sentences Resources Inference: The model is implemented in NumPyro [26] in Python and approximate Bayesian inference was conducted using Stochastic Variational Inference [27] using the ADAM optimizer [28] with a learning rate of 0.01. NumPyrosuggested: NonePythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:With this mind, this work is not without limitations. The underlying transmission model is deterministic and does not account for demographic stochasticity and over-dispersion in transmission which has been documented in SARS-CoV-2 transmission [17]. As with all methods which depend on parameterizations of the generation time, misspecification of the generation time can be lead to biased estimates of the effective reproduction number or growth advantages [18]. In order to quantify this source of error, we derive an equation relating our inferred growth advantages, the epidemic growth rates, and the mean and standard deviation of the generation time distribution. This source of error can be partially combatted by converting effective reproduction numbers to their corresponding epidemic growth rates under the generation time assumption. (see Supplement Appendix) There is also a general need to account for biases in the case data which may not faithfully describe the infection dynamics of SARS-CoV-2 due to changes in case ascertainment rate, as possibly caused by differences in testing intensity, infection severity among other reasons. However, we suspect that case ascertainment remained largely consistent from January to October 2021. We do not explicitly model multiple introductions of variants which can play an important role in variants establishing themselves in different geographies at low infection counts and could bias our estimates of the effective reproduction number i...
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.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-