Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants
Curation statements for this article:-
Curated by eLife
Evaluation Summary:
This manuscript will be of broad interest to readers interested in understanding characteristics of variants in ongoing epidemics that lead to faster (or slower) growth rates, and will be of particular interest to those wishing to understand the factors leading to selection of SARS-CoV-2 variants. The transmission advantage of a novel strain of a pathogen depends not only on its relative transmissibility, but also on its generation time relative to other strains; the relation between transmissibility, transmission advantage and generation time changes across different phases of the epidemic, enabling statistical inferences to be made about both the transmissibility advantage and generation time of an emerging variant. The method is supported by simulation studies and applied to the Alpha and Delta SARS-CoV-2 variants to show that selection was likely driven by changes in transmissibility rather than changes in the generation time.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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- Evaluated articles (eLife)
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Abstract
Evaluating the characteristics of emerging SARS-CoV-2 variants of concern is essential to inform pandemic risk assessment. A variant may grow faster if it produces a larger number of secondary infections (“R advantage”) or if the timing of secondary infections (generation time) is better. So far, assessments have largely focused on deriving the R advantage assuming the generation time was unchanged. Yet, knowledge of both is needed to anticipate the impact. Here, we develop an analytical framework to investigate the contribution of both the R advantage and generation time to the growth advantage of a variant. It is known that selection on a variant with larger R increases with levels of transmission in the community. We additionally show that variants conferring earlier transmission are more strongly favored when the historical strains have fast epidemic growth, while variants conferring later transmission are more strongly favored when historical strains have slow or negative growth. We develop these conceptual insights into a new statistical framework to infer both the R advantage and generation time of a variant. On simulated data, our framework correctly estimates both parameters when it covers time periods characterized by different epidemiological contexts. Applied to data for the Alpha and Delta variants in England and in Europe, we find that Alpha confers a+54% [95% CI, 45–63%] R advantage compared to previous strains, and Delta +140% [98–182%] compared to Alpha, and mean generation times are similar to historical strains for both variants. This work helps interpret variant frequency dynamics and will strengthen risk assessment for future variants of concern.
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Evaluation Summary:
This manuscript will be of broad interest to readers interested in understanding characteristics of variants in ongoing epidemics that lead to faster (or slower) growth rates, and will be of particular interest to those wishing to understand the factors leading to selection of SARS-CoV-2 variants. The transmission advantage of a novel strain of a pathogen depends not only on its relative transmissibility, but also on its generation time relative to other strains; the relation between transmissibility, transmission advantage and generation time changes across different phases of the epidemic, enabling statistical inferences to be made about both the transmissibility advantage and generation time of an emerging variant. The method is supported by simulation studies and applied to the Alpha and Delta SARS-CoV-2 variants …
Evaluation Summary:
This manuscript will be of broad interest to readers interested in understanding characteristics of variants in ongoing epidemics that lead to faster (or slower) growth rates, and will be of particular interest to those wishing to understand the factors leading to selection of SARS-CoV-2 variants. The transmission advantage of a novel strain of a pathogen depends not only on its relative transmissibility, but also on its generation time relative to other strains; the relation between transmissibility, transmission advantage and generation time changes across different phases of the epidemic, enabling statistical inferences to be made about both the transmissibility advantage and generation time of an emerging variant. The method is supported by simulation studies and applied to the Alpha and Delta SARS-CoV-2 variants to show that selection was likely driven by changes in transmissibility rather than changes in the generation time.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
I read with interest the manuscript "Selection for infectivity profiles in slow and fast epidemics, and the rise of SARS-CoV-2 variants". I think that the method provides an important step forward in dissecting the nature of selection during a pandemic, allowing changes in transmission rate and generation time to be estimated from case numbers of different variants over time.
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Reviewer #2 (Public Review):
This work presents a clear perspective on the relation between (relative) transmissibility, transmission advantage and generation time. The approach to infer these multiple parameters using the time dependence of the effective reproduction number is brilliant and innovative. Although it must be emphasised that due to the longitudinal time series required, it tends to work better for post hoc studies, rather than for real-time assessment of the features of a novel strain. Complex patterns of cross-protection between strains, or differential immunity from vaccination, are also likely to induce strong biases in this approach. Hence, this inference is probably inappropriate for scenarios like Omicron spreading among partially vaccinated/immune populations. However, if the strains offer complete cross-protection, …
Reviewer #2 (Public Review):
This work presents a clear perspective on the relation between (relative) transmissibility, transmission advantage and generation time. The approach to infer these multiple parameters using the time dependence of the effective reproduction number is brilliant and innovative. Although it must be emphasised that due to the longitudinal time series required, it tends to work better for post hoc studies, rather than for real-time assessment of the features of a novel strain. Complex patterns of cross-protection between strains, or differential immunity from vaccination, are also likely to induce strong biases in this approach. Hence, this inference is probably inappropriate for scenarios like Omicron spreading among partially vaccinated/immune populations. However, if the strains offer complete cross-protection, this work provides a new interesting method to infer the relative transmissibility and timing of transmission of different strains.
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SciScore for 10.1101/2021.12.08.21267454: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of our framework is that we only consider the impact of changing transmission on selection for variants. We do not consider the impact of interventions shortening the distribution of generation time such as isolation of positive cases and contact tracing, which also potentially change over time (21). These interventions …
SciScore for 10.1101/2021.12.08.21267454: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of our framework is that we only consider the impact of changing transmission on selection for variants. We do not consider the impact of interventions shortening the distribution of generation time such as isolation of positive cases and contact tracing, which also potentially change over time (21). These interventions would alter the selection coefficient differently, in particular would favour earlier transmission (shorter mean generation time). We do not consider either the impact of other changes in the environment. For example, the positive selection on vaccine escape variants may increase over time in the context of rapid vaccination of the population. Analogously, different levels of vaccination across countries could also affect the spatial analysis of spread of a vaccine escape variant. Lastly, we consider only one emerging variant. Our inference framework could readily be extended without additional technical complications to the frequency dynamics of several variants. In spite of these limitations, our simple framework makes minimal assumptions (exponential growth rate and stable age-of-infection structure) that proved robust when tested against a simulation model including more complicated features (build-up of immunity, isolation of positive cases, explicit epidemiological dynamics in the context of changing transmission). In conclusion, we developed a conceptual framework to study selection on variants modifying the transmission of an infectious pa...
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.
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