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Review 1: "Viral load dynamics of SARS-CoV-2 Delta and Omicron variants following multiple vaccine doses and previous infection"

Reviewer: Lee Kennedy-Shaffer (Vassar College) | 📒📒📒◻️◻️

Published onMay 25, 2022
Review 1: "Viral load dynamics of SARS-CoV-2 Delta and Omicron variants following multiple vaccine doses and previous infection"

RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.

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Review:

Woodbridge et al. seek to investigate the effectiveness of SARS-CoV-2 vaccines against infectivity, using viral load (VL) and qRT-PCR cycle threshold (Ct) values as proxies for infectiousness [1]. They approach this using data from Israel during the recent Delta and Omicron waves of the SARS-CoV-2 pandemic, linking laboratory results from positive cases during those waves to the vaccination status (including the number of doses and time since the last dose) and demographic factors (age and sex) of the tested individuals. The authors find a strong association between vaccination and increased mean Ct value (corresponding to lower viral load), adjusted through linear regression for calendar time and collected demographics, for the Delta variant, and for the Omicron variant for individuals who recently had a third dose. They also find a similar protective association with recovery from infection, which appears to have less waning. From these results, the authors conclude that “vaccination-related immunity has only a negligible long term (>70-days) effect on Ct value” [1].

The importance of this question is clear for public health and policy-making; understanding the vaccine effect on transmission is an urgent question that was not fully answered by the randomized vaccine trials. To answer the question using Ct values, however, requires several strong assumptions and specific sources of the laboratory data.

First of all, this is an observational study conducted in a population with a high vaccination rate. This can lead to substantial confounding of the results shown, both between the vaccinated, previously-infected, and immunologically-naive populations and among the vaccinated group by a number of doses and time since vaccination. For example, if individuals with weaker immune systems were prioritized for early vaccination, they may be overrepresented in the group with a long time since vaccination, even adjusting for age and sex. Residual confounding by age may also occur due to the course categorization used. As such, any interpretation of a causal effect here should be heavily caveated and interpreted with appropriate limitations.

Secondly, the reason for testing in the samples obtained is not clear, raising the question of its suitability as a proxy for infectiousness. Since, as the authors note, VL and Ct values are very sensitive to the time since infection, the ideal study would involve individuals randomly sampled on the same date to get a cross-section of times since infection [2–4]. Otherwise, vaccination could reduce overall infectiousness by reducing the duration of PCR positivity without reducing peak viral load. In the absence of that data, however, it is important to know the source of the tests: does it include only voluntary testing, testing upon hospital admission, routine surveillance testing, etc.? If the reason for testing differs between vaccinated and unvaccinated individuals, this can cause further confounding. Without this information, it is impossible to assess how representative these results may be. Epidemic dynamics can also affect the observed Ct values, as recent infections will predominate in a rapidly-growing epidemic, such as the Omicron wave assessed here. Since viral load dynamics are rapid, different growth rates between the vaccinated and unvaccinated populations within the seven-day windows may cause residual confounding [5,6].

Finally, there are many assumptions needed to connect vaccine effectiveness on viral load to an assessment of vaccine effectiveness on transmission. For one thing, if a vaccine prevents primary infections, that will have a large impact on transmission as well; this is not assessed here and cannot be by the data given [4,7]. Moreover, assumptions about Ct value acting as a mediator of the vaccine’s effect on infectiousness, as well as accounting for other potential factors like behavioral change or change in symptoms, must be made [4,7]. Whether the vaccine changes the relationship between Ct values and infectiousness is an open one, especially for new variants, and not addressed in this manuscript [6,7].

This manuscript addresses an important question and attempts to make use of the value inherent in semi-quantitative PCR data. Estimating vaccine effectiveness from such data, however, requires strong assumptions and a purposeful study design to ensure the appropriate values are used. While this manuscript suggests trends that may hold and should prompt further research on the issue, the methods are not strong enough to justify the policy-relevant claims made.

[1] Woodbridge Y, Amit S, Huppert A, Kopelman NM. 2022. Viral load dynamics of SARS-CoV-2 Delta and Omicron variants following multiple vaccine doses and previous infection. medRxiv Preprint; doi:10.1101/2022.03.20.22272549.

[2] Rinta-Kokko H, Dagan R, Givon-Lavi N, Auranen K. 2009. Estimation of vaccine efficacy against acquisition of pneumococcal carriage. Vaccine 27: 3831–3837.

[3] Lipsitch M, Kahn R. 2021. Interpreting vaccine efficacy trial results for infection and transmission. Vaccine 39: 4082–4088.

[4] Follmann DA, Fay MP. 2022. Vaccine efficacy at a point in time. Biostatistics; doi:10.1093/biostatistics/kxac008.

[5] Hay JA, Kennedy-Shaffer L, Kanjilal S, et al. 2021. Estimating epidemiologic dynamics from cross-sectional viral load distributions. Science. 373: eabh0635.

[6] Jones TC, Biele G, Mühlemann B, et al. 2021. Estimating infectiousness throughout SARS-CoV-2 infection course. Science. 373: eabi5273.


[7] Kennedy-Shaffer L, Kahn R, Lipsitch M. 2021. Estimating vaccine efficacy against transmission via effect on viral load. Epidemiology 32: 820–828.

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