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Review 2: "Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts"

This preprint offers a model for directing vaccine allocation using seroprevalence data obtain from Rhode Island and Massachusetts. Reviewers recommend clarifying some model assumptions, but find the work well-crafted and significant in its contribution.

Published onMar 01, 2021
Review 2: "Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts"
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Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts
Description

AbstractAs three SARS-CoV-2 vaccines come to market in Europe and North America in the winter of 2020-2021, distribution networks will be in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation is critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs require that distribution is prioritized to the elderly, health-care workers, teachers, essential workers, and individuals with co-morbidities putting them at risk of severe clinical progression. Here, we evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not be included in the first round of vaccination. And, we account for current age-specific immune patterns in both states. We find that allocating a substantial proportion (> 75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. As we do not explicitly model other high mortality groups, this result on vaccine allocation applies to all groups at high risk of mortality if infected. Our analysis confirms that for an easily transmissible respiratory virus, allocating a large majority of vaccinations to groups with the highest mortality risk is optimal. Our analysis assumes that health systems during winter 2020-2021 have equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and will result in 1% to 2% reductions in cumulative hospitalizations and deaths by mid-2021. Assuming high vaccination coverage (> 28%) and no major relaxations in distancing, masking, gathering size, or hygiene guidelines between now and spring 2021, our model predicts that a combination of vaccination and population immunity will lead to low or near-zero transmission levels by the second quarter of 2021.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review:

The word “optimal” is inappropriate in this context. The methodology used by the authors does not allow them to claim that the strategy they propose is optimal. When the authors talk about optimality, what they in fact mean is “the best among the strategies evaluated” (as the authors correctly point out on Page 7). This method is called scenario analysis, in which several ad hoc scenarios are compared with each other, and the one that has e.g. the lowest number of deaths is the best scenario. The reasoning here is that the authors only consider a subset of all potential strategies and hence cannot claim that the one they propose is optimal. For instance, it possible that sharing vaccines between groups in another way than the ones considered in the manuscript (e.g. 75/25, 50/50, 25/75) is better, or that splitting up the age groups in different segments would be better. Hence, the word “optimal” is misleading and I recommend avoiding using this word, especially in the title of the manuscript, and even in the context of “sub-optimal,” because technically all proposed strategies in the manuscript could be sub-optimal and there is no way of currently verifying that.


Another concern I have is the authors’ claim that “[i]n our model, groups that are at high risk of death if infected are the older age groups, but our analysis implies that any high-risk group—whether the risk factor is age, obesity,  diabetes,  past lung disease,  lack of health care access,  or anything else—should have an equally high priority to vaccination.” It seems possible that the other factors that increase the risk of death if infected are correlated with a variable that the current risk factor they consider (i.e. age) is not correlated with.  As a result, this other risk factor may not require as high vaccination priority as the older high-risk population. For instance, individuals with past lung diseases may have hands-on experience of what needing a ventilator to breathe feels like and as such, these individuals may have a more risk-averse behavior which in turn implies that they are less exposed to SARS-CoV-2 than older individuals.  For this reason, I believe this claim is misleading, and fully addressing this question would require further analyses.

I believe this manuscript is important as it highlights the importance of having information on seroprevalence. However, I do not think the authors do a good job at presenting this result (it is merely presented as a secondary result), and there lacks an explanation on how their work differs from Bubar et al. [14]’s work.  Finally, I would like to see the authors detail more their model in the supplementary material. It is very helpful for some readers to be able to see the state equations.

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