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- Page navigation anchor for RE: Some vaccinated have natural immunity, too.RE: Some vaccinated have natural immunity, too.
In a couple of the comments, it has been observed that the model produces nonsensical results if a high baseline immunity parameter is used. On examination, it is clear that there is a serious flaw in the model.
If a high baseline immunity parameter is used, Fisman believes that this would be a "scenario where there is far more widespread immunity among the unvaccinated than among the vaccinated". However, that is not the scenario that is being considered. In a scenario with high baseline immunity, such immunity would not distinguish between unvaccinated and vaccinated populations and their baseline immunities would be the same. It is the model, not the scenario, that makes nonsense of this parameter and assumes that "we treat the unvaccinated as though they have already come through an epidemic, while treating the vaccinated as though their epidemic is yet to come." The compartmentalization should recognize the subgroup of people who chose to get vaccinated but had natural immunity already; however, the model places people with natural immunity only into the unvaccinated subgroup and none into the vaccinated subgroup. It is the way that the model compartmentalizes the initial population that is at odds with the real world.
This error results in a model that produces inaccurate results in all cases that use the baseline immunity parameter. In some cases, the inaccuracies are small enough that they do not impact the conclusions of the art...
Show MoreCompeting Interests: None declared.References
- Fisman DN, Amoako A, Tuite AR. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194(16):E573-E580.
- Page navigation anchor for RE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmissionRE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission
We appreciate the opportunity to respond to the concerns voiced by Doidge and Lungu with respect to our paper. We have responded to Doidge’s comments on our model structure and parameters in an earlier letter. We reject Doidge’s comment that our paper has “potential to…foster social division and misplaced anger and blame”. We think that the stoking of anger around use of remarkably effective vaccines during a public health emergency is being done, quite intentionally, by others. Indeed, as federal agencies have noted, the use of vaccination as a wedge to foment social division has been identified as a key goal of threat actors, likely including hostile states (1).
Our paper, by contrast, is using the utilitarian (i.e., “greatest good for greatest number”) lens commonly applied in public health to identify policies that protect the health and wellbeing of Canadians. Balancing the rights of individuals with the rights of the wider community is a key tension in public health practice, as Doidge and co-authors would likely know. Canadian public health statutes do contain provisions to limit the freedoms of individuals when this is necessary for protection of the wider community from virulent communicable diseases. Identification of sources of risk does not imply stigmatization or moral condemnation; we are not morally condemning or stigmatizing an impaired driver when we point out that they are a danger to others on the road. Identification of sources of risk...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Bell S. CSIS accuses Russia, China and Iran of spreading COVID-19 disinformation. Available via the Internet at https://globalnews.ca/news/7494689/csis-accuses-russia-china-iran-coronavirus-covid-19-disinformation/. Last accessed May 1, 2022. Global
- Page navigation anchor for RE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmissionRE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission
We appreciate the opportunity to respond to correspondence related to our paper. Several correspondents such as Schabas, Hsiang, and Aumond-Beaupre, suggest that we have used unduly favorable estimates of vaccine efficacy in the face of Omicron. In fact, the best and most recent Canadian data (from Carazo et al.) are remarkably consistent with our base case parameter values for vaccine efficacy in the face of Omicron infection (VE 70-80%). Furthermore, our findings don’t change in the face of wide-ranging sensitivity analyses on vaccine efficacy.
While it is true, as Doidge notes in his letter, that it would be possible to create scenarios where vaccinated individuals were less protected than unvaccinated individuals, this would necessitate the use of nonsense parameters without relation to real-world data, and would also (for example, in the scenario where there is far more widespread immunity among the unvaccinated than among the vaccinated) require that we treat the unvaccinated as though they have already come through an epidemic (while ignoring the costs and risks of their having done so), while treating the vaccinated as though their epidemic is yet to come. This too, we believe, would be nonsensical.
In other correspondence, Schanzer and Strobel both correctly point out that our model does not include waning immunity, which appears to be an important limitation of mRNA vaccine-derived immunity, as well as immunity conferred by prior infection. I...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Carazo S, Skowronski DM, Brisson M, Sauvageau C, Brousseau N, Gilca R, et al. Protection against Omicron re-infection conferred by prior heterologous SARS-CoV-2 infection, with and without mRNA vaccination. medRxiv. 2022:2022.04.29.22274455.
- Townsend JP, Hassler HB, Wang Z, Miura S, Singh J, Kumar S, et al. The durability of immunity against reinfection by SARS-CoV-2: a comparative evolutionary study. Lancet Microbe. 2021;2(12):e666-e75.
- Chu L, Vrbicky K, Montefiori D, Huang W, Nestorova B, Chang Y, et al. Immune response to SARS-CoV-2 after a booster of mRNA-1273: an open-label phase 2 trial. Nat Med. 2022.
- Page navigation anchor for RE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmissionRE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission
We are pleased to see that our paper stimulated so much thought and reflection. The criticisms on this page can be divided into three main categories: (i) our interpretation of our model results is incorrect; (ii) our choice of parameters is incorrect; and (iii) our model is stoking hatred against those who choose to remain unvaccinated against SARS-CoV-2. We respond to the first criticism below, and others in subsequent letters, due to a character limit on letter responses.
Some of the letters, despite their vehemence, contain errors which may suggest lack of familiarity with infectious disease modeling. Rancourt, for example, states that "the model as presented is blind as to whether the “contacts” in the normalizing denominator of Ψ are infectious or benign, irrespective of vaccination status". In fact, the disproportionate infectivity of unvaccinated contacts can be inferred from our results (as well as obtained directly from the model, which we have made publicly accessible in Microsoft Excel format). It is the higher average infection prevalence among the unvaccinated that is the driver of the disproportionate burden of infection derived from contact with unvaccinated individuals. Note that we treat contacts with vaccinated and unvaccinated infectives as equally infectious in our model, which has the effect of biasing our results against vaccination. Recent data from Puhach et al. demonstrate reduced infectivity among fully vaccinated individua...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Puhach O, Adea K, Hulo N, Sattonnet P, Genecand C, Iten A, et al. Infectious viral load in unvaccinated and vaccinated individuals infected with ancestral, Delta or Omicron SARS-CoV-2. Nat Med. 2022.
- Page navigation anchor for RE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmissionRE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission
I thank Dr. Lynch for her feedback. Dr. Lynch is correct that a substantial fraction of hospitalizations are expected to occur among the vaccinated when the overwhelming majority of the population is vaccinated, as is the case in Canada. Conflation of risk (far lower among the vaccinated) with absolute burden of hospitalizations is referred to as the "base rate fallacy". I would encourage Dr. Lynch to learn more about this concept by accessing the second suggested reference below; as I would assume she has read our paper, I'd encourage her to revisit Figure 1, where this concept is also illustrated.
Indeed, her point, that risk persists in vaccinated individuals despite their attempts to protect themselves, and that that risk derives disproportionately from interaction with unvaccinated individuals, is the basic finding of our paper.
Dr. Lynch suggests that my declared service on one AstraZeneca and one Pfizer vaccine advisory board since the start of the pandemic somehow invalidates our model findings. I would point out that all journals require authors to disclose any potential or perceived conflict, which we have done. As a recognized expert in, and enthusiastic supporter of, vaccination, I am invited to serve on such boards, as are many other Canadian vaccine experts. Attempting to discredit a paper by attacking an author is termed "argumentum ad hominem", and I provide information on that logical fallacy in the resources bel...
Show MoreCompeting Interests: None declared.References
- The base rate fallacy. In: Fallacy Files (web resource). Available via the Internet at http://www.fallacyfiles.org/baserate.html. Last accessed May 3, 2022.
- Argumentum Ad Hominem. In: Fallacy Files (web resource). Available via the Internet at https://www.fallacyfiles.org/adhomine.html. Last accessed May 3, 2022.
- Page navigation anchor for RE: using flawed inputs to vilify a minorityRE: using flawed inputs to vilify a minority
While David Fisman et al point out that, historically, "behaviours that create health risks for the community as well as individuals have been the subject of public health regulation,"(1) they appear to ignore the reality that, historically, minority groups have been scapegoated as carriers of new diseases and often harmed as a result.(2)
It is well established that particular demographic groups, in Canada and elsewhere, are disproportionately vaccine-hesitant. According to a study by the Ontario Covid-19 Science Table, "Vaccine uptake in Ontario has been lower among racialized communities, especially in areas with the highest proportions of refugees, recent immigrants, and recent OHIP registrants — communities that have also been the most impacted by SARS-CoV-2 infections." (3) There are good historical reasons for such hesitancy, which is not simply a product of demands for the "rights of the unvaccinated," as referenced in the article, or of "misinformation."
Certain demographic groups - including racialized and low-income Canadians, as well as recent immigrants - have disproportionately borne the burden of Covid-19.(4) As communities which tend to be more vaccine-hesitant, they have suffered further from coercive measures such as vaccine mandates and passports. These interventions have disproportionately benefited higher-income white individuals while disproportionately excluding lower-income, racialized Canadians...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- “I Know Who Caused COVID-19”: Pandemics and Xenophobia. Reaktion Books, September 2021.
- Barrett KA, Feldman J, Trent J, et al. COVID-19 vaccine confidence in Ontario and strategies to support capability, opportunity, and motivation among at risk populations.https://doi.org/10.47326/ocsat.2021.02.47.1.0
- Ibid.
- Page navigation anchor for RE: Flawed assumptions render this mathematical modeling study potentially seriously inaccurateRE: Flawed assumptions render this mathematical modeling study potentially seriously inaccurate
There are significant problems with this study.
Starting with the title: The title implies that there was actual mixing of populations in the study, this was not the case, this was a math modeling study not a clinical study.
There were inaccurate assumptions with this model rendering the conclusions inaccurate and not consistent with real world experience. The most glaring assumption is that the baseline immunity in unvaccinated people is only 0.2. There is evidence that even before the delta and omicron waves baseline immunity in a BC population was much higher than this (1). Given the current situation and that many have recovered from Covid infections and are now naturally immune, the immunity in the unvaccinated population is likely to be greater than 80% as a conservative estimate. Another assumption that is clearly inaccurate is that vaccine effectiveness is 80% (0.8 in table 1) this refers to relative risk rather than absolute risk and without adequate consideration of known and considerable waning immunity. Re-calculation with correction of these assumptions should be done.
Real world experience using the most recent Ontario statistics demonstrates that it is the vaccinated who are at most risk of having or being hospitalized with Covid (2).
There was also significant conflict of interest with the lead author having served on numerous advisory boards for the pharmaceutical industry, including those involved in the Covid response. Thi...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- https://insight.jci.org/articles/view/146316
- https://covid-19.ontario.ca/data?fbclid=IwAR22LnYRpR0xJdihWceFxojOQ98mxmO-KVDAijgE1x_6UO8dUfnYYvMQ2Rk#hospitalizationsByVaccinationStatus
- Page navigation anchor for Fisman et al.'s model uses out-dated inputs resulting in incorrect predictions as verified by real-world dataFisman et al.'s model uses out-dated inputs resulting in incorrect predictions as verified by real-world data
We are writing to address the article written by Fisman et al. regarding the mixing of vaccinated and unvaccinated populations. We feel that the study has significant limitations that merit discussion, as they substantially hamper the validity of the study’s conclusions to suggest policy directions, most of which are justified neither by the study’s methodology or results.
The first limitation is the choice of an endpoint of minimal clinical interest. Ultimately, COVID-19 infection is inevitable for almost all people, independent of vaccine status. Waning immunity means that most infections currently occur in the vaccinated, with equivalent infection rates in vaccinated and unvaccinated people.1 If the expected infection rate of the population is ultimately close to 100% vaccinated or not, the vaccine status of the source of infection becomes irrelevant.
Secondly, the model erroneously assumes a high level of permanent immunity following vaccination. While COVID-19 vaccines retain excellent efficacy for preventing severe disease, they quickly wane for preventing infection.2 This is true of the primary series as well as booster doses. The model assumes an vaccine efficacy of 40-80%, which may have been true with previous variants, and immediately following vaccination. However, in the Omicron era, vaccine efficacy is substantially lower and wanes quickly. It would better be modeled between 0 and 30% for infection.2 Similarly, the team assumes low immunity in t...
Show MoreCompeting Interests: None declared.References
- 1. BCCDC, COVID-19 health outcomes by vaccination status, http://www.bccdc.ca/health-professionals/data-reports/covid-19-surveillance-dashboard, accessed April 29 2022
- 2. Government of the UK, COVID-19 vaccine surveillance report: week 17, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1072064/Vaccine-surveillance-report-week-17.pdf
- 3. Clarke K, Jones J, Deng Y et al., Seroprevalence of Infection-Induced SARS-CoV-2 Antibodies — United States, September 2021–February 2022, MMWR Morb Mortal Wkly Rep 2022;71:606-608. DOI: http://dx.doi.org/10.15585/mmwr.mm7117e3external icon
- 4. León TM, Dorabawila V, Nelson L, et al. COVID-19 Cases and Hospitalizations by COVID-19 Vaccination Status and Previous COVID-19 Diagnosis — California and New York, May–November 2021. MMWR Morb Mortal Wkly Rep 2022;71:125–131.
- 5. Government of the Netherlands, National Institute for Public Health and the Environment, Research results from GGD data about children and COVID-19, https://www.rivm.nl/en/coronavirus-covid-19/children-and-covid-19/research-results-ggd-data
- Page navigation anchor for RE: Flawed AssumptionsRE: Flawed Assumptions
Given the serious flaws of this modelling study, it is unfortunate that it has received wide media attention. The media narratives being sold based on this study are troubling, because they do not reflect reality but are causing further societal division in Canada.
The authors claim a VE against infection of 80% (but admit a possible lower bound of 40% for Omicron). Definitely 80% and even 40% are overly optimistic VE assumptions for the current vaccines. For example, a recent NEJM article showed double dose mRNA VE dropping to 15.4% after 15 to 19 weeks and dropping further to 8.8% after 25 or more weeks (https://www.nejm.org/doi/full/10.1056/NEJMoa2119451). Many other papers have confirmed these findings.
The authors also claim a baseline immunity in the unvaccinated of 20% based on an 'assumption' (i.e. no reference given). This is a very false assumption as this point in the pandemic. In the Omicron era, a large percentage of the unvaccinated (or vaccinated) population has already been infected. For example, the US CDC recently announced (https://www.cdc.gov/mmwr/volumes/71/wr/mm7117e3.htm) that as of February 2022, approximately 75% of children and adolescents had serologic evidence of previous infection with SARS-CoV-2, with approximately one third becoming newly seropositive since December 2021. Given th...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Page navigation anchor for Fisman et al.'s main conclusion does not follow from their modelFisman et al.'s main conclusion does not follow from their model
Fisman et al. [1]’s main conclusion – that risk of infection among vaccinated people can be disproportionately attributed to unvaccinated people – does not follow from the model presented [2].
Their ad hoc parameter Ψ – defined as “the fraction of all infections among vaccinated people that derived from contact with unvaccinated people, divided by the fraction of all contacts [involving vaccinated people] that occurred with unvaccinated people” – is incorrectly asserted to represent “a normalized index of the degree to which risk in one group may be disproportionately driven by contact with another.”
The assertion is incorrect because the model as presented is blind as to whether the “contacts” in the normalizing denominator of Ψ are infectious or benign, irrespective of vaccination status.
In the model, most “contacts” are benign (not involving an infectious person and a susceptible person), whether vaccinated or unvaccinated. This means that the normalizing denominator of Ψ cannot be assumed to represent “contacts driving infection”, as advanced by Fisman et al.
It is easy to see that the ad hoc parameter Ψ is nonsensical, from figures in their paper:
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a. Fig. 2A shows Ψ dropping dramatically with increasing reproduction number. This would mean that unvaccinated people threaten vaccinated people proportionately less when the presumed pathogen is more infectious. The state should not worry about unvaccinated people if the pandemic is su...Competing Interests: None declared.References
- [1] David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- [2] Denis G. Rancourt, Joseph Hickey. OCLA Statement on CMAJ Fisman et al. Article Claiming Disproportionate Infection Risk from Unvaccinated Population, and on Negligent Media Reporting. Ontario Civil Liberties Association, 27 April 2022: https://ocla.ca
- Page navigation anchor for RE: Fundamental Error in Key Input Invalidates ModelRE: Fundamental Error in Key Input Invalidates Model
I offer the following comments regarding the recent model published in the Canadian Medical Association Journal.1 There are several important shortcomings and concerns with this paper.
First, this is a model. It does not measure, observe or test anything. It is a prediction based on a self-described "simple" model. The predictions of this model have not been tested so, at most, this model should be regarded as an hypothesis. Like any hypothesis it needs to be tested and validated before its predictions should be considered evidence.
Second, the output of any model is totally dependent on the quality and accuracy of its inputs. This key input for this model is the vaccine effectiveness (VE) in preventing infection. The model assumes that this VE is 40-80%.
The authors cite two references to support the lower bound (40%) estimate. The first is a surveillance report from the United Kingdom at a time (December 2021) when Omicron Variant was just emerging.2 The data in this report are based on Delta Variant but the report makes it clear that lower VE with Omicron is anticipated. The second reference is simply another unvalidated model.3
The authors cite only a single reference to support an upper bound (80%) estimate of VE.4
The authors' use of this single reference is highly problematic for three reasons. First, the reference only covers data up until October 20, 2021 - six months ago! It does not take into accou...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Page navigation anchor for RE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmissionRE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission
Fisman and colleagues[1] present a oversimplification of a complex epidemiological, social, and bioethical issue. The findings are predetermined by the authors’ own model design choices; something that should never occur in science. That the authors make strong ethical and political claims that feed existing social polarization makes the flawed design even more problematic.
The authors use a compartmental SIR model to compute the infection burden in vaccinated and unvaccinated population subgroups and assess contribution of the unvaccinated group to the cumulative rate of infection among the vaccinated. The study’s main conclusion—that mixing with unvaccinated people increases the risk of infection among the vaccinated—is predetermined by the authors choice of model and parameters. By ignoring waning immunity (from both vaccination and prior infection), the authors have constructed a model in which herd immunity always occurs, leaving some residual proportion of the population uninfected indefinitely. In this hypothetical scenario, it is a foregone conclusion that if one group with high baseline immunity is mixed with another group of lower baseline immunity then a greater proportion of the high-immunity group will become infected before herd immunity is achieved, than if they had not mixed. This is nothing more than dilution. The model[2] contains two crucial parameters: ‘vaccine efficacy’ and ‘baseline immunity in unvaccinated’. If these are set to any combination...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- doi.org/10.6084/m9.figshare.15189576
- ndrews N, Tessier E, Stowe J, Gower C, Kirsebom F, Simmons R, et al. Duration of Protection against Mild and Severe Disease by Covid-19 Vaccines. N Engl J Med. 2022;386(4):340-50.
- Page navigation anchor for RE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmissionRE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission
Dear Sir,
The article by Fisman et al.1 has garnered much medical and societal attention. The authors used a mathematical model to simulate COVID-19 infection risk across various patterns of interactions amongst vaccinated and vaccine-free individuals. The authors conclude that individuals who avoid vaccination contribute to negative health consequences for others. Such an assertion is incorrect and biased for the following reasons:
1. Uses problematic mathematical modeling as a surrogate for real-world data. Mathematical modeling has been used throughout the COVID-19 response to justify lockdown measures while promoting unscientific public health edicts. As there is abundant real world data, why they would choose a mathematical model is unclear.
2. Overestimates vaccine effectiveness against symptomatic infection. The range of vaccine effectiveness against symptomatic infection was 40-80%. The upper bound limit of 80% may apply to the old Delta variant but the lower bound limit of 40% does not apply to the Omicron variant. Current data shows that vaccine effectiveness against Omicron symptomatic infection ranges from 0% to 75% independent of vaccine type, duration since primary series, or duration since booster(s).2
3. Overestimates the risk of transmission (secondary attack rate). The authors overstate the ability of vaccines to reduce the risk of transmission. Kampf has shown that the proportional rate of symptomatic Covid-19 cases among fu...
Show MoreCompeting Interests: None declared.References
- 1. Fisman DN, Amoako A, Tuite AR. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- 2. United Kingdom COVID-19 Vaccine Surveillance Report Week 16 (April 21st, 2022).
- 3. Kampf G. The epidemiological relevance of the Covid-19 vaccinated population is increasing. Lancet Regional Health https://doi.org10.1016/j.lanepe.2021.100272.
- 4. Majdoubi A, Michalski C, O’Connell SE et al. A majority of uninfected adults show pre-existing antibody reactivity against SARS-CoV-2. JCI Insight. https://doi.org/10.1172/jci.insight.146316.
- 5. Goldberg Y, Mandel M, Bar-On YM et al. Waning Immunity after the BNT162b2 vaccine in Israel. NEJM DOI: 10.1056/NEJMoa2114228.
- Page navigation anchor for RE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmissionRE: Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission
Noting that once the Omicron variant started to dominate over Delta in late December 2021, infection rates started to increase faster among the vaccinated than the unvaccinated and the rate ratio (RR) of unvaccinated/vaccinated quickly dropped below 1.0, I’d suggest that the main finding of this study “We found that the risk of infection was markedly higher among unvaccinated people than among vaccinated people under all mixing assumptions” should be reviewed (See Ontario Public Health data available at https://covid-19.ontario.ca/data/case-numbers-and-spread for rates by vaccination status and https://data.ontario.ca/en/dataset/covid-19-vaccine-data-in-ontario to download data).
At this time vaccine passports granted the vaccinated access to high-risk venues such as restaurants bars and gyms and the vaccine effectiveness (VE) of 2 doses was much lower for Omicron than Delta. My interpretation of the diverging trends in rates at the time was that the privileges provided by vaccine passports increased the rate of high-risk contacts among the vaccinated more than the protection against infection offered by the vaccine – that is, the vaccinated rather than the unvaccinated had become the main drivers of Omicron wave. The policy decision to shut down the high-risk venues after Christmas seemed to have worked in reduc...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Page navigation anchor for All models are wrong. Some are useful.All models are wrong. Some are useful.
Some are not though. A good model approximates the real world, and its assumptions reflect reality. The recent modeling by Fisman et al. published in the CMAJ is an example of a model presented in such a way that does not well represent the real world. Moreover, the interpretation of the model does not necessarily lead to the policy conclusions the authors present.
Of note, the authors make a major unrealistic assumption which is non-waning immunity among the cohort of individuals who are vaccinated. This means that these persons cannot transmit COVID19 to each other or to unvaccinated individuals. Where immunity wanes as it seems it does in the real world [2], or where there is not sterilizing immunity, the disproportionate impact that is reflected in the authors' models does not necessarily hold. This very seriously calls the authors' conclusion that the choices of the unvaccinated affect everyone “in a manner that is disproportionate to the portion of unvaccinated people in the population.” While not a perfect reflection of waning immunity, one suggestive result from the authors that reflects this is the sensitivity analysis that changes the effectiveness of the vaccine (fig 2 panel 4). Where the vaccine effectiveness drops to 40% the outcome is a much lower contribution of the unvaccinated to infection risk than their headline results suggest.
This issue aside, the authors then proceed to outline policy recommendations that do not necessarily fo...
Show MoreCompeting Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Hall V, Foulkes S, Insalata F, Kirwan P, Saei A, Atti A, Wellington E, Khawam J, Munro K, Cole M, Tranquillini C, Taylor-Kerr A, Hettiarachchi N, Calbraith D, Sajedi N, Milligan I, Themistocleous Y, Corrigan D, Cromey L, Price L, Stewart S, de Lacy E, Nor
- Hofmann A, Nell M. Smoking bans and the secondhand smoking problem: an economic analysis. Eur J Health Econ. 2012 Jun;13(3):227-36. doi: 10.1007/s10198-011-0341-z. Epub 2011 Aug 13. PMID: 21842184.
- Page navigation anchor for RE: Unvaccinated Subpopulation AssumptionRE: Unvaccinated Subpopulation Assumption
It seems your making the assumption that the unvaccinated are also previously uninfected, in your infection and transmission numbers calculation. At this point in the pandemic it would be almost impossible to locate any sizable population of individuals who were both unvaccinated and previously uninfected (most now with multiple infections). It would seem that your calculations should be adjusted for the natural immunity that multiple infection events confer, to more accurately reflect a “real world” experience?
Competing Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Page navigation anchor for Your title and the presentation of the findings in mass-media is misleadingYour title and the presentation of the findings in mass-media is misleading
You should have included the word 'model' in your title and the authors should have been very clear that the findings represent the result of mathematical modelling, not a real-life study of people mixing with other people. Any model is a simplified representation of the word and - more importantly - it is greatly affectedby its assumptions and its capacity to account for a host of other variables. The sensationalism in science is something that it will ruin us all in the long run. It is really sad to see that you contribute to it by not making very clear, from the title, that this is mathematical modelling, so the vulgarization of its results is misinterpreted as being based on data collected from real people.
Competing Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Page navigation anchor for RE: unvaccinated spreadRE: unvaccinated spread
We must be careful not to overstate vaccine efficacy. When we do it fuels the fire of anti-vaccine sentiment and conspiracy theories.
The evidence that vaccine protects against asymptomatic or mild infection is very poor. Strangely the highest case per 100 000 group is the group with booster shots (total cases, not severe cases which show good protection from vaccination).(2) While this is likely a sampling issue or other confounding factors we certainly can't make the argument the vaccine is anywhere close to the 40% 60% or 80% efficacy used the models in this study.
These comments are my own and do not represent the opinions of my employment hospitals.
Competing Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- 2. https://covid-19.ontario.ca/data/case-numbers-and-spread#casesByVaccinationStatus
- Page navigation anchor for RE: covid transmission mathematical model.RE: covid transmission mathematical model.
I would comment on a potential flaw with this mathematical model.
It does imply unvaccinated individuals in groups would result in more spread.
But unvaccinated individuals are more likely to be significantly symptomatic if infected. So then they would be more likely to stay home, isolate , and then not spread.
Conversely a vaccinated indivudual may be more likely minimally symptomatic or asymptomatic if infected. They would thus mingle more in groups and potentially spread more infection.
Signed Dr Steve Blitzer MD da math nerd.Competing Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- Page navigation anchor for RE: Erroneous parameters in studyRE: Erroneous parameters in study
In their conclusions they state : "We found that the risk of infection was markedly higher among unvaccinated people than among vaccinated people under all mixing assumptions."
This is clearly not the case with Omicron according to public data from UK, Scotland, Sweden, Denmark, Ontario, Quebec, Iceland, etc.
The vaccine efficacy against infection they used is 80%.
This is not realistic, even against Delta, the vaccine efficacy eventually becomes negative. This whole population study from Sweden show the vaccine efficacy became negative after ~240 days :
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3949410This was also shown in the UKSHA reports before Omicron, you can see it in any of their Covid Vaccine Surveillance Report before the arrival of Omicron.
https://assets.publishing.service.gov.uk/government/uploads/system/uploa... (Table 2)And now with Omicron, it's even worse. The Week 13 report raw data showed a vaccine efficacy against infection of around MINUS 300% for the triple vaccinated above 18 years old. Now they simply stopped publishing this inconvenient data.
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...Competing Interests: None declared.References
- David N. Fisman, Afia Amoako, Ashleigh R. Tuite. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission. CMAJ 2022;194:E573-E580.
- UKSHA, COVID-19 vaccine surveillance report Week 13
- Hansen & al. Vaccine effectiveness against SARS-CoV-2 infection with the Omicron or Delta variants following a two-dose or booster BNT162b2 or mRNA-1273 vaccination series: A Danish cohort study
- Buchan & al. Effectiveness of COVID-19 vaccines against Omicron or Delta infection
- Nordström & al. Effectiveness of Covid-19 Vaccination Against Risk of Symptomatic Infection, Hospitalization, and Death Up to 9 Months: A Swedish Total-Population Cohort Study.