The Failures of an Ideal COVID-19 Vaccine: A Simulation Study
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Abstract
This paper simulates an ideal COVID-19 vaccine that confers immediate sterilizing immunity against all SARS-CoV-2 variants. The purpose was to explore how well this ideal vaccine could protect a population against common conditions (such as vaccine hesitancy) that might impair vaccine effectiveness. Simulations were done with an SEIRS spreadsheet model that ran two parallel subpopulations: one that accepted vaccination, and another that refused it. The two subpopulations could transmit infections to one another. Success was judged by the rate of new cases in the period from 1-5 years after the introduction of the vaccine.
Under good conditions, including a small subpopulation that refused vaccination, rapid distribution of the vaccine, duration of vaccinal immunity longer than 12 months, good retention of interest in getting vaccinated after the first year, strict maintenance of nonpharmaceutical interventions (NPIs) such as masking, and new variants with R 0 s less than 4.0, the vaccine was able to end the epidemic. With violation of these conditions, the post-vaccine era futures ranged from endemic COVID at a low or medium level to rates of COVID cases worse than anything seen in the US up to late 2021. The most important conditions for keeping case rates low were a fast speed of vaccine distribution, a low percentage of the population that refuses vaccination, a long duration of vaccinal immunity, and continuing maintenance of NPIs after vaccination began. On the other hand, a short duration of vaccinal immunity, abandonment of NPIs, and new variants with a high R 0 were powerful barriers to disease control. New variants with high R 0 s were particularly damaging, producing high case rates except when vaccination speed was unrealistically rapid. A recurring finding was that most disease afflicting the vaccinated population in these simulations originated in the unvaccinated population, and cutting off interaction with the unvaccinated population caused a sharp drop in the case rate of the vaccinated population.
In conclusion, multiple common conditions can compromise the effectiveness of even an “ideal” vaccine.
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SciScore for 10.1101/2021.11.22.21266669: (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 The Simulation of an Unmitigated Epidemic: The simulations used an SEIRS model done using difference equations on a Microsoft Excel spreadsheet. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)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:If the vaccine had realistic limitations, such as failure against a new variant, both the “optimistic” and “pessimistic” …
SciScore for 10.1101/2021.11.22.21266669: (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 The Simulation of an Unmitigated Epidemic: The simulations used an SEIRS model done using difference equations on a Microsoft Excel spreadsheet. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)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:If the vaccine had realistic limitations, such as failure against a new variant, both the “optimistic” and “pessimistic” results would produce even higher case rates. So it might be said that the tables in this report are not a prediction of what will happen in reality; these case rates are the best that can be expected. On the other hand, there is reason to accept these results with caution. First, we must consider a general weakness of simulations. Assuming that they include the proper relationships and parameters, simulations estimate what would happen if a set of conditions is continued for the length of the simulation. We certainly hope that if the pessimistic scenario were in progress and case rates were escalating drastically, the parameters of the system would react and change--NPIs would be restored, vaccination speeds would go up, and some Population 2 individuals would shift to Population 1. These responses would moderate the results. A related objection is the assumption that the members of the whole population are interacting randomly with one another. The complex history of COVID-19 in the US has many examples where cases would flare up in a region, but then die out without spreading nationwide. The popular press has mentioned a “two-month COVID cycle,” in which cases tend to increase for two months and then recede, for unknown reasons (Leonhardt and Wu, 2021). This phenomenon may be caused by localized epidemics that increase until enough susceptibles have been...
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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