Incorporating Mass Vaccination into Compartment Models for Infectious Diseases
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Abstract
The standard way of incorporating mass vaccination into a compartment model for an infectious disease is as a spontaneous transition process that applies to the entire susceptible class. The large degree of COVID-19 vaccine refusal, hesitancy, and ineligibility, and initial limitations of supply and distribution require reconsideration of this standard treatment. In this paper, we address these issues for models on endemic and epidemic time scales. On an endemic time scale, we partition the susceptible class into prevaccinated and unprotected subclasses and show that vaccine refusal/hesitancy/ineligibility has a significant impact on endemic behavior, particularly for diseases where immunity is short-lived. On an epidemic time scale, we develop a supply-limited Holling type 3 vaccination model and show that it is an excellent fit to vaccination data. We also extend the Holling model to a COVID-19 scenario in which the population is divided into two risk classes, with the highrisk class being prioritized for vaccination. For both cases with and without stratification by risk, we see significant differences in epidemiological outcomes between the Holling vaccination model and naive models. Finally, we use the new model to explore implications for public health policies in future pandemics.
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SciScore for 10.1101/2022.04.26.22274335: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. 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:One area where previous models fall short is the incorporation of vaccination subject to vaccine refusal and limitations on supply and distribution. The models developed in this paper will likely be suitable for novel disease pandemics of the future. They …
SciScore for 10.1101/2022.04.26.22274335: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. 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:One area where previous models fall short is the incorporation of vaccination subject to vaccine refusal and limitations on supply and distribution. The models developed in this paper will likely be suitable for novel disease pandemics of the future. They are more complicated than the standard single-phase transition model, so it is important to be clear about the benefits. 5.1. Vaccine Refusal: As of April 2022, the fractions of national populations that have received a full initial vaccine protocol (not counting boosters) is as high as 96% in the United Arab Emirates, but with a worldwide average of only 59% [19]. The actual percentages of people who have been fully protected at any given time are surely smaller, as the initial protocol without boosters was no longer adequate in April 2022. In some cases, the main difficulty is global inequity, but in other cases, such as the United States (just 66% of people having had the full initial protocol), the problem is significant levels of resistance to vaccination. This resistance will likely be present for novel diseases of the future, so it is important to account for it in models. This requires models that partition the standard susceptible class into prevaccinated and unprotected classes. Of course the extra state variable adds additional complexity to a model, which should be avoided when the extra detail is not necessary. For an endemic disease, the benefits of vaccination are significantly reduced by widespread vaccine re...
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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