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A Model for COVID Vaccines- What did it Show?

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Another model for COVID vaccines- what did this one show?

A recent study published in JAMA assessed how effective the COVID-19 vaccines were in the US during the vaccine rollout across a portion of the pandemic (October1’2020- June 30’2021)

Researchers at Yale University’s Center for Infectious Disease Modeling built a predictive model that included transmission dynamics of the original strain and the Alpha, Gamma and Delta variants along with parameters for age-specific risk factors. Data on administration of the 2-dose vaccines was taken from the CDC’s COVID data tracker and incorporated in the model. Vaccine efficacy was derived from published literature on its ability to reduce infection, symptomatic disease and severe disease. (You can learn more on how they used data to derive parameters for their predictive model in the “supplements” section of the study.) For comparison, the model included 2 counterfactuals: a scenario with no vaccine and a scenario in which only half of the daily vaccination rate was achieved.

 

What did the model for COVID vaccines conclude?
According to the model, compared to a scenario in which there is not a vaccine, the vaccine campaign nsaved 240797 lives and prevented 1133617 hospitalziations between 12 Dec’2020 and 30 June’2021. The model predicted that the number of cases averted because of the vaccines  was above 14 million.
Compared to the scenario in which the daily vaccination rate was 50% of its reported rate, the model predicted that the US would have experienced 77283 more deaths and 336000 hospitalizations. The model also predicted that the vaccination campaign prevented what would have been a “wave of infections” driven by the Alpha variant in April of 2021.

Some Limitations, Though:  

If the model for COVID vaccine’s set assumptions, parameters and conditions perfectly mirrored reality, its spit-out statistical snapshots would be highly optimistic. But that just isn’t the case, as the most obvious limitation of the study is that the model does not consider waning immunity after vaccination, which published studies have clearly shown to be an issue. The model was fitted for a specific period (Oct’20- June’21), so it also does not account for the Omicron variant, which is currently causing cases to explode across the US, whether one is vaccinated or not. Waning vaccine-induced immunity and new variants is why US public health authorities are pushing boosters for eligible populations and why we aren’t “back to normal” or “doing the things we love” as was promised at the start of the vaccine rollout. That said, the bulk of published studies consistently show that vaccinated folks are less likely to be hospitalized and die from COVID-19 than unvaccinated folks, a protective effect more pronounced in the highest risk groups. How long that effect remains significant and what a COVID “booster” schedule will ultimately look like and whether different age groups need a nuanced approach are still things scientists are trying to figure out.

 

Something to Consider for a Future Model for COVID Vaccines: 

While we’ve now seen a few models from talented scientists that attempt to shed light on how protective the COVID-19 vaccine really is, future models that account for the known waning vaccine-induced immunity and the probability of new variants that can escape vaccines would provide a more accurate picture. A model that doesn’t just rely on reported cases and accounts for an underreporting of cases and a model that considers recovery from infection would also be ideal. Of course, that might be too much to ask of a model.

 

Thanks for reading a “Model for COVID vaccines” — also consider:

Fluvoxamine: A cheap drug that might get us out of the pandemic for good.

A COVID Calculator? You can predict your risk of hospitalization & death here

A Napkin Risks-Benefits Analysis- What Everyone Should be Doing

Natural Immunity? Shhh…Don’t Say Those Words

 

Don’t forget to subscribe to the Causes or Cures Podcast too!

Thanks all- Dr. Eeks ( Erin) 🙂

 

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