How can you tell if a vaccine for a bug is effective? It’s not so easy; indeed, it can be excruciatingly difficult.
At the individual person level you’d need to measure all kinds of things, like the level of antibodies and other immune cells present before vaccination, and then again after and through time.
Then you’d demonstrate, in that person, the exact mechanism by which the vaccine was able to boost immunity, and whether this boost was sufficient to quell the infection, by looking at severity of illness (due to the bug and other existing conditions), how long it took for the infection to abate, and things like that. And that is only a hint of the complexities.
The analysis is made harder because the vaccinated person may never come into contact with the virus. People he meets may have already had prior infections, and so are now mostly or completely immune. Or those people have had a vaccine that was effective to varying degrees.
As difficult as all that sounds, it is not impossible in highly controlled circumstances to discover the extent and to quantify vaccine effectiveness. But it is a slow and painstaking process.
One way you cannot learn, not with anything approaching certainty, is looking at group-level comparisons, where people are not individually counted and compared, but where averages across groups are contrasted, and where you have no idea what the status of any individual is.
This is a popular kind of analysis because it’s cheap and easy. But it can, and often does, lead to huge over-certainties.
A prime example is from the paper “Global impact of the first year of COVID-19 vaccination: a mathematical modelling study” by Oliver J Watson, Gregory Barnsley, Jaspreet Toor, Alexandra B Hogan, Peter Winskill, and Azra C Ghani, in Lancet Infectious Disease.
They used a “mathematical model of COVID-19 transmission and vaccination” for both “reported COVID-19 mortality and all-cause excess mortality in 185 countries and territories” to assess vaccine efficacy in preventing deaths. This is as group-level an analysis as they come, especially with its “excess” deaths portion.
What are “excess” deaths? Deaths different than those predicted by a model. They can be positive, meaning more deaths than predicted by a model, or negative, meaning fewer deaths than predicted by a model. What it means here is that Watson and his co-authors used as input to their model, output from another model, and what that means I’ll explain in a moment.
Let’s first look at their transmission model.
In it, “Vaccination was assumed to confer protection against SARS-CoV-2 infection and the development of severe disease requiring hospital admission, and to reduce transmission from vaccine breakthrough infections”. Incidentally, “Breakthrough infection” is a term of incredulity: it assumes vaccines work and that, somehow, bugs are able to bypass it sometimes.
In other words, their model was told that covid vaccination worked. The model was told that the vax blocked infection and prevented severe disease, including death, and the model was told that infections were harder to pass on in the vaccinated.
The only thing this model can “discover”, therefore, is that the covid vaccine works. It could do nothing else.