Welcome to Impact Factor, your weekly dose of commentary on a new medical study.
I’m Dr. F. Perry Wilson of the Yale School of Medicine.
Possibly the most maddening statistic to find during this pandemic has been one of the simplest
– the number of COVID infections.
This number, whatever it is, is critical for so many reasons.
It forms the denominator for the infection fatality rate – how serious is this virus,
compared to all the others out there.
It also tells us what proportion of the population might already have substantial immunity, which
might change vaccination thresholds targeting herd immunity.
It would also help us plan for the future – insofar as some individuals who were infected
will likely have substantial health needs long after the pandemic subsides.
But the total number of covid infections has been tricky to pin down, thanks largely to
its stubborn ability to infect people and cause minimal – or even no symptoms.
Combine that with a lack of easy access to testing early on, and an ongoing reticence
towards testing since we force quarantine on people who test positive without guaranteeing
their wages – and you have a recipe for an undercount.
There have been multiple attempts to estimate the true cumulative incidence of SARS-CoV-2
infections in the US, most notably by the CDC which, as of April 2021, suggested that
there have been 114 million infections in the US (of which about a quarter were detected
through testing).
But there are limitations to the CDC approach, including some pretty rough assumptions about
the likelihood of infected people seeking care.
A new approach, reported in the Proceedings of the National Academy of Sciences by Adrian
Raftery and Nicholas Irons leverages several data sources – reported case rates, death
rates, and testing rates – all varying over time and place - to get a better estimate
of the number of infections.
Before we get too deep into this, I think it’s helpful to imagine the easy way to
figure out how many people have been infected.
The easy way – and the way many of us think we should have been doing all along – is
systematic, random sampling of the population for antibodies to coronavirus.
We do this for other things all the time –it’s how we know what the average cholesterol level
of an American is for example.
But so far only two states, Indiana and Ohio, have done a systematic job of randomly sampling
their populace to figure out how many people have been infected.
Raftery and Irons anchor their statistical model to these states and then use that high-quality
data to extrapolate to the rest of the US.
The results suggest that from March of 2020 to March of 2021, 65 million people – roughly
20% of the US population had been infected with the coronavirus.
I want to note that this is substantially lower than the CDC estimates of infections
in that time period.
That has multiple important implications.
The CDC estimates suggest the infection fatality rate of the virus is 0.5%- keep in mind this
is different than the case fatality rate – which is the death rate among people who actually
test positive.
The 0.5% figure dilutes the denominator by including those who never knew they were infected.
But the Raftery data suggests the infection fatality rate is a fair amount higher – around
0.8 or 0.9% - they break it down by state here – with a low of around 0.2% in Utah
and a high of 1.7% in Massachusetts.
So, how do we know this model is any more accurate than the others?
Fortunately, the authors published their projections of coronavirus infections and deaths based
on their data – which was truncated in March 2021.
Comparing their projections to reality looks pretty good, though they don’t capture the
new spike in infections from the delta variant.
This may be due to some of the assumptions of their model, which includes the idea that
individuals who were previously infected can not become infected again.
They also incorporated vaccination data into their model of overall infection to plot what
percent of Americans would be immune to the virus.
They project that, by August, roughly 70% of the population will have immunity, largely
through the vaccination effort.
Herd immunity for the delta variant is probably around 75 or 80%, so this looks pretty good.
But here too the model assumptions – which include the idea that 2 million individuals
will be vaccinated every day – might be overly optimistic.
We’re actually giving just over 500,000 shots a day right now.
Altogether, this model has a bit of a sad story to tell – the virus is slightly deadlier
than we may have thought, and herd immunity slightly farther away.
If accurate, it suggests we may be in for a difficult fall.