(bright music)
- So hello, everyone.
Today I'm here with Duke biology professors,
Steve Haase and Greg Wray,
for a bit of behind-the-scenes discussion
of some of the work
that has helped guide our approach
to COVID management on campus.
So Steve and Greg,
you either volunteered or were drafted to the cause,
but either way, you stepped up
to lead the modeling and sequencing work
that has been a crucial part of our strategy
to control COVID on campus for more than a year now.
So today I'd like to focus in particular
on how your work has helped guide our decision-making
related to this semester,
as well as what we've learned this semester
from viral sequencing.
So let's start a little bit with the testing,
and I'll start with you, Steve.
So Duke decided much earlier than other schools
that we would continue testing all students this fall.
So what made you recommend
continued testing for all students,
even those who are fully vaccinated?
- Yeah, it's a great question.
So early on, in June,
the CDC came out
and set some guidelines for universities
and included in those guidelines
were that fully vaccinated populations of students
did not need to do (indistinct) testing
or social distancing or masking,
you know, none of the mitigations
that we had used so effectively in the year before.
And so we actually modeled that
by fitting our model parameters to data from last year
and at the levels of vaccine efficiency
for preventing transmission,
which at the time was about 90%,
we saw that without surveillance testing
you did just about as good as surveillance testing.
And I think this was the foundation
for these kinds of decisions.
What bothered us at the time
were reports coming out of a number of countries
that the vaccine efficiency against some of the variants,
at that time, Alpha and Beta,
was not at the 90% but at a diminished rate.
And as well, we were starting
to see Delta really taking hold in India and other places
and there were also reports
that that variant might have the capability
to be resistant to vaccine-induced immunity.
And so we did another modeling exercise and asked,
"What happens if we reduce,
you know, go through the same exercise
but reduce the vaccine efficiency to 75% or 50%?"
And what we saw in those exercises
is that there were substantial numbers of infections
that occurred in the vaccinated population,
especially if we weren't testing.
And so it was this caution that we had
about the potential loss of vaccine efficiency
against the variants
that led us to suggest
that we were gonna keep testing in the fall.
- Yeah, and if I remember from looking at your data,
there was also an impact of the number of contacts
the average student would have this year
compared to last year,
given things being much more open
and the capability of transmission going up.
- Yeah, 100%.
We modeled that as well
and turned the dials in the model
because we expected if we took masks off
there would be many more opportunities for transmission.
And as well, the density on campus is much higher this year
'cause we have more students back.
So all of these things kinda led us
to think that we should probably start testing
at the beginning
and that we could scale that back if we didn't need it.
- So as you're sort of looking at these data in real time,
how do you adjust who's gonna get tested and when?
You know, there's different sectors,
there's different parts of the, areas of the campus,
and also different populations of students.
- That's right.
And so I think testing in,
our general strategy is to apply the most testing
to groups that are in congregate living settings,
like our undergraduates in dormitories.
And so we set baseline levels for testing.
You know, once a week.
Right now, we're twice a week
for our undergraduates on campus.
And then we have the ability,
and we're looking at the data on a day-to-day basis,
actually, we look it as it comes out hourly,
and if there's a particular group,
let's say we had three or four cases in a particular dorm,
we might increase the number of tests we push at that dorm
just to make sure that we're catching everything
if there is a surge in that place.
So we look geospatially at where students are living,
social living groups that we might see,
you know, classrooms,
all kinds of things
that we're looking for signals of transmission
that we might push more tests to
to make sure we're catching everything.
- Yeah, no, that makes sense.
So I think one things that's,
it's not maybe absolutely unique
but it's certainly within a very small group of peers,
we're sequencing every viral sample
from individuals who test positive for COVID
in our surveillance system.
So maybe Greg, can you tell us a little bit about that
and, you know, what you've learned from the sequencing?
- Sure, yeah.
So, you know, you might ask,
"Why would you sequence every single sample?
Aren't you seeing the same thing over and over again?"
And when we started this, that was an open question.
But what we've learned
is that almost every time the virus transmits
and goes to a person and reproduces
and then goes onto the next person
that there's a genetic change.
And we can use those genetic changes
to learn about the genealogy of the strains of viruses
that are circulating on campus
and even to understand something
about the patterns of transmission.
That has been really useful
for thinking about how we sort of understand
the way in which the virus moves around
and moves from person to person.
- No, that's interesting.
And presumably, also, it would be,
we'd get an early warning system
if there were really a concerning variant
that were to emerge.
- Certainly, yeah.
I mean, it was quite remarkable
to watch the variants sort of come in in successive waves
over the weeks of this year.
So when we were in the fall of last year,
most of what was circulating
was very similar to what you might call the original strain.
And then over the winter,
we began to see some of the California strain
and the Alpha strain, Beta strain, come on.
And then about July of this year,
we saw the first Delta
and within a couple weeks it became almost 100%.
So there's just been almost a displacement
of wave after wave of strain coming in.
But you're right.
If a new concerning variant were to appear somewhere,
we would almost immediately know when it shows up on campus.
- No, that's great.
So, you know, one thing
I think that our faculty and students
have all been concerned about
is really how we know where infections are occurring.
You know, we have said over and over again,
and our peers have said,
that we have not seen classroom transmission,
particularly in our setting
where, first of all,
we have such a high level of vaccination now
but, also, even going back last year before vaccination
because everyone in the classroom is masked.
But maybe you, both Steve and Greg,
maybe you can tell us a little bit more
how contact tracing and sequencing
are actually contributing
to identifying the sources of infection
and how this sort of bolsters our conclusion
that we're not seeing classroom transmission
and how it tells us a little bit
about where our major transmissions are actually occurring.
(Greg chuckles)
- Sure, I think maybe I could speak to the data part
and Steve could speak to the patterns of transmission part
that comes with the contact tracing.
I think one of the things that's helpful to understand here
is that the virus
in almost every individual we see on campus
has a different sequence.
It's almost like it's got its own fingerprint.
And indeed, most of the time
when we get a batch of samples to sequence,
every single individual has a unique viral sequence
that they're carrying
and those sequences
are not even right next to each other, if you will.
They don't differ by one or two positions.
They might differ by 10 or 20 or even 30 positions.
- Wow.
- So that means that, in fact, it actually stands out
when we see two samples
that have a very, very similar
or potentially identical sequence.
Those are the cases that are probably direct transmission,
either between those two individuals
or perhaps a third unknown individual
that directly transmitted to them.
But those are the exceptional cases.
And every now and then,
so most of the time,
it seems like there's not campus transmission happening.
Every now and then,
we see a cluster of really similar sequences.
And Steve, why don't you pick up on that
and explain what happens when we send those back to you?
- Right.
So sequencing data's been very helpful
in putting it together with contact tracing data
where contact tracers are using interviews
and other kinds of information about those that are infected
to ask, "Hey, what's the most likely mode of transmission?"
And so what we've learned in these cases
where we see individuals
with highly similar sequences
and sometimes identical
is the contact tracing data actually fits with that
and they all, in at least one case,
have reported being at a gathering
at an off-campus establishment.
So I think,
so the contact tracing data that's been collected
since the start of the pandemic
is very highly complementary
to the sequence data that Greg is discussing
and both are telling the same story,
and that is that most of the infection we see on campus
probably comes from students or other individuals
being infected while off campus
and not really student-to-student infections on campus.
- Yeah, and I think a lot of this has guided our policy,
for instance, around dining.
I think if we look,
or food at meetings, et cetera,
I think when we look through contact tracing
we're actually getting the information
that it's mostly in unmasked situations,
almost entirely with eating and drinking,
and sort of certainly the mass transmission events
that we can see through sequencing
bolster that conclusion.
But even if we look at, you know, smaller clusters'
presumably sequence relatedness,
you can say these are social events, by and large.
So, you know, this is great.
I think folks can see this
almost like as a movie trailer teaser
because Steve and some other colleagues
did a talk at Academic Council this week
delving into a lot of the data,
some of the sequencing data that Greg has generated,
and complemented by work by Tom Denny and Cam Wolfe
and then Kyle Cavanaugh
sort of as an overview.
So I think probably in another week or so
we'll be releasing that video to the whole campus
and I urge folks to watch it
and I hope that folks found this informative
just in terms of these specific questions
of how we're using these data.
So thank you again for all your work
and thanks for the conversation today.
- Thanks, Sally.
(bright music)