All right, now let's move on to the remainder
of our first session on landscape and framing.
At this point we will begin taking audience
questions and you'll be able to ask a question
by going to the link on the website that says
'join the conversation' at hai.stanford.edu.
I'd next like to welcome our speaker Nigam
Shah. Nigam Shah is a professor of biomedical
data science at Stanford University. Nigam,
you can share your screen and slides now.
Welcome.
Thank you. So, this is a story that is spanning
multiple departments. I am just a messenger
here. This is the work of over 60-70 colleagues
who got on the same page at this URL below
and it's Stanford internal. So if you are
a Stanford person you can access it. It is
not a public document, but I'm going to walk
you through the highlights and this is our
take as an academic medical center to see
what we can do to help respond to this pandemic.
So if you can go to the next.
So the overview is that in such a situation
there are multiple kinds of information needs.
So the first bucket is operational planning
and there we might need to know things such
as how many patients do we expect in our region?
How many floor beds are we going to need,
how many ICU beds, how long will our PPE last?
The second bucket is clinical care decisions.
We just heard that testing had been limited.
Who do we test? Can the presenting symptoms
help us screen people better? And then the
last bucket is these broader research questions
that drive the science. What are the drugs
that might alter clinical outcomes? What are
the characteristics of the [illegible] of
the patients worldwide? And all of these are
efforts that several of our faculty are already
engaged in. And let's walk through the first
a couple of them. So if we go to the next
slide.
Here there should be a blue box with the information
sources that are available to us. These would
be sources from the region such as the county
public health department, the census breakdown,
and then institutional sources from our testing
facilities and so on because we were amongst
the few places that started testing very soon.
These sources give us estimates of the growth
rate and the disease burden in our neighborhood
and some internal signals about how often
is the test coming positive, what is the rate
at which we're admitting people? And then
if we go ahead, that feeds into three different
kinds of modeling efforts that help us plan
hospital bed and resource use, that allow
us to project county level information, and
also estimate policy impacts.
All of this then feeds into our resource planning,
which is geared around planning our surge
beds, modeling our protective equipment usage,
and then also partnering with our county and
as one of our county officers put a beautifully:
"Can we think of the county as one large hospital?"
And I think that's the right sentiment to
plan with. And then lastly, that as we're
doing all of this modeling, we also learn
some clinical insights such as for example,
the co-infection rates with other respiratory
viruses as [illegible]. And as a result of
that, it is not okay to test for, say, influenza
and based on that rule out Covid.
And what I want to now focus on is that there's
two kinds of models that are going around
that most of you probably heard of by now.
There are these things called CR simulations
or epidemics simulations that capture the
dynamics of the epidemic. These tell us the
impact of the policy interventions that help
us answer "When can we get out at least shelter
in place situation?". And we need 10, 12 diverse
inputs for these, which are all practically
guesses at this point. Then there are these
simple calculators that tell us about the
next few days. These state ready few inputs,
cases, hospitalizations, bed capacities, and
then help us plan for the next five days so
to speak. So here's a screenshot of the epidemic
simulator. And this is from the open AI group.
And the point here is there's many such things
out there that give you this beautiful academic
curve.
But what we need is accurate inputs. And I
love this quote from FiveThirtyEight that
says: "Think of making these models is like
making a pie. But then if your recipe says,
add three to 15 chopped apples, steaks or
Brussels sprouts, depending on what you have,
that's going to resolve in a differently tasting
pie." And that's a good way to think about
these complex models that despite everybody's
best efforts, we don't yet have accurate inputs.
And so what we started doing is to say: "Can
we focus on getting the accurate inputs?"
So here's a plot from the Clarke County of
the number of cases and hospitalizations.
And typically what happens is you would start
with this blue dot, a case estimate. In this
case, as of March sixteenth you would then
guess how many infected people are there in
the county based on that guess, you would
project how many will need a hospital bed.
And then that gets you your number.
But because there's uncertainty in these estimates,
these numbers could vary between 1,500 to
about 380. And I'm just going to go back and
forth. Now that's about a fivefold difference
and that's highly uncertain. You can't plan
a hospital with that level of uncertainty.
So we said, well, we're also tracking hospitalizations.
Can we not just use that same CDs to project
where the next dot is going to be? That's
where this question mark is flashing back
and forth. And so that's what we started doing.
And the URL below, will walk you through our
planning around that, but our suggestion is
use hospitalization data from your local region
for the health system. You'll be much better
off in your capacity planning. And then remember
this 12 to 14 day lag between interventions
like staying at home and then peak demand
for hospitalizations and day to day variation
can be misleading. And we got to be watchful
about that.
That one number trends down doesn't mean that
we're out of the woods yet, but California
has been amongst the most forward looking
and amazingly efficient in responding to the
situation. So in order to help other people
use this kind of hospitalization data as well
as share our planning about our own hospital
and so on and to use it for others, we released
these calculators. A URL is on screen right
now. There's one that helps you allow new
hospital bed and resource use projection.
And then the second one allows you to do your
county level or regional hospitalization predictions.
The red box shows you that you can use confirmed
cases or hospitalizations as inputs. The person
highlighted here, David Shanka, is the person
who led this large team to get all of this
done on a very short notice. So kudos to that
team.
And then I would just like to give you a flavor
for the kinds of things we're monitoring as
an institution. This is sort of our situational
awareness. We're looking at the neighboring
counties, we're looking at case rates, hospitalization,
how are the ratio of new cases changing over
time? How is the number of new cases changing
over time? And all of this feeds into our
modeling efforts that then informs our hospital
and our neighboring counties and our peer
hospitals at "how should we think about this
situation and be prepared". And then lastly,
I'll zoom out a bit. So all of the things
that I talked to you up until now were "how
are we responding to make sure that we're
prepared", but at the same time we do need
to understand what's going on in the population
and another group of faculty of Eleni Linos
and Julia Simard are running the Stanford
Coronavirus study.
And then Dr. Rusty Hofmann and Steven Goodman
are running this daily health check, which
is actually going to be launched very soon,
I think today or tomorrow, so that we can
monitor what is going on with people, how
are they feeling symptom-wise, are they getting
testing? And then inform that larger geography
information in our planning and response.
So that is the gist of the effort. And I'll
end with this URL, that if you have questions
that you believe the Stanford data science
team may be able to answer, please submit
them here. This is a public URL. There's no
guarantee that we can get to every question
that you submit, but we will try our best.
And I'll stop here.
Thanks so much, Professor Shah. For everyone
listening, you get a good sense here of the
ways in which an interdisciplinary approach,
bringing all of the resources to bear to people
who work within the university can help inform
this Coronavirus response, so it's not just
a matter of providing bedside health care,
issues of masks and PPE and tests, but how
data scientists can produce important models
that will predict the path of the outbreak
and how to provide a better response from
within hospitals as well.
