Okay, thank you to our panelists, we have about 15
minutes left for discussion and a number of questions that have
been emerging here. I'm going to jump right in here and say,
there's a clear kind of opportunity or synergy between
the kind of data that the wearable sensors can provide.
And what how it might impact the models that we saw both from
Pienaar and Natasha, so I just like an hour and Natasha just
reflect a little bit about if there was a small set of pieces
of information about an individual, what do you think
would be the most impactful in the modeling efforts that you've
been doing.
Thanks I totally agree we've been thinking about this as a
potentially really hugely valuable source of information
at the moment we are using just you know self reported symptom
screens in order to encourage people to get tested as well as
our kind of routine ongoing asymptomatic testing program,
where I think that this can be really valuable as if, you know,
if people were in these wearable sensors, it could give us a
signal prior to when they even registered and noticed it
themselves that there was something wrong. And, and then
use that as the kind of a way to suggest encourage that person to
go get tested, and therefore help us, you know, detect covert
early. One of the really tricky things about code infection is
that individuals are infectious prior to the development of some
clinical symptoms. Yeah, but they these technologies may be
able to detect it in that stage where not only they're
transmissible but it's it's one of the highest transmissibility
periods that sort of just just prior to onset of clinically
observable symptoms. So being able to detect individuals
earlier in their time course of infection and potentially
through using these wearable sensors would be fantastic.
You know,
so well most of the models, they kind of look at the population
or sub population level I think some of these cancers could
really help us
take action at the individual level is both Natasha and I
imagine people go through these different stages differently.
The impact is different duration is different.
Some people may have no symptoms and yet they may be infectious.
Many of these things we don't know in detail. If we knew a
little bit more specifically for this person. They are quite
infectious right now and then, you know, they really need to be
careful about even staying away from their family members or
household members, those types of information could really help
us take some of the action reviews or recommendations from
the one size fits all public levels to a more smaller group
or individual level. And because if you think about it, many of
these internationals may look like they create disruption in
the way we know life. And, you know, having more detailed
information could not only help us better understand the
progression individual will also be able to take action at a more
smaller detailed individual level rather than just one size
fits all.
So now, john, I want to ask a question that a lot of people
are kind of directing towards you, it's it's clear that
there's opportunity for the kind of data, you're collecting to
inform the models that we've heard about. And so some of the
questions to you are these technologies that you've
developed. How far away are they from being available to others
to use not maybe not necessarily commercially available but are
they are we near. These being available to people and how long
might you use one of these devices and collect data in a
reliable manner.
Yeah, great, great question about Greg I mean I think as a
biomedical engineer your, your goal, ultimately is wide scale
deployment right you'd like to have a broader societal impact
and we're very much interested in that, as a goal. We have a
couple of startups in place everything I shared with you
today is really coming out of my academic group we have some low
volume manufacturing capability in my lab and so we can crank
out kind of a few hundreds of these faces but we designed them
upfront to be well aligned with established manufacturing
capabilities that support the consumer electronics industry so
I don't think there's any intrinsic challenge and scale
out, there's no cost impediment. I think, you know, for
deployment, at least in most places on the globe on, we do
have. Yo, advanced commercial on your deployments of related
devices similar form factor slightly different sets of
measurement functionality really focused on vital signs
measurements maternal fetal neonatal pediatric health. We
have funding from the Gates Foundation so we're deployed at
scale right now in Kenya Zambia, Zambia Ghana, will be in Indian
Pakistan by the end of the year I think five 7000 devices so far
will touch, probably 50,000 lives within the next 12 months
for these suprasternal notch devices kind of tailored for
COVID, we'll be doing a deployment funded by the God
into certain segments of the military later this fall, a few
thousand devices. And we have a large deal with a third largest
medical insurer here in the US and the current schedule is to
deploy devices onto 300,000 people by the middle of 2021,
we'll see how that plays out but but we're poised to do to do
scale scale deployments, you know, and we're excited about
that. I think everything is lined up, you know the the
interesting thing is, cough, and cough counting and measuring the
sounds of coughs and intensity and cadence and frequency and so
on. This is well known to be an important signature of disease
but it's never been measured continuously before so it's a
little bit about a discovery aspect associated with the
device, it's not just a matter of engineering you know devices
and manufacturing at scale and getting them out there, it's
also around the question of how do I interpret the data, most
effectively and I think that's what's great about having this
opportunity to share some of the things that we're doing with
this community because that kind of machine learning and data
analytics expertise, I think is going to be really valuable in
this space is a little bit outside of my own core areas of
expertise, and at the same time I think you know having that
analytics in place is going to be almost as important as the
devices themselves and so I think as I mentioned before,
there's a lot of opportunity there.
Thanks john. So, Natasha and pin our you can see in what John's
presenting and a lot of the people in this community. We
really strive to get devices that can collect continuously
information from a large number of people that relevant
information, but what that means is, compared to the relatively
static data and parameters that you've used to feed your models,
this would be essentially data that would aggregate and change
on a weekly, daily maybe hourly basis, how would that modify or
impact your modeling approach to have that much change in these
parameters.
I mean, I would say, the more they go the better for these
models, they can be adapted easily incorporating some of
those changes the parameters can be can be adjusted. And in
particular, if we can actually project, how some parameters of
some of the input into the model might change we can incorporate
that as well as we are making projections into the future,
just like we do for example with the, with the scenarios, about
interventions. We don't assume that people will comply with the
interventions at the same level. Throughout the next six months,
we look at the behaviors and patterns from before and the
project how those, you know behaviors might change moving
forward, and incorporated in a way into the model that helps
us, you know, for see some of those changes and then as new
data, information becomes available. You will adjust and
recalibrate based on the new information. So I would say any
additional, you know, information data or information
that by manipulating the data for the model that is always
very very much welcome and one additional thing I'll mention
very briefly is to also think beyond people because like I
mentioned very briefly. This panel focuses on, on COVID but
there are so many diseases where the transmission includes
animals, as well as the environment I think some of the
techniques and technologies developed by this community
could be very, very helpful for those as well. I just had I mean
I agree with, I totally agree with all of that, I think in
some, some domains there is going to be an issue in terms of
needing kind of data reduction and identification of what are
the key pieces of data that we need to measure in terms of
understanding this change so one of the things we're thinking
about right now is we have this proximity data. It's collecting
in, you know, information on on devices that are in contact with
other devices every five minutes and putting that to us. And
we're using it to infer to you know contact networks between
individuals, and we have simulations where contact
networks can change over time. And and we want to use the
concept of a kind of Bluetooth bumped data to it to help us
parameterize that. But, but there is a question about you
know how much of that data do we need do we need it between all
devices cell phones and computers and, you know,
whatever, Apple Watch you're wearing right, trying to
understand and kind of reduce that data set to understand what
are the key features and key pieces of data that we need that
have the most effect on Cova transmission is really kind of
the really important first step and then we can feed those key
features to diet, you know, into the model and have those things
change over time. But I think there is there is space for
trying to understand that and do that in that in a sophisticated
way.
Alright so I'm going to wrap up this panel and first with an
apology, our general chairs thought we should have made this
session 90 minutes and I said no 60 minutes would be enough and
they were right I was wrong, I apologize. You can tell by all
the questions that are in the q&a right now. But I want to end
giving each of you about 45 seconds or so, I want you to
reflect on what your experience has been as researchers who do
things that are related to COVID but you weren't planning to do
something like this until maybe seven or eight months ago. And,
you know, at this point, how do you reflect on how it's impacted
you as a researcher and just give some general advice to, we
had a lot of students here about how you take your research and
pivoted toward meaningful and pressing problems. Why don't we
start with john.
Yeah, that's a great question. I mean I think we were thrilled to
have an opportunity to contribute in some way and I
don't want to sort of overstate what we've been able to do but
we have a certain set of competencies and we got some
requests from downtown and you're we're happy to engage and
I think the students responded very effectively and very
energetically and forcefully write urgently to those to those
requests and so, I think, I think it was kind of kind of a
great, great experience for us and hopefully we've done
something that's that's helpful. Maybe, maybe not just for this,
this endemic episode we'll eventually get through it but
setting the foundations for things that are likely valuable
going forward. I think one of the consequences of this episode
is that it's really a heightened the awareness of the value of
telemedicine and remote monitoring. And I think that's
going to be a real sea change in the way that people think about
how to effectively on and efficiently deliver care to
patients, you know, and how to improve outcomes and reduce
costs, and so on. So it's been been been a very engaging and
rewarding experience but I think the other thing is, you know,
working with these frontline health, health care workers it
really inspires us I mean you know what what they're doing and
the fact that they're putting their lives at risk to help
people it's really humbling to see that, you know, and to
contribute in some way it's great but but those are real
heroes, in my view,
thank you john Natasha.
Yeah, I would say it's overwhelming but also incredibly
inspiring, I think even just speaking at this conference
today exemplifies how far we've come in the last few months
really everybody coming together across multiple disciplines who
are working on totally different things, trying to figure out you
know what they can add How can they can work together to
respond to COVID, and it's put me in contact with people and
disciplines and thinking about ways that we might integrate
data that I've never experienced, but it's really
it's where we should be as a field and I feel like actually
in a way COVID is great, and a great way that has brought
people together and facilitated open collaboration, because of
the urgency, you know, people have reached out and the
willingness to collaborate even when things aren't perfect and
you know hasn't been peer reviewed necessarily and just
kind of putting it out there and trying to make things better has
been so exciting really inspirational in general for my
research.
Thank you for now.
The lighting has done quite a bit of work in infectious
disease modeling. In the past, including pandemic flu. So in
that sense, the modeling aspect. Starting on COVID was a natural
transition. But I would say at a much more tightened timescale
that we academics are not always used to everything kind of moves
moves very fast, and also working very, very closely with
folks from the medical field and public health. So, in that
sense, it was fascinating.
My background is in systems engineering it highlighted one
more time. The importance of looking at this as a system
because just trying to work on one piece doesn't help you might
know of a test works you might you might have, you know, the
lab, but we need to have all the pieces otherwise you just don't
have testing capacity and you cannot deploy. So, again, once
again highlighted, you know, for me and my team how important it
is to work together across different fields and
disciplines. When you are facing a complex situation like this.
And I guess. Finally, you know, it's also being very patient
sometimes and then your question, you know, we came up
with our first predictions about the disease spread in Georgia.
Again, taking into account. People behavior. And we were
hearing at the same time this is like late April very early in
May, or you know the peak is already behind us and things
like that, then our models were predicting the peak is gonna be
sometime mid to late August. So you you have to really like kind
of keep your ground and say, you know, we have we have the data
and the science here. And, of course, be open minded but, to
some degree, also stick to what with what you know. So those are
some levels, it was a fantastic time and I think for for the
students. I'm so grateful for the fantastic team and all the
work that they've done it was a great learning experience.
Right, so you all kind of mentioned the importance of
coming together, teamwork interacting with a wide variety
of people and I think the three of you agreeing on relatively
short notice to come and present at this conference is yet
another example of how we as academics, scientists can come
together to attack problems so from the bottom of my heart I
thank the three of you for inspiring us and for work, the
work you're doing. In these trying times and I wish you all
the luck going forward. Thank you very much. It's all a round
of applause. All right, now we're going to transition to the
town hall I believe it's actually going to stay in the
same zoom channel. No change because I see all the other
conference organizers are here so yes I think Koji Are you
taking it over. Yeah. Okay, I will mute myself and listen.