So with No further ado, I'll ask our
first speaker, Professor Pienaar Keskin to check if you'll share
your screen, and I'll do a quick introduction. I am very
fortunate to have known Pienaar for several years here she's one
of my colleagues here at Georgia Tech and is a preeminent scholar
in understanding and modeling disease infectious disease and a
variety of related topics we've decided to have her go first
here today because she's going to give a basic understanding of
how one goes about understanding and modeling the spread of
disease and work she's done specific to COVID-19, both in
the Atlanta Georgia area and beyond that. Our second speaker
will then follow on with a more focused discussion about
understanding the spread of disease, on a university campus.
And our final speaker will talk about a technology that is
wearable technology that you can use to help understand the
progression of a disease. So please welcome Pienaar cast
Kenna check who will talk to us about infectious disease
modeling.
Thank you very much Gregory and it's truly an honor and a
pleasure to be here, and thank you all for participating. So
this is going to be a 10,000 feet, very quick overview of
some aspects of infectious disease modeling and how your
community has been helping modelers like me and others. In
populating these diseases with meaningful data, and in other
ways. So when we think about disease modeling really there
are two key components. One of them is the nature of history of
the disease, essentially, how it progresses within an individual.
After contracting the disease. And then the second component is
how it spreads in our population. So in looking at our
model results. There are several potential metrics of interest,
such as how many infections we receive per time period the
daily, weekly, what is the timing and magnitude of the
peak, this is basically when the daily new infections reach their
highest level. I'm sure you all have heard the word peak quite a
bit over the past couple of months, we are interested in the
total number of infections or the percentage of the population
infected, that tells us something about the broader
impact of the disease in the population. We may be interested
in adverse outcomes such as the number of people who are
hospitalized or who die. And then of course we are interested
in the resource needs, such as how many hospital beds, we may
need ventilators or other types of resources. So, the percentage
of population infected gives us an idea about overall impact,
like I said, as well as the resource needs across time. The
peak helps us understand when we are really, really stressed in
terms of our resources. How is the situation is going to look
like are we going to have enough capacity to meet the needs,
especially around the diseases around speed. So, we look at
these numbers metrics and estimates together to get an
idea about about the impact and also whether or not we will be
able to respond properly. And these metrics, typically are of
interest, not just in aggregate numbers but also by dissecting
into into specific details such as by age group or geography or
specific sub populations, because the impact, typically is
not uniform across population so we are interested in the impact
both geographically and over time as well as the population
level. So, we are of course, in addition to understanding how
the disease would spread the nature of history, if we do
nothing, but we're also very interested in modeling and
understanding the impact of interventions.
The models can inform us if we have limited amount of resources
to invest into these interventions or for whatever
reason, we may be facing challenges for example educating
people to focus on a particular intervention. So we want to
understand how to allocate these loads of their personnel
resources across different interventions and what we do
could again depend or change geographically or over time or
by sub population not necessarily static across the
course of the disease. And finally, I keep coming back to
resource allocation, this is an area where epidemiology models
that informs us inform us about the spread our combined, you
know in in creative ways optimization models is coming
from my field to give us a good sense of what might be the best
way to allocate them into three sources. So going a little bit
into the disease spread Part one is the nature of history how the
disease progresses within an individual, typically an
individual would go through the different stages, after being
exposed to the disease and I'm giving the example from COVID-19
here. Some people will go through a symptomatic phase.
Others might go through the stages, completely asymptomatic
or with very mild symptoms. And then the majority might recover
some people might need hospitalization, or my die is a
result of complications. So in the models we try to capture
these different stages of the progression of the disease in an
individual, and how a person progresses across these
different stages will depend on their age as well as underlying
conditions. In the absence of any, any treatment or
intervention. So all of these different parameters. Come to
the models from from the work of others. In the medicine field or
other fields such as yours. So you may have heard about si our
model in the literature or se IR models in the literature, these
are kind of the basic versions of these models that you will
typically see. And they, they capture the key components. For
example, if we simply look at is somebody currently infected and
not very about whether or not they are simply that they are
symptomatic or not. That will be our infection stage so that's
very we'll see the eye, or in fact infected. And under
recovered face, you know, could be combined by including some of
these other stages so these are the simple versions simpler
versions of the models that you may have heard about or seen in
the literature. So, our, our mind is has been totally
occupied with COVID of course over the past many months. But
when we broadly more broadly, think about infectious diseases.
There are many others, which are transmitted through different
pathways. So COVID is a respiratory disease flu, whether
it's seasonal flu or pandemic flu also falls into the same
category. But then we have other diseases. For example, such as
malaria, or guinea worm, which are transmitted through vectors,
other diseases the environment plays a role in the
transmission. So the models still follow relatively similar
structure as the one I described before, but if you are modeling
a disease such as malaria, you will not only model the human
population but you will also monitor the mosquito population,
as well as the interactions between them. So my research
team has done work in several of these diseases and are able to
follow up later in case any of you are interested in those
others. So the second component is the spread of the disease,
across the population, again I'm going to give an example from
COVID. My team uses agent based models, which develops and uses
agent based models and these models actually capture some of
these interactions at a very detailed level on like some of
the models such as a car or Si, si a car, where they are
captured at a higher level, but not necessarily individual
level. So an agent based model, we can actually model, each
individual as an agent, and we can capture the interactions
between them. For example, angels would belong to a
household of different sizes, where the input about the
household sizes and so on would come from, from data. And then
they would interact in with their household members at a
closer level. they would also belong to different peer groups
depending on their age, which might be work place or school.
And again information data about workplace sizes pool sizes etc
would come from the data that we have. And then finally they
would interact in a broader community such as you know
visits to grocery stores and other places where they will get
together with other people who are not necessarily within their
work place or household. And as you can imagine, we also model
these interactions with the different possibility of disease
transmission if you live with someone in the same household,
the chances of disease transmission is much higher
compared to the interactions that happen in the community. So
lots of data goes into populating the this portion of
the model. In terms of how these interactions take place in
practice. Now, a ton of model parameters I'm not going to go
through these but just, just to give you a little bit of flavor.
So what you see on the left is a sample of the parameters that go
into the nature of history. And then what you see on the right
is a sample set of parameters that go into the essence of
resource allocation. So we project the progression or the
spread of the disease but then we also project. The need for
resources, particularly I'm showing some examples here. And
from healthcare related resources, is the development of
the disease so this is an area where researchers from computing
played a very significant role in helping us estimate some of
these parameters, whether they're at the individual level
or at the community level, or in terms of the
resource needs such as hospital taxi beds and so on. In case of
COVID. So resource allocation and intervention so
interventions, typically we can put them into two big picture
categories. Pharmaceutical interventions such as vaccines
and antivirals, and these usually do not become available
early on, if you have a new infectious disease such as
COVID, for others that have been around for a long time, they are
available, and then non pharmaceutical interventions are
primarily behavior behavioral interventions, such as school
closures terrible restrictions people voluntarily engaging in
different types of physical distancing wearing masks and
things like that. And and we also could think about some of
these combined strategies or interventions such as testing
tracing and isolation, by themselves, they may have
limited effectiveness but when you put them together in
combination they are really impactful compliance can change
a lot. This is another aspect that we try to put into these
models when we evaluate these intervention scenarios. This can
change by geography, it can change over time, people might
be less colorblind with certain interventions earlier on, if
they think the disease is not that serious and then the
compliance might might increase and then towards the end, it
might go down again. So all of these different scenarios, go
into some of these disease spread models so we can actually
evaluate their, their impact. And then of course the big
picture again is how to allocate value and how to allocate to the
special set of resources, whether it's vaccines
anti-virals efforts in education, educating the public,
and so on. So those are again areas where data and information
coming from, from the field of computing, help us a lot. For
example, when we think about compliance. I will give one
example say mobility data. We may not know exactly who is
supplying via surveys and so on we may get limited information,
but when we look at more visitor data that gives us a very good
idea or understanding of people's, you know, travel
patterns and that could be used as an indicator of their
compliance with some of these physical distancing measures
such as shelter in place, or, or other types of recommendations.
So I'm just going to I'm going to share two small pieces of
results from some of my team's work, where we looked at a
variety of scenarios to project the disease spread on the on the
left side of the graph you see a very typical way we would look
at this disease model outcomes. You have dates on the x axis and
then on the y axis we have one of those metrics that are of
interest to us. In this particular example I'm showing
you daily new COVID-19 infections. This is specifically
for the state of Georgia, and then you can see here the zigzag
is the actual cases or our estimate of the actual cases
also considering the fact of underreporting, and then the
solid lines are some of these projections coming from our
model. And then what you see on the right side are some
additional scenarios that we tested. Looking at school
reopening scenarios that are shared with us by Georgia
Department of Education. What if we delay the opening start
online. What if we had kids come to school every other day in two
completely separate cohorts, what's the impact of some of
these potential scenarios. And the one takeaway for us was that
actually having this alternating day scenario alone would select
the domain the opening significantly reduces the
potential number of connections. So these are the kinds of
results that you would get from these models and communicate
with policymakers. So, going back to the data and how the
UBICOMP community and others, again really help help modelers
or cooked food, help even further moving forward. Any kind
of information about transmission patterns and
probabilities. These vary wildly depending on the disease,
whether they are indoor or outdoor for example when we
think of COVID. The impact of an international reducing to
transmission. What is, what how much does it help to wear a mask
depending on the type of mask and the duration, and you know
our proximity to the person so these are all the types of info
that would come. You know from the work of of different
communities and then would be used by modelers surveillance is
another area which is very important, and the base, some of
it could be symptom base can be infer based on symptoms, whether
or not somebody is infected even, he may not have a test
available, due to capacity or other limitations. And finally I
think an area where I see a lot of potential for, for, for this
community to help modelers is for us to better understand
behaviors. So you can take your horse to the water but you can't
make the worst thing to water. So how do people interact. How
To what degree do they follow the recommendations, how much
they comply with interventions. And not everybody else
sheltering in place when a shelter in place orders, in
effect, so these some of these behavioral
input or data that's really essential in how we create
scenarios in the model is variable to really benefit a lot
from additional computing. So, thank you very much and looking
forward to the discussion.