- My name is Barbara Han,
I'm an Infectious Disease Ecologist
at the Cary Institute of Ecosystem Studies.
I use machine learning to try and understand
how infectious disease is operating in a system
and whether we can make predictions
about the types of organisms that present threats
or high risk for infectious disease
that might come from the environment.
Machine learning is a way of using data
to discover patterns and processes.
It's sort of like data methods
that allow for pattern recognition
and drawing information out of large volumes of data.
It's useful in ecology because oftentimes in ecology
we have a lot of data that are imperfect in many ways,
you know there's multiple hidden interactions
that are unknowable in advance and so oftentimes
it's hard to control for those interactions.
There are multiple data streams
that are giving us various amounts of information
about different aspects of a system
and so having a way to combine them into one big analysis
is really useful for gleaning more information out of it
than would be possible through just looking at single
or just a few data streams at once,
and so it gives us sort of a more holistic understanding.
And also I think one of the strengths of machine learning
is that it's a little bit agnostic to process.
It doesn't assume that there is an underlying process,
it assumes that it's kind of unknowable,
and instead it takes more of a top-down approach
where it looks at the data first and it identifies patterns
that are strongly present in those data,
and in that process sort of offers up hypotheses
to the researcher for what might be going on in that system
to generate those patterns that you are seeing in the data.
So it's kind of inherently data-driven in that way
and sort of model-free.
So machine learning has been applied in ecology
in a lot of really interesting and creative ways.
Some of my favorite examples are
using machine learning to try and understand
what types of organisms might be most at risk for extinction
and how different groups of organisms
are differentially vulnerable to extinction.
That's work by Ana Davidson and colleagues
that was published in PNAS a while ago.
There's another group of studies
that thinks about invasion potential for different species,
particularly in plants.
There's a lot of data to suggest that some plants
are more likely to invade new areas compared to others,
and some colleagues at University of Georgia,
John Paul Schmidt and John Drake
have done some really interesting work to try and understand
what traits are predictive of invasive plant species
and whether it's possible to assign a value to controlling
those types of risks posed by invasive plants.
And the work that I do
has really focused on infectious disease
and trying to understand what types of organisms
are most likely to give rise to infectious diseases,
so what types of pathogens are more likely
to infect humans compared to others,
what types of mammals are more likely to carry
those types of diseases and transmit them to humans,
and what types of vectors, like tics and mosquitoes,
what is it about those
that vector human infectious pathogens to us
that distinguish them from the tons of species
that there's no indication that they're carrying anything
or vectoring anything that's harmful to humans?
So distinguishing those groups of organisms
is important for public health, but also ecologically
just really interesting to think about.
I think some of the major strengths
of applying machine learning approaches in ecology
is that it allows us to, as ecologists,
draw from our domain expertise in understanding,
you know, having the sort of background understanding
of what drives fitness, evolutionary strategies,
life history strategies, how ecosystems function,
what happens when we perturb them?
Taking all of that domain expertise
and matching it up with data, patterns that are offered up
by the machine learning algorithms from the data themselves,
I think it generates a couple of things.
One, it allows us to really take advantage
of the data streams that are available,
and secondly, it allows the algorithms
to sort of systematically offer up hypotheses
that are then testable by ecologists.
So rather than the sort of traditional approach
which is to make observations on the landscape
and then think of hypotheses intuitively
for why those things might be happening in the landscape,
instead we start with a whole bunch of data
that are increasingly available at our fingertips
and generating patterns from those data that are suggestive
of hypotheses that are generating those data patterns
and then we can go out and test those hypotheses.
So one example of how we have applied machine learning
to try and understand infectious disease systems
is to think about rodents.
So as a group rodents have over 2,000 species in general
and about 200 of them are carrying between one
and 11 different infectious parasites or pathogens,
so these are parasites and pathogens
that can infect humans and cause disease in humans,
so these are zoonotic pathogens.
So we wanted to understand, you know,
what is it about those 200 that carry diseases
that distinguish them from the remaining 2,000 species?
And so we trained the machine learning algorithms
on just these intrinsic variables
that distinguish one species from another,
things like how many litters they have per year,
how soon they reach sexual maturity, what they eat,
how social they are, what's their geographic range,
are they diurnal or nocturnal and, you know,
these things that are really sort of fundamental
to distinguishing one species from another.
And we asked the algorithm to learn the features
of species that carry many zoonoses,
and when we trained the model to do that
it gave us a really interesting picture
of what a rodent reservoir looks like,
and it turns out that rodent reservoirs
tend to be things with a faster life history strategy,
so things that reach sexual maturity early,
they have larger litter sizes
than the majority of other rodent species,
and rodent reservoirs also tend to have geographic ranges
that are depauperate of mammal diversity,
so there are many fewer mammals
that are in the geographic ranges of rodent reservoirs
compared to rodents that don't carry any diseases.
And so all of these traits come together to form a profile
of what makes a good rodent reservoir,
and it was really satisfying to see that it sort of
corroborates a lot of the independent field studies
that have been done by scientists
that are looking at specific systems, specific co-species
infected with specific parasites in a particular location,
and when you gather up those lines of evidence
a bigger picture emerges of why some rodents might be able
to carry more zoonotic pathogens compared to others.
It also suggests some nice hypotheses that can be tested
and follow up studies in particular systems
or in a comparative way, looking at hosts that differ
in particular traits that are deemed to be important
by the machine learning model, and we want to understand
why and how those things are contributing
to the number of zoonoses that they carry.
You can do this type of analysis
for lots of different problems, not just infectious disease,
I think that machine learning has huge potential
for the field of ecology
and has a lot to offer for forecasting in general.