– I thought I’d run
through a little background on what EcoHealth Alliance
is and what we do, and then I think that
and I’ll just use that and count it as a theme
for this One Health and I’ve been calling it One
Health in the 21st century, though we’ve been in this century for almost two decades now. But it doesn’t seem that, it’s just starting to feel that way to me. And I wanted to kinda move us along in our thinking about
integration between disciplines and kinda where this One Health started and say more about some of
the possibilities we can do, and thinking a little creatively. So I don’t know if you’re
familiar with EcoHealth Alliance but we’re a 501(c)(3), a
non-profit based in New York. The focus of the work is on the linkages between ecology and
health, hence the name. The organization’s actually
about 45 years old. It was called the, was
started by Gerald Durrell, who was a British naturalist and liked to travel the world and help wildlife and he formed a group in Philadelphia called The Wildlife Trust to
raise money for his program in the UK, actually in Jersey Island. Over the years it grew and they
started doing more and more health-related ecology
work, so about 10 years ago it changed the name to EcoHealth Alliance so it is quite old. The alliance part is that all
our projects are implemented by local partners, so wherever
we work around the world or in the U.S., we have
implementing partners and that number is somewhere above 60 of these kind of partners. Some are government
partners, some are NGOs, some are universities so we’re very small. We have a, I think our
total staff is 50 people. But we have hundreds and
hundreds and hundreds of people working on projects around the world and they work in their country
and we have a big focus on local engagement. They help design the projects
and implement the projects. I think that’s enough of that. The big, where the spin-off really came, you know 10 years ago, was
in some of the modeling work that members of the team did
and you might remember this from 2008 which was
basically the hot spots of disease emergence, so a big focus in the organization’s effort is still on emerging infectious diseases and the team in New York
is really, a large majority of that team is the modeling team, and doing this type of work in technology and software development there, and then so I’ll kind of give you a mix of what we do on the modeling side, but then some examples of
what the field projects end up looking like and how we
somehow pull those together. So we all know, hopefully
everybody’s familiar with this trend in emerging
infectious diseases over the last few decades we’re
all familiar with the fact that most of those are zoonotic and most of those are linked to wildlife. If we take kind of a
historical perspective and looking at where
emerging infectious disease events have occurred, we get
a very biased view of that based on kind of the inherent
nature of science and medicine and the fact that where
are diagnostics better, where are people more likely to publish, where are people more prone to
reporting anything they find, and so it looks like, if
you just looked at all emerging infectious
diseases lumped together, they’re very focused in the
developed world in Europe and North America, and Southern Australia. But what the original work
that was done with hot spots and now we’re kind of on a new version, kinda looks like this, is looking
at the underlying factors, risk factors, or the
characteristics underpinning where those events took place, correcting for the reporting bias, and then remapping where
the risk factors are, rather than mapping where the events are. So there’s several versions
of a hot spots map, you know Anthony Fauci uses one that’s very dramatic basically. The whole planet is covered
with emerging infectious disease events because he
picked ones that stood out that we all remember
and the point was to say it’s a global problem. When you actually look at the risk factors for emerging infectious
diseases you get kind of a different distribution and
the world is not all equal. So you see, some places
certainly have more of those risk factors than others. A lot of people have been
calling these the drivers of emerging EIDs so common to say what are
the underlying drivers. I’ve kind of just
recently learned from many of your community in basic risk analysis in epidemiology to stop
saying they’re drivers ’cause this is not
causality, we don’t know what the causes of these events are, we just know they correlate
with certain conditions. So pushing, going back to the
traditional term risk factors there but what we see really drives that. – Can I interrupt briefly?
– Sure! – [Attendee] You gonna cause any, are any of these a causal relationship? – I can’t tell you why, I
think many of them are proxies for something else that’s going on. So we, human population
and human population growth are clearly linked but I don’t know that by having another kid, that causes Zika. – [Attendee] Right, but
like land-use change and it’s– – It’s, I don’t know
what the mechanism is. I think it’s good, thank
you for bringing it up. So you know the more
detail you pay attention and you say, well what’s the
mechanism in land-use change? Is it a change in the density of animals? Does it change the density of people? Is it contact rates? Is it vector abundant, well, I don’t know really what’s truly causing the problem. I just know it’s highly correlated. – [Attendee] So is the
map showing locations of the first infection
recognition of a new disease? – That is. This map is population that when we do, so take
about, it’s over 250 globally gridded databases
on demographics, GDP, number of health care
workers, number of hospitals, road systems, so you can get
globally gridded data sets for all of those and then
match them with these and pull out the ones
that actually correlate to those locations and
that’s these factors that are on the left basically. Land-use change, pasture
change, urbanization. Those things seem to correlate
with where those red dots were and then you map the factors instead of mapping the events, make sense? Now of course at countries, every country’s a little different and this is where the
resolution starts to get weak because you might only
have four or five events in the last 60 years, emerging disease events in one country, so now we’re starting to lose
our resolution of course, statistically speaking,
so it is not as perfect, but you do see there are
different correlations, different drivers, different risk factors in different countries,
which of course makes sense. The world is not homogenous. But we also see, and I
always use this slide to say I’m encouraged, at
least for public health, and for preventing which
is we really try and focus on prevention, is that these
drivers or risk factors line up with different types
of transmission pathways. So with land-use change,
we’re seeing a lot more with vector-borne diseases,
hence way and throughout. Maybe it’s a change of
vectors distribution. So now we’re starting to
kind of get a little closer to what it is, or in
agricultural industry change. So these are really big
factors for disease emergence. You get more things with
agricultural industry about contact with livestock and, you know, contact with
animals, oral transmission, so you get more fecal-oral
emerging infectious diseases than you do with land-use
change which are related about disturb, or habitat
disturbance, which probably, and that’s something to do with vectors, vector abundance, which 60% of those are vector-borne diseases
that we see there. The encouraging thing I find out, because a lot of people
say well every time we find a new infectious disease
or a new emerging disease we have to spend years figuring
out what to do about it and then we have to develop a new vaccine, that all takes 15 years,
and so it’s like one bug, one drug, one bug, one
drug, and now we have 256 emerging disease events
so it’s gonna take us several hundred years to
cope with all of them. Except in the world of
public health and prevention, there’s actually things you
can do to prevent transmission that have commonalities so all
of those vector-born diseases kind of lump into this category
of improving vector control and reducing human exposure. You don’t actually have to come up with a brand new treatment in many cases if we just use the old
treatments more effectively. Not that we do vector
control very well, obviously, with malaria and Zika, but we
actually do know what to do, we just don’t do it very well. So it’s not the lack of science, it’s the lack of implementation. So we can look at these different ways. We did this based on the IOM’s
drivers of disease emergence which actually came from Odette Comeau’s Institute of Medicine report, and we looked at all those
emerging disease events and put ’em, scored
’em in these categories and once again we get the same. So using different methods
came up with really the same type of results about land-use change, agricultural industry change
are really the big risk factors or drivers as you just called
it, well that time was up. The thing for me that’s fascinating, ’cause I spend a lot of my
time working with wildlife and bushmeat trade and
early days in the late ’90s with Ebola outbreaks which
were clearly all linked back at that time with
eating chimps and gorillas and some contact with bats, but we didn’t understand that thing, so I always thought
bushmeat was a big driver of emerging infectious
diseases so you have HIV/AIDS, you have Ebola, so the big headline ones, SARS with the markets
and the wildlife markets. But really it’s a very tiny
percentage of the real number. So it’s always sobering
when you look at real data, you find out that things we
really trusted and believed in are not necessarily true. So it’s been eye opening for
me to have actual objective, an objective look at this. And you can do it for
different kinds of diseases, it’s foodborne EID events,
mostly actually related to bacterian set of viruses. Our organization really
focuses on viral diseases so I think we’re missing the
boat in the foodborne world but it’s an area to explore, and so my favorite foodborne
disease of course is Ebola, which we don’t typically
think of that, but I do. So that link like I said,
we spent a lot of time with the bushmeat and
we kind of think of this as Ebola outbreaks, this
is what was imagined but really that root where they begin comes from this world which
is the consumption of all meat and the contact with wildlife
in some way or another and it’s a huge, it’s a huge
consumption pattern there. This is about a billion
kilograms a year of bushmeat are consumed just in Central Africa alone, not West Africa, the
East, not other parts, not Asia, just Central Africa. So you see when you think about contact, the opportunity for contact is pretty high so even though some of these
emerging disease events are really rare, you
can start to understand that while the prevalence or the presence of some of these infectious
organisms are very uncommon, extremely rare, even in wildlife, it’s kind of overwhelmed
by the volume of the nature of the volume and as
I mentioned yesterday, that even influenza in
chickens and seeing people are surprised, this linkage
with influenza and poultry, and the fact that we somehow say that some influenza’s a natural occurring disease but we have raised 10
billion chickens in the U.S., and we’ve raised 10
billion chickens in China and there’s nothing natural about that, it’s only been present in
the history of our species, it’s only been going on for about 50 years so there’s nothing really natural about that promotion system either, and we force through these rare events into the large, multiplied by a billion, multiplied by 10 billion. We only see a very small percentage, less than 10% of these events
are even linked to bushmeat so it’s just a really kind
of a fascinating thing that we see it at all but
I think it’s about volume. So of course what people
here in the U.S. worry about is that you don’t have to, you might not live in a hot spot, but you probably live near an airport that’s eight or 10 hours
away from a hot spot so you can do some nice modeling
which is just about, again, calculating those risk factors with airline travel and you
get something like this, which is EID risk for an airport which I’m seeing becomes applied, so I’d mentioned earlier
we’re doing some work with Homeland Security and it’s really about looking at airports and customs and where more vigilance is
required versus less vigilance and you see, especially
if you live in Canada, you’re pretty safe at the
airport, it’s a great place to fly into if you wanna go through Canada
(group laughing) on your way (chuckling),
it’s always better. Unfortunately Dulles is red,
I think it’s orange red. – [Attendee] Has this been published yet? I know it says in review. – [William] I don’t know. – You forgot.
– I don’t, I don’t think so, I don’t
think it ever got in. No, Parviz who did all
this work is now a fellow at the State Department so
she’ll never publish again. (group laughing) – [Attendee] Why’d she have to do that? (group laughing) – But we have turned some of it into some software technology
so this was published, we call it FLIRT, which is the flight information
risk something or another, it’s just a cute acronym,
and we ran it for Zika and got that published
and this is up online available free for anybody to use so we took Parviz’s models and
just put it online as a tool and we’re also applying with
this for some other country what we’re doing for Homeland Security ’cause they like high resolution. But you can go onto this software and you can see if there’s
an outbreak somewhere in the world, you can
see where those flights or passengers are going. Yeah, so that’s interesting
and of course it doesn’t have to be, the software
doesn’t care about it being an infectious disease, you can just see, you can use it for anything
related to passenger travel. And we just think that is a good proxy for the possible relative introductions since things do move with humans. Even some of the livestock diseases, so if you look at, there’s a lot of worry about African swine fever,
big outbreaks in Europe, it was introduced in the
Caucasus originally from Africa, the Russians spread it
through their military system up and down the western part of Russia, finally broke into the European Union, and then people thought it was
kind of contained in Poland, and the Czech Republic, and Ukraine but of course now two weeks
ago it popped up in Belgium which is clearly spread
by people, either in a car or an airplane, it did
not walk 3,000 kilometers across Europe and pop up in Belgium so air transport’s probably
a really good indicator and certainly for the risk to the U.S. that would be the case and then you can use that
same approach of looking at the risk factors and
mapping risk factors instead of mapping events and we moved on that for the original work was zoonotic diseases, you can do the AMR, it’s a nice world map for drug-resistant pathogens,
vector-borne pathogens, whatever you want, so once you kind of capture this approach, I’ll show you some examples
of what else we did, and you can look forward with
those same kind of factors if you want to, this is the B1
scenario for climate change, so you look at what the
world would be like in 2050 under that scenario, which is
a fairly conservative scenario and then map across
something like where would the distribution of
possible reservoir host be for a certain disease
and so this is Nipah virus which is right now kind
of in India, Bangladesh, into Malaysia but you
can see actually green means reduced risk and
red means increased risk so you get a different distribution because these diseases,
infectious diseases, are linked to ecosystems
and ecosystems will change with climate change,
so you have to rethink the distribution, the
geographic distribution. Yes. – [Attendee] For the reduced risk what is causing the reduced risk? Is it the conditions in the ecosystem, is it the conditions of
the social environment? How is it indeed that there’s reduced risk?
– This was– – I’m interested in–
– That’s a great question. This is based kind of on
ecological factors there, and a lot of that is, is the Nipah virus, is a mammalian reservoir
host so it’s really about the distribution of
possible hosts for that, ’cause without the reservoir
it’s not maintained in humans, it’s maintained in an animal reservoir. So once you lose that reservoir, you lose, the disease drops out ’cause humans can’t maintain it that way. There’s some interesting things ’cause people say, well why would it come to the Western Hemisphere
because you need a host but we’ve also learned
over the last 50 years, I don’t know the exact number, but I think it’s close to a thousand, does somebody know the number
of invasive alien species in the U.S. from that work,
invasive, it’s hundreds if not thousands, hundreds
that I’d comfortably say of alien invasive species
in the United States over the last hundred, 200 years, so that’s another thing
that’s happening all the time in globalization, it’s
this invasive species. Someone would say, oh you could
never have any Nipah virus in the Western Hemisphere ’cause we don’t have the right reservoir,
there’s actually, you can show that actually
the habitat would be very good for the right reservoirs
with climate change and then all you have to do currently if you release one of
those animals intentionally or accidentally, they would
die but in 2050 it won’t die, it probably would become established so there’s a lot of the
reason invasive aliens that are not here is because we currently don’t have a good habitat for them, but that’s all changing
too ’cause they’re always being released and introduced if you look at Florida as a great example. And I just wanted to point
out that it’s not just about infectious disease, I’m
gonna talk about EIDs and infectious diseases this whole morning but it’s not really, all of these kind of ecological factors. Of late I mentioned AMIs, a
kind of infectious diseases but anyway, this is kind
of, this is where, you know, if any of you know Assaf
Anyamba who did this work, and this is kind of an El Nino event, which is another kind of
proxy, severe weather, kind of gives you an
indication of what might happen with climate change but
some of these things are, you get fisheries collapse as they have nutritional problems here. We see it in marine mammals and sea birds. You have massive die-offs of sea birds because the fisheries
collapse from the change in water temperatures so
it goes from cold water to warm water, warm water
in an El Nino you lose your, the fisheries stuff kind of goes away, sea birds start dying,
sea lions start dying. And in the South, I
didn’t mention of course, right here Louisiana’s
really prone to copper and selenium deficiency so
you have nutritional disease with El Nino events because
there’s more rainfall in the Southern U.S.
during the El Nino years, so the grass grows faster and greener, and people that way, this
is a big production area for livestock actually
most livestock in the U.S. are born there, cattle, it’s the biggest cattle
production area in the U.S., and then they’re shipped to
the Midwest for feed law, for raising, but they’re
born in Louisiana, Mississippi, Alabama, Florida and that rainfall, the calves
all get copper selenium deficiencies ’cause they’re
eating bright lush green grass and so they drop dead unless the ranchers actually supplement them with additional copper and selenium, so
it changes the economics of raising livestock and the cost of beef just based on the rainfall
patterns in the Pacific Ocean. It has global effects, so
we see all around the world that one event affects
the whole health nature of the planet. And then I wanna get into
this One Health thing that we’ve kind of always
were talking about these days and just to say that this relationship about humans and the
environment and animals changes depending on which disease
we’re talking about or which factors, I mean, like I said, doesn’t have to be an infectious disease. This relationship of, who are the players, ’cause we’re really talking
about interdisciplinary collaboration or
intersectoral collaboration, and I just wanted to just kinda say that it’s not always equal. Sometime it really is driven by an issue, and one sector and the other
sectors or other collaborators have a little to contribute, it’s not always an equal partnership and everything that we feel like, there’s always contributions you can have by including other groups of
people who think differently and have different approaches, so you just wanna kinda use
this to illustrate the fact that there’s a flexible approach. We tend to focus, and my
interests certainly are about infectious diseases,
it’s not just EIDs, and really the thinking behind that is this, once again, conservative number of about a billion new cases a year of diseases that are endemic or emerging, and it doesn’t really
matter but they are zoonotic and they’re linked to animals
so it’s a really big burden of disease on humans and of the planet. Some people say up to two
billion and I don’t disagree with that, I’m just not quite
comfortable saying that number but let’s say conservatively a billion and 2/3 of those are zoonotic, actually 1/3 are not. So we have this kind of concept well what can we do about that so you have the standard epi
curve of a disease outbreak and I’m kind of thinking
like what can we do on the left side of that curve, understanding that 66% of those
infectious diseases or EIDs are linked to animals,
can we do a better job on the animal side and suspend
an effort in One Health to engage the animal health community and the environment community really so we can look at on the left side do a better job of forecasting
prediction prevention and reduce the impact on the humans, which is that red part, and
that little light blue section is really the benefits to the
cost savings effectiveness, increased effectiveness, no,
you know, reduced tallies. Something we can measure to
show that working earlier prevention, earlier
control, actually has these demonstrated benefits and
hopefully try and measure some of that, which is
not being done very often. And so I will give you two examples. When would you like me to stop? – [Attendee] I’m showing we
get kicked out around nine. – Okay.
– So you stop whenever you’d like. – Okay. – [Attendee] You can go to spec. – Okay, so let me give
you just two stories then. There is time for that. So everybody familiar with leptospirosis, it’s probably one of the most
common bacterial diseases. It affects humans and
wildlife and livestock, and it’s basically, it’s not ubiquitous but it’s common around the world. It’s treatable in animals,
there’s actually a vaccine to easily, very easy to treat. But usually confused in much of the world dengue fever, other
infectious diseases typically are often confused,
they’re just lepto cases. 25% of dengue fever
diagnosis in Southeast Asia are actually just lepto. You get in touch with a
Great Depression person, doxycycline, really cheap for pennies. They would be treated, normally
though they’re just sat down and said you have dengue,
there’s nothing we can do, which is very sad. At risk, health care workers certainly, people who work with animals
and of course animals. So those groups. This is kind of the way
I was taught in school about how to determine where the risk is for infectious disease. You look at the historical
presence of a disease, you look at laboratory data, so this comes from one
laboratory, IDEXX Laboratory, and these are spacial
clusters of a high rate of positive test results for lepto. This is for dogs, we don’t
actually have this for humans because it was a reportable
disease for a very long time, then it became non-reportable and then CDC has just made it reportable
again about three years ago so we don’t have really good human data but dogs get it the
same way humans get it, it’s very similar so I’m just
gonna use this as a proxy, the best we can do but you’ll see there’s some differences there. So anyway, that would be
a map of where’s dangerous to get, where the highest
risk of leptospirosis is based on historical reports of lepto that are confirmed by
laboratory diagnosis. We were all taught that it
has to do with rainfall, that lepto has to do with
flooding and rainfall. We did this analysis as Mike brought out. We’ve looked at 250 data
sets that were created down to one kilometer square for the U.S. so it’s soil type,
education level, tree type, and you can get great data sets now from the U.S. Geological Survey
and from the Census Bureau and just run ’em because
with those lab results come rainfall, it doesn’t
really line up with rainfall at all because rainfall and
flooding are really different so when we get flooding in
Arizona with very little rainfall it has to do with soil type, topography, land-use, is it paved and asphalt or is it trees and forests, is it rocky? So again there are a lot
of ecological factors that go along with flooding
besides rainfall patterns, so a lot of the thinking
that I was taught in school, it’s like ah, we’re
getting a lot of rain here and it’s lepto, it’s not really
true because when we do that in the lab, it’s definitely a help. Interestingly, income and education fit with positive test results so that’s weird for an infectious disease except remember we’re basing
this on laboratory diagnostics which means there has to be a laboratory, there has to be a client,
there has to be somebody to pay for it and who’s willing and generally educated
enough to take their Whiskers, their dog, to a veterinarian and the veterinarian suspects lepto, sends it to a diagnostic
lab to request that test, but somebody has to be willing to pay and finance and say yeah,
sure, I’m willing to pay $35 to have it tested versus
$5 just to treat the dog. Let’s spend extra money and test it too. So it’s interesting
thinking a medical practice in general (chuckling) and so you get a bias
of course towards that in the results, so that’s not
really helping us too much. So as I said, we did this
partial dependence plots, the modeling team jumps on this and goes, like, okay what are all those, out of those 250 possible
factors that we have data for to compare, which ones start to shake out and you get positive and
negative correlations that can hopefully if some
of you do enough statistics that you can see that
during some kind of roll up that you get these
boosted regression trees and you see that the
underlying factors once again for where they had positive tests and this is down to county level, we did every county in the United States, which is several thousand, two
to three thousand counties, that it’s got linked to evergreen cover, shrub cover, herbaceous link. On the left are PCRs, it just means that the animals are shedding
the bacteria, the spirochaete. On the right where there’s antibodies, it means they weren’t shedding a ton but they have antibodies again so it means they were probably exposed in the last year or so. These antibodies are not lifetime. So you see some of these factors, median income, we threw it in
there, it’s not very strong, but it was still there. So once again does forest
cause leptospirosis? No, but there’s something
about forest cover probably it’s a great
habitat for the reservoir, the reservoirs are rodents, raccoons, some of the common wildlife reservoirs that shed this bacteria. So that you get a proxy for that because we don’t have a gridded data set for rodents in every
county in the United States and I can’t tell you how many
species and how many rats per block by county anywhere. But we do have data sets
from satellite imagery for the habitat types, so
these will end up being proxies for once again something that
we don’t fully understand but they’re very strong correlations and then we get a risk map
like this for leptospirosis which is very different if you
remember the hot spots now. Sorry, let’s just see
’em again for our place. Again, hot spots. Here, so that was one
of the original ones. This is the historical records and where we would be taught
where to vaccinate for lepto, where to worry about and
when to send in a lab, when to treat somebody,
when to be suspicious if you’re a practitioner or clinician. This is the risk map based on where the real risk factors are, so not really, they have
not being doing a good job of paying attention to where we should be preventing this disease
either by vaccinating or catching it early and treating it because this is really
risk, this is just history. And I certainly, and even
yesterday we were hearing people talk about we still,
people still talk about using historical data or
confirmed lab results or something as a true indicator of
where the risk of disease is and we see it’s really different. And on the upper right is we did some, we got the state vaccinations of dogs. We even broke that down by counties. Once again there’s no
map of the United States related to county levels
of dog populations, it’s interesting, we don’t know that but we do know dogs are
associated with humans and so you can calculate it, you know, there’s .7 dogs per family
and then you can map out where the families are and
then you can kinda map out where dogs are so you kind of get a proxy for dog populations, and
you see that where people are vaccinating dogs, down the line, actually historically very
good form with the risk factors so you have vaccinations of
dogs where the drug companies are doing the best job
of selling vaccines. They’d like to sell ’em anywhere you can, so this is our uptake map, this is sales. It ended up being a good
sales map, not a risk map. And then they really need to
be pushing vaccinating dogs if they want to actually protect dogs in the areas where there’s higher risk and it’s interesting.
– Bill. Sorry to interrupt, I
just, I was wondering now if the vaccination map will change that the combo lepto distemper vaccine is becoming more common,
you know, used to be you’d go to the vet and
they’d have this separate lepto vaccine in the
fridge and they could say, well, you could have it
if you want it, you know, if you take your dog to the dog park a lot but now more and more you’re
seeing that lepto just already include in the distemper vaccine and I was wondering if
that was potentially an offshoot of this work that you did for Zoetis.
– It’s possible. Is that a Zoetis book?
– I’m not sure, I’d have to look that up, I’m curious now.
– Take a look. – [Attendee] Yeah, yeah. – I did note that Zoetis,
the pharmaceutical company asked us to do this
modeling and they took it and developed an app for an
iPad that they distribute to all their clients, the veterinarians, so they can actually show to their client that they live in a risk by their county and say, you know our
county’s a higher risk compared to these other counties so they’re using as a
marketing tool for vaccine. So that might also change since. But this is all published,
it’s peer reviewed and published, anybody can use that. So the second one, this is good, kind of a One Health approach. This is a vector-borne
disease, Rift Valley fever, if you’re not familiar with that, it’s kind of a complicated
vector-borne disease ’cause it requires two
different mosquitoes. One by itself doesn’t really
kick off a big epidemic or an outbreak, and they are linked to differential rainfall so one species the Aedes
mosquitoes in this case are floodwaters so they only hatch out when there’s flooding, so you
can’t just have a little rain, you have to have a lot of
rain accumulated over time for the water to rise
up to reach the level of where they lay their eggs
’cause they lay their eggs up on the high banks, not near
the water or in the water. So you have to have flooding first, so you have to have significant rainfall. And then you get the
outbreak of Culex mosquitoes. The difference is it’s in
the millions or billions, so they get large and they,
Culex spread it from animal to animal to animal so the
Aedes, when they hatch out, they keep the virus in
them, theoretically, those eggs could last 10 or 20
years with the virus inside. They hatch out, might just
infect one or two animals, and then the Culex
mosquitoes spread across to hundreds of animals and
get a massive outbreak, causes abortions and stillbirths. So in a matter of a
few, four or five days, every sheep on the farm, every cow, would abort or have a stillbirth
so you lose your calf crop so for farmers it’s pretty devastating. And then humans get it
from handling animals so that when the workers
are touching animals or there’s an abortion or
stillbirth, they touch it, or if they’re butchering
animals in the slaughterhouse. So those are at risk. So we designed a big project
to look at the ecology and those links to see if
we could better predict them because just climate rainfall predictions don’t work that well in Southern Africa. They work some places, but
not in Southern Africa. So that’s the study site,
it’s about the size of Ohio. It’s a really big study site, and we enrolled about 250 farms. Each farm is about 20,000 acres with all of their animals
so we’ve got all of them enrolled in a longitudinal
study so those are the sites. We did a really kind of
a One Health approach, so we looked at livestock and humans and wildlife and vegetation ecology. I’ll show you a few pictures of that. We have remote sensing satellite imagery and also weather stations
set up on the farms and the farmers really liked
having the weather station. It’s kind of a little perk
for them for participating in that, and we link it
so we get ground truth in the satellite data with
really on the ground data, and that’s rainfall, we
even had moisture probes. We’re doing soil science in there, integrated in soil science
and also electronic probes that link to the weather station so we can get soil moisture content. We have grassland ecologists
out there, botanists, doing surveys on vegetation
’cause a mosquito has to lay its egg some place, it doesn’t just suspend it in air. It actually attaches
it to the base of roots around the wetlands, so you get
wetland plants are involved. So you see the exact same site,
dry season and rainy season see dramatic change at
the same kind of place. It’s one of the botanists out there. So they did weekly
transects for four years on the same sites across all these farms. You got really accurate
vegetation growth data which is also matched to
NDVI satellite green data so we’ve had ground truth
and vegetation index also. We were doing mosquito larvae collection, mosquito trapping so we
had etymologists involved and then we had medical
anthropologists out there actually interviewing the
farmers and the workers and getting, asking questions
related to their behavior, their contact with the
animals, or history of it, information so we can get
some behavior pattern, behavioral risk factors identified, and then of course sampling humans for serology and testing
and sampling animals, livestock, and wildlife and
so we’ve been doing that for about four years and
now pulling that together is a really kind of a
One Health field approach with real local engagement. It’s all done by South Africans. The South African Wool Growers Association sponsors the project, the Dairy Farmers Association
sponsors the project. The farmers are excited
’cause no one’s seemed to ever care about them or their animals or the fact that they got sick, and so it’s really nice
on the ground project designed, managed, run by them and our role is to kind of
develop the study design scientific design and
find them, find the money, which was funded by our
U.S. Defense Department, we have to give thanks to the Defense Production
agency seeing this. Early findings already we’re seeing about 10%, 9% in farm workers and average farm workers
we’re seeing that even though farmers say their
vaccinated, their livestock, a lot of those vaccines are crap or they don’t use them
well or they out of date or they’re just no good. So people have been
critical of the vaccines, the farmers particularly
said the vaccines are no good so then they stop using them. They actually, we actually
tested the vaccines and when used appropriately
they work really well. So it’s not really a
problem with the vaccines, vaccination programs,
it’s implementing vaccine is the real cap there. – Bill?
– Yes! – [Attendee] I think that
Rift is really being, at least Rift as we know it today, a mandated disease, a
result of the introduction of European livestock and large numbers of European livestock into Africa, and if you look back for
outbreaks of, you know, some of the British colonial publications right around 1910, there’s
a first description of an unexplained die-off of lambs which as, you know, when
this is just getting going so the problem is, you know,
the virus has been there. I mean, it’s in that environment. So if you remove European livestock what do you think the picture
of Rift would look like? – We see it in wildlife but
not much on the clinical side. So I think African wildlife probably evolved to the, adapted to that disease thousands of years ago. There is some reports on springbok which are an African
antelope that in South Africa is raised in huge numbers on farms and some of them have reported this so that could be a management issue about crowding together
thousands of springbok in a confined area, making them, forcing them to be exposed to the mosquito that also would be unnatural. But I agree with, I think
the livestock issue. – [Attendee] Essentially
it’s an experiment in introducing a, what, I’m likening them to–
– Susceptible host. – [Attendee] Yeah, now to monitor, you’re putting, no. It varies in, oh, and you’re
putting the animals there that are susceptible–
– Yeah. – [Attendee] Trying to find
out if there’s a virus. It’s sort of like the
way Zika was discovered. Put some animals in places– – Yes.
– See what happens, and in the case of Rift what
you get are huge die-offs. – It’s true.
– You might get it in livestock. – I guess that’s basically–
– Sentinel animals. – Sentinels, yeah. I guess that’s really what
we’re doing to the planet with people, and-
– Yeah. (group laughing)
Put them there and see what happens.
– See what happens. Just put some in the
forests, see what they get. Yes, unintentionally we’re
doing that. (chuckles) It’s true, and with livestock
by putting them everywhere. – [Attendee] Some person
had mentioned why DTRA? – Why DTRA, oh, Rift Valley fever is a special, is classified
as a special agent and it’s weaponizable. So it’s on the list of special agents– (coughing drowns out speaker) Thank you for reminding me. It’s not why we’re working with them but that is why they’re involved. – [Attendee] Why they’re funding it. – That’s why they’re
funding it, that’s true. It’s not a good disease, there’s no cure. And when it gets into people it kills them so it’s a hemorrhagic fever
and not a pleasant thing. I do know some people who have survived, been infected and survived but it was not a fun time for them. And actually, with defense when you think about diseases and armed forces is actually if they’re sick it’s more of a problem than if they die. So it takes several people
to take care of a sick person so wounded soldiers, sick
soldiers is a bigger hardship on armed forces than just dead soldiers. Let them lay there for
awhile, do something else. Okay, so I just wanna finish up. I’ll do this really quickly
with some just think about some of you that are engaged in policy, so okay, where’s One Health fit in? There’s sustainable development goals. We feel like health kind of runs across, it’s not just limited to the health code. I don’t want to spend
a lot of time on that. With climate change,
we’re always wondering about One Health and where does it fit in. So we did this little
paper but we kind of tried to look at there’s these
fact contributing sectors to climate change that
we start to understand and then there’s these
kind of intermediate things and then we’re trying to
look at health outcomes here down at the bottom,
what and where they could be. So we’re trying to
figure out our thinking, like what can we measure or what can we do if we’re gonna have some effects. What are the metrics gonna
be and where should we be focusing on some of these issues and how to explain the
climate change meanings as it flows through this system. You know, is it about a
coal fired power plant or is it about malnutrition and so how those linkages tie together and then how to label it. Somebody here earlier
mentioned on the economic side. We know that these emerging
infectious diseases cost a lot of money. Most of that money has nothing
to do with medical cost. So if you break that
down, you can see kind of the percentage of a cost, let’s
say SARS with $30 billion. The percentage in medical
costs are really low so you typically do that. Someone will come out
with a number and say well, it costs a billion dollars
to treat all these people but the real cost, economic
cost, for $30 billion ’cause of the impact on global trade. So then a lot of stakeholders
that typically aren’t at the table when we’re
having a conversation about global health or
global health security while the biggest stakeholders
aren’t even invited ’cause we tend to think
it’s a health discussion and not a societal discussion. They’re really trying to do
more of this kind of work to show that it’s not just,
it’s not a medical industry. Not a medical conversation,
it’s a societal conversation when we’re talking about One Health. Fortunately that word health is in there so it really confuses people ’cause it’s not a health discussion, it is a health discussion but it’s not a medical discussion. We’ve been doing some
work with The World Bank to try and help for them,
the bank to engage in this, and how to implement
these kind of One Health cross disciplinary efforts
into the bank’s funding mechanisms, their loan programs
or their grant programs so this has been moving
forward, and as part of this they’re gonna start using
some One Health assessments in countries to see how
they qualify for loans and what kind of funding
they can be getting so really kind of using as a carrot. And we’ve kind of done some mapping, I know some of you kind of do this too but how does this all fit together and we try to break out these parts ’cause I think kinda how
they different efforts and programs are linked is
confusing to a lot of people unless you’re really fluent with them so you know, you have
people throw around terms that there are National Health regulations and then somebody else talks about conventional biodiversity and then there’s the Codex Alimentarius, which are the rules on moving food around, healthy food around the world and they fall into kind
of different groups. So you’ve got regulatory efforts, we have assessment tools,
we have planning tools about action packages and planning tools and I think wherever
I go around the world, most people on the ground
are really confused about how any of this works. So I’m really trying to
come up with a map of that and help people, and these are all linked. This is from The World Bank document and you can kind of zoom in. This is trying to say that
these different frameworks have to somehow work
together and that we see that kind of a One Health theme helps
to kind of push that agenda and engage some of the non-medical groups. This is Convention on Biodiversity. We’re working with them to link them with the World Health Organization, and look at biodiversity in human health and come out with some efforts for them and they’ve actually passed a resolution that the CBD Convention on Biodiversity actually indicating biodiversity
linkages with health. A number of countries are conventional. The U.S. is not a signatory
of Convention on Biodiversity. North Korea’s not, but
every other country is besides North Korea. It’s nice to be really
paired up with North Korea in a strong way that way and we’re working on those relationships even today. (group laughing) You may be familiar with Sendai Framework. This is about disaster risk reduction. We get excited about it because
of this word, reduction. It’s not the Sendai Framework which is actually risk response. It’s disaster risk
reduction and supposedly countries have signed
onto this and committed to making efforts to reduce the risk of disasters and we’ve
been working with them to include health as actually a disaster. So I’ll just skip to that, so we kind of took some of the same stuff I talked to you today, went
to the Convention of Parties. There were 3,000 people there
and said that health issues, diseases, are not just
a result of disasters, they are disasters in many
cases and the risk reduction framework, what you’re implementing, you need to also be putting in things that reduce the risk of health disasters. Not just responding to earthquakes but actually doing something proactively. So we’ll see that that goes. – And I heard that that
comp was in Cancun. – That was in Cancun,
was a beautiful location. – Hard work.
– Hard work, they sent me there for
two days and never made it to the beach unfortunately. And the GSHA, I don’t want
to spend time on that. Everybody’s familiar with GHSA? Clearly there’s One Health
implications in there because it’s about linking human health, animal health to environment health. That seems to be kind of,
people tend to forget that. They also tend to forget
the word security in there, so we see WHO very engaged in global health security agenda as long as the money’s involved and the other five
sectors kind of still want to participate but it’s been difficult, mostly because of the funding sources and we’ve gone with it, its conversations. We were just meeting yesterday, mentioned National
Blueprint for Biodefense, the National Strategy for
Biodefense just came out a couple of weeks ago
from the Executive Office in The White House, there’s
some good strong components in there about One Health
too, about linking health in animals and people, even
some issues in the environment was interesting, that was included but a lot of us have all been involved, have been pushing that. And then I just wanted to end by saying that everything can’t be One Health. That there are reasons why
and there’s some benefits of not trying to do everything
together collaboratively. The donors like siloed
programs that they can measure really easily, sometimes
they’re just more efficient, faster and more effective. So don’t get all
frustrated if everything’s in the One Health approach, there is room in the world for silos. They are really good for storing corn, okay.
(group laughing) If you want some vertical things, it’s what they were actually designed for and they’re really good
and so there is a utility in the silo approaches as well. I’ll close with that, thank you.
(group applauding)