>> Announcer: From the Library of Congress, in Washington, DC.
[ No audio ]   >> Jennifer Habster: Good afternoon.
I'm Jennifer Habster, a research specialist in the Science,
Technology and Business Division at the Library of Congress.
I'd like to welcome you to today's program:
Predicting Disease Outbreaks from Space,
in which we'll learn how Earth satellite data can be used
to find links among weather, disease and famine.
This program is the third in a series of programs in 2011,
presented through partnership between our division
and the NASA Goddard Space Flight Center.
This is our fifth year of presenting programs with Goddard.
Our speaker today, Dr. Assaf Anyamba, is a geographer
and remote sensing scientist
at the Goddard Earth Sciences Technology Center at the University
Someone didn't silence their digital device [laughter].
He received a bachelor's degree in geography and economics
from Kenyatta University, in Nairobi, Kenya; a Master's degree
in geography from Ohio University, in Athens; and a doctorate
in geography, with a focus on remote sensing
of El Nino-Southern Oscillation, from Clark University,
in Worcester, Massachusetts
[back-and-forth on correct pronunciation of Worcester]
That's okay.
Dr. Anyamba applies his remote sensing expertise
to benefit global public health.
His collaboration with earth science and public health colleagues
from NASA, the Department of Defense
and the USDA has helped develop an early warning system
to prevent vector-borne diseases in various regions of Africa.
Having an early warning system in place can help prevent
and minimize the impacts of these outbreaks.
One example of this work is a prediction of outbreaks
of Rift Valley fever, which is a deadly disease transmitted
by mosquitoes.
NASA has graciously provided literature and handouts
on this research out in the lobby.
So if you're interested, please stop by and pick up some.
If you want more information, Jeanie is here to help you with that.
So now please join me in welcoming Dr. Assaf Anyamba.
[ Applause ]
>> Dr. Assaf Anyamba: Thank you, everyone.
Good morning.
It's a pleasure to have the opportunity to come
and talk to you folks here.
It is not many times that people at NASA come to Library of Congress;
but through this collaboration, we are able to share some
of the interesting stuff we are doing at NASA Goddard.
So my presentation today is on predicting disease outbreaks.
And basically, what I'm trying to do in this presentation,
I'm trying to tie together what we measure from satellites,
in terms of the vegetation and climate or rainfall,
and associate that with the margins of vectors of disease,
like mosquitoes, and how that relates to human habitation
and livestock, in terms of disease.
So I have a little bit of an outline of my presentation here.
I'm not -- I'm not promising to keep track of it, but generally --
generally, this flow represents what I'm going to talk about.
In doing science, basically, it is not done out of nowhere.
There are personalities, and this work would not have been possible
if I had not met Ken Linthicum -- then at DOD, now at USDA --
if I had not met him about 12 years ago.
So this is a picture that we took in Rift Valley of Kenya,
which is the origins -- or the first detected --
the first detection of the Rift Valley fever.
However, that was not possible --   would not have been possible
without this other guy on top here, Jim Tucker.
When I came to Goddard as a post-doc to work for him,
it happened that Ken Linthicum was also back in the U.S.
from Southeast Asia, after a long time.
And basically, our meeting together led to this work.
So, basically, this work is a contribution for --
from the three people, and other work that other people have done,
especially historically, on Rift Valley fever.
The representation
of El Nino-Southern Oscillation here is basically what I used
to do in my background.
But since meeting Ken Linthicum, we managed to impose on it outbreaks
of Rift Valley fever through time.
So basically, our meeting 12 years ago basically lead
to understanding the dynamics, in term of climate
and the vectors that spread the disease.
In general, most of these diseases, like Rift Valley fever,
Ebola or dengue or Chikungunya, and other vector-borne diseases,
they occur discontinuously in time.
So you -- in order to do this work, you actually have
to understand the dynamics in space, both of the climate and the ecology.
And to do that, the technology that is only currently available
for observation is remote sensing, because it offers the ability
to do surveillance over space and time,
over large areas throughout time.
And in trying to use the data, we can be able
to either monitor the conditions;
and after understanding the conditions, we can do prediction
and assessment of disease outbreaks.   And the observation --
the observation you can make from remote sensing,
like shown in this picture here, are much more valuable --
in most cases -- for observation,
instead of doing ground surveillance,
because you cannot carry out surveillance over large areas.
So having a platform in space is very useful.
And this is more so important, to do global observations,
because at this time in human history, there is a lot of --
there is a lot of travel.
For example -- this is not very clear on this --
you can see this is air traffic.
And somebody infected with a disease --
say in China, or somewhere in Africa or South America,
or even from North America -- going somewhere else, to another country,
can be able to transport that disease.
And if it's communicable, it will be able to affect many people.
So this increasing -- the role of increasing travel,
in terms of pathogens transport, is important.
Also, if you look at the shipping traffic all over the world,
you can see that over time, since the early 1900s,
shipping traffic has increased.
For example, there has been introduction
of particular mosquitoes in California that came
from Southeast Asia, and it was transported through boat tires.
So you can just see that through transport networks,
diseases can spread, or the vectors
of diseases can spread to new locations.
Now, if you look at a map of the burden of vector-borne diseases,
you can see Africa is the most affected continent.
However, because of the reason that I just gave,
that is only a model of geography and time.
We have had situations like, for example, in 2004,
there was an outbreak of a disease called Chikungunya
in coastal East Africa and in the Indian Ocean islands.
And within a couple months,
the disease was also detected in France and in Italy.
And this was simply because of tourism.
A number of tourists traveling from France and Italy went
to Comoros and Seychelles.
Also some people from Colorado were traveling to the region.
They got infected.
So basically, they became a host of the pathogen.
And going back home, there were already vectors.
There were already mosquitoes.
So they managed to bite them and spread the disease to other people.
Now, one major factor, in terms of what I'm going to present,
and maybe you have heard of it, casually,
is something called El Nino-Southern Oscillation.
This is an internal mechanism of the climate.
And it basically is shown as the increasing surface temperatures
in the equatorial and eastern Pacific.
That is usually accompanied, sometimes, by an increase
in sea surface temperatures in the Indian Ocean.
The cycle of this phenomenon is about three
to seven years, so the repeat cycle.
There is also an opposite phase of El Nino, which is La Nina.
So basically is one cycle.
In the La Nina phase, there is a cooling.
There is a cooling of these waters, which usually will result in drought
in this region of the globe, and in these areas.
For example, basically, what we are going on right now --
what you are observing in the tropics, that I'm going
to show later on, is basically a result of that.
So we make observations of sea surface temperature globally,
and we are able to monitor what is going on.
And then also we are making observations --
proxy observations -- of cloudiness.
So you can see here, there's a good convergence between areas
that are cloudy and therefore are precipitating,
and higher-than-normal sea surface temperatures in eastern
and equatorial eastern Pacific.
And in Southeast Asia, you are seeing a dry belt;
and in East Africa you are seeing a wet belt.
So there is a convergence of this system around the world.
Basically, what usually this leads
to is this idea of droughts and floods.
So you can see, for example, here, East Africa,
it was receiving a lot of rainfall.   Southern Africa is dry.
Northeastern -- no, northern South America is wet; now it's dry.
Southern U.S. tends to be wet during some periods,
but right now it is very dry.
This idea of things all over the world being connected
to what is going on somewhere else is known as teleconnections.
This is simply because of the sheer size of the Pacific Ocean.
Any changes in the Pacific Ocean are going to have downstream impacts
on circulation and precipitation elsewhere in the world.
So, for example, you can see here
in East Africa you have had a drought going
on right now, and it's still going on.
So many animals and many people are suffering
from hunger, and animals are dying.
In South Africa, on the other hand, you have seen a lot of --
you have seen a lot of flood happening
in South Africa during the last four months.
Also, in California sometimes, during El Ninos, you tend to have --
you tend to have floods,
and I'm sure that is something you can relate on.
When it comes to El Nino, which is the opposite of La Nina, basically,
what you can see here is Texas is drying, and many farmers and herders
and ranchers actually have [inaudible] the forage
for the animals.
And in Australia, during La Nina events,
you tend to have forest fires.   So if you look at it,
these phenomena basically affects everything that we do,
and we are not in control of the weather, for the most part.
So we have to learn how to manage the weather.
The picture of the current situation is basically shown
in this graphic here.
You know that there has been a lot of rain in northwestern
and northern U.S. There is drought in Texas.
Yet there is flooding in Mississippi.
That is simply because of these rains
in northern U.S. are draining off into the area of Mississippi.
Western Europe is very dry right now.
Central Russia is dry.   East Africa is dry.
South Africa is wet.   Australia has been very wet.
So this is during the La Nina phase.
During when the climate system was switched to an El Nino event,
you are going to see, more or less, the opposite in these regions.
Now, because this is a coupling of both rainfall and temperature,
it is going to affect the ecosystems,
and the ecosystems mainly in terms
of water available -- and water available.
And this, in fact, has an effect on vector populations.
So from the publication we did several years ago,
we documented a number of diseases that have occurred,
when you have these kind of anomalies.
For example, in East Africa, we usually have outbreaks
of Rift Valley fever, an increase in malaria cases, cholera.
And in Southeast Asia you have dengue and respiratory illnesses
that are related to forest fires.
In India and Bangladesh, you tend to have an increase in cholera cases,
especially along coastal regions.   In Brazil, dengue increases.
In Columbia and Venezuela, there is an increase in malaria.
And in Southeastern U.S., you have outbreaks
of a disease known as hantavirus.
Now, the contribution of remote sensing.
Because these things are happening through time and they are changing,
through -- throughout the last 30 years NASA --
at NASA we have managed to compile information
of the entire global biosphere representing vegetation.
And the vegetation data is known
as a Normalized Difference Vegetation Index.
The importance of that is that in most parts of the world,
you don't have very good observations of meteorology; say,
rainfall, from ground stations.
So we can use this dataset as a proxy,
to represent what is going on in the environment.
So you can see, in this particular graphic,
there is a very good relationship between these data
and rain formation on the ground.
So, through time, as I had mentioned,
there are many different instruments that are monitoring the environment,
and we are pushing this forward.
Right now, we have about 30 years of data on the global biosphere.
This is something that had not been available for us --
both scientists and decision-makers --
and we have both the vegetation index and land surface temperature,
which is from another instrument called MODIS, that has been there
for [inaudible] the last about ten years.
Now, the importance of having these data is not just in having the data.
We have to analyze it and see what we can tease out.
So, what we do is something called anomaly analysis.
Basically, we are going to -- we look to see what is different
about today that is not typical, that is not common.
For example, right now we are having a very wet spring in this area.
If you look at the data right now, you will just think is wet.
But if you compare it to the historical information,
you can see things like this.
So, for example, you can see episodes where there is a lot
of greening, and during those time periods, temperatures are cooler,
like you are seeing right now.
That one -- that particular situation has its consequences
for vectors.
Or sometimes you are going to get periods that are dry,
and basically that has consequences for vectors.
Okay?
So, this -- putting together all this information,
we are able to then tie it
on this particular disease called Rift Valley fever.
Rift Valley fever is an exotic disease.
It is known as a zoonosis, because it is mainly a disease
of livestock and some wild animals.
It was first described in Kenya in 1932, following an episodic of sheep
in the area of Naivasha in Kenya.
Naivasha is located within the Rift Valley,
and this is a detail of it from satellite.
So in an area north of Lake Naivasha, which is here,
there was an episodic, or a mass death
of sheep population and abortions.
So this person, Daubney, is the one who first documented it.
However, when we read through literature and records,
it seems that the disease has always been there with human beings.
There are even indications in the Bible that this is something
that is -- that has been there for a very long time.
It is called Rift Valley fever, because the origins are
within the rift system of East Africa --
which actually runs all the way from Lebanon to southern Africa.
The manifestations?   Usually, it results in bleeding.
You will have flu-like symptoms.
For a number of people, it may lead to blindness or encephalitis,
which is damage of the brain.
Up to now, there is no licensed vaccine
for human beings for the disease.
There is only vaccination available for livestock.
The mortality rate is estimated to be at one percent
of human beings who are infected.
However, the episode we saw in Sudan in 2007, there was a mortality
of about 40% in human beings.
In livestock, especially sheep, you have a mortality rate
of almost a hundred percent.
Sheep are the most vulnerable of the livestock populations.
And because of this, it is actually a disease of concern
for Homeland Security, Department of Agriculture, Department of Defense,
the World Health Organization, [inaudible] organization.
And also, more important, in terms of defense, is because in the '70s
and the '60s, the Soviets tried to culture it as a bioterrorism weapon.
So, looking at the geography of the outbreaks, we have had outbreaks
in West Africa in the area of Senegal; in Egypt in 1977;
also in Saudi Arabia, there was an outbreak there in 2000;
and historically in East Africa.
And all of those areas present different ecosystems.
For example, in East Africa is mainly in savanna areas,
which occasionally flood.
This is one area where we work in Kenya,
which occasionally will flood during El Nino events.
And these depressions are known as dambos.
In West Africa, it is -- it is mainly on the river, so --
on the floodplain on the river.
So this shows you that it is highly adaptable.
And in Saudi Arabia -- Saudi Arabia, the disease also occurred
on the coastal flood plain.
So it seems, from looking at the disease historically,
it is governed by wet and dry cycles.
And this is simply because vectors -- vectors of disease,
like mosquitoes, or like vectors of farming, like locusts,
tend to follow the flush of green vegetation.
So by monitoring vegetation we can be able to capture the dynamics
of the disease through space and time.
Now, in terms of actually doing predictions, we have to be able
to understand what goes on with the population of the vectors.
And this -- this slide here actually shows you Ken Linthicum
in 1985 doing an experiment on a dambo in Kenya.
It is kind of funny, because at that time I was in high school,
and he used to drive around.
He was driving around in an area called Kwale.
And we usually joked that he could have run over me, because I was one
of those young men that were running around, crossing the roads,
when he was driving around [laughter].
So this work was very important,
and it goes to us pointing why just a small experiment can yield very
important information.
And it also shows the important of we as a country investing
in research and development.
From the flooding of this habitat, they managed to find
out the dynamics of the mosquito population after a flood event.
And simply -- this can be shown here --
but during the first 20 days of the flooding this dambo,
there is an emergence of particular mosquito vectors.
Okay? And then, as they continued flooding it over time --
say, over 30 days -- there was also emergence
of another vector of mosquitoes.
So flooding this dambo habitat continuously
over time generated large numbers of mosquitoes.
And basically, that is what happens,
when you have flood events or rainfall.
You are going to have a situation whereby many vectors are emerging.
Some are dying.
For example, these that died first are going
to transmit the disease to livestock.
And then the second generation that emerge are very efficient
at transmitting the disease
from livestock population to human beings.
So the foundation of the work is basically this small piece of work,
So this gives you some views of the environments
where the disease is common.   This is the area in Rift Valley.
And these are the kind of dambos you find
on the high plateau of East Africa.   And you can also find similar --
similar dambo depressions in game parks.
For example, this is a game park that is just outside of the capital
of Kenya, Nairobi, so very near a large population
of people and going to the park.
And this graphic here is showing, for example, dambos in South Africa.
The transmission is shown here by basically biting mosquito.
This is an experiment [inaudible] an experiment we are doing
on Ken Linthicum one time, when we visited Kenya.
We are -- we were checking
if he produce a good blood meal for mosquitoes.
The good thing about him is he is vaccinated.
The Army has been working on a vaccination for military personnel,
and this is not available for public use.
Also, for people who have animals --
for example, sheep and goats -- are very vulnerable.
This is an area in Kenya called Baringo.
We visited it after an outbreak.
Although there is no documentation of abortion in human beings,
this particular woman had been infected by the disease,
and two weeks prior to our arrival, she had just had an abortion
that was due to Rift Valley fever.
Also, the way in which people process their meats,
or slaughter their meat, is very important.
This is a picture taken from Madagascar.
And basically, this -- they kill livestock at home.
And if they don't know the disease is there, are handling blood
or eating not-well-cooked meat,
can transmit the disease to human beings.
And here is one of my colleagues, Ed Park [assumed spelling].
We were traveling in Kenya.
And you will often find marketing of products from animals;
for example, hides and skin.
If they are still wet, and if you buy them,
you can be infected by the disease.
So there are many ways the disease can be transmitted.
I have shown this as basically showing the historical outbreaks
of the disease, in relation to climate variability.
Now, over the last year -- the last four years, we have had outbreaks
that have happened in Sudan, in Kenya and in South Africa.
And I'm going to illustrate how the system we developed was used
to predict these areas.
So, in a simple form, we usually have a, say,
monthly measurement of vegetation.
And then we get -- we get the mean, which is the longtime measurement.
But this information by itself is not important.
The moment you subtract the longtime mean from the measurement --
from the current measurement, you get something like this.
This is called an anomaly.
And all of a sudden you are able to detect where things are different.
So this information is fed out in our model, and we run it.
And using something we call persistence --
or persistence of wetness or greenness --
we are able to determine by thresholding
which areas meet that criteria.   So we generate maps like this.
So this is West Africa.   This is for Sudan, Madagascar.
This is for South Africa.
Every month these maps indicate what areas -- say, health workers,
people working with public health of different countries; or,
for example, in areas like Kenya and Egypt,
where the U.S. has laboratory facilities by CDC or Department
of Defense -- they can help the nation to go out in the field,
do early surveillance and be able to do pre-treatment.
So we generate that information every month.
It goes out.
And the manner in which we run the model is what is shown here --
every three months.
Using the information that I showed on vector populations,
we use information coming into the model every three months.
It's fed into the model.
And basically, is like a moving window, so you will see
that accumulating over time.
And we are able to detect areas of increasing risk
or decreasing risk over time.
Okay? So that information is then sent out.
For example, for Kenya, we sent out the information.
The response was good,
but my expectations is they did not take it seriously the first time,
so there were outbreaks of the disease in some areas.
So these dots that I've shown here in blue are areas
where the disease occurred.
So we had a match of about 70% of areas
to map -- we mapped to be at risk.
At the end of the season, we can do an assessment,
where we analyze how many months any given area
on the ground was at risk.
So that information is also given to them.
This is not showing up.   I'm sorry about that.
But what I'm showing here is that having the information --
or producing information -- is not good enough.
The nation has to be --
the information has to go into people who can make a decision.
So we produce the information at NASA here.
The information is passed on the --
onto the global Emerging Infectious Diseases,
who have been the main sponsor of the work.
That is an operation of the Department of Defense.
So that information is then distributed,
first to DOD facilities --
laboratory facilities around the world.
It is also given to World Health Organization.
World Health Organization deals with human health,
so they handle that portion.   Also, it is given to FAO.
FAO is the Food and Agricultural Organization,
and their [inaudible] is on livestock health.
It is also -- is taken to other regions.
For example, through this chain, it is then passed to organizations
in Kenya, say through KEMRI -- KEMRI is the CDC of Kenya --
to various regions, like Middle East and areas.
So we basically produce early warnings during times
that we think are high risk, and they are monthly.
For example, there are published,
like this publication here is called EMPRES --
Emergency Prevention System for Transboundary Diseases.
This is on FAO website.
So we provide information to them, and they publish it on the website.
And through that channel, the [inaudible] is routed to a number
of people, or the region offices all over the world.
So during the 2006-2007 outbreak, there were a number of actions
that were taken that were based on our early warning.
They took time to declare the disease as going on in December,
although we provided information much earlier; but this is simply
because of the bureaucracy of any government system.
I'm sure you all understand that [laughter].
So they were hedging their bets, to see whether we were right or not.
Remember that this is something that had not done --
been done before, anywhere else.   So the first case was in December.
We also managed to publicize this,
by publishing a Web -- a paper on the Web.
And based on that warning, there was mobilization of response team
from various organization -- WHO, FAO and Government of Kenya.
One of the things that they did early on was to ban the slaughter
of livestock, especially because the impact of the disease was mainly
in the eastern portion of the country.
The eastern portion of the country is mainly inhabited by people
who follow the Muslim faith;
and during December is actually a very --
a very big holiday for the Muslims, called Ashura,
and also the beginning of the Muslim new year.
So banning the slaughter of livestock was important
in preventing the spread of the disease.
Also, various NGOs and the WHO distributed mosquito nets,
so this helps prevent mosquito bites, especially for people
in these rural areas who are --
who don't have the capacity to buy mosquito nets.
They also banned the consumption of beef and goat meats
over most of the country.
Obviously, this has economic impacts,
because there was an increase in the price of beans,
an increase in the price of chicken,
for various commodities over the country.
However, what we can say is that based on this early warning,
the response was one and a half months earlier
than the last episode in 1998.
So for East Africa -- which is Kenya, Somalia, Tanzania --
this is the amount of time we bought,
using this particular system.
It is even better when you look at Sudan and South Africa.
And that is shown here.
In these graphics here, you can see that for Kenya,
the warning was actually three to four months; but the response --
compared to '98 -- was one and a half months earlier.
For Sudan, the early warning was about six to -- six months.
The outbreak happened towards the end of the year.
And for southern Africa is actually a longer time.
And because of what I said about the mechanics and the circular nature
of the climate system, actually, in South Africa, we think that,
technically, we can give an early warning of one and a half years --
just on the basis of understanding the cycles of El Nino and La Nina.
But what is more important is that we have come up with a strategy
that now FAO and -- FAO and WHO are planning to implement.
And simply, this shows that if you have an early warning,
the first thing you have to do is vector control.
So they should go out and control areas that are flooded,
where mosquitoes are likely to emerge.
And also, vaccination of animals has to happen early enough,
because if you vaccinate during the outbreak,
you are likely to spread the disease.
Okay? So that is the strategy that is being worked on now.
However, there have been fatalities, simply because you cannot be able
to prevent the disease once it starts happening; you cannot be able
to prevent it everywhere.
So these numbers show for you the numbers of people
that were affected in Kenya.
In Sudan was very high, compared to history.
In Kenya, during the -- during the '98 event,
the population affected was in hundreds
of thousands; like, more than 200,000.
So you can see, having this system in place,
you manage to reduce the impact.
Now, this also has consequences for trade.
And this is not shown, but this is what I call
where is the beef [laughter]?   Where is the beef?
Because of not controlling the disease, in 2008,
Kenya lost its quota for beef imports to Europe,
and this was given to Botswana.   So there are losers and winners.
The important thing is countries have
to make an effort to control the disease.
From this outbreak in 2006-2007,
they estimated losing trade is $65 million for the East Africa region.
The potential impacts on the U.S., if the disease were ever
to come here, was estimated by a study that was carried out by USDA
to be about $5.7 billion.   If the disease came here,
that is actually what we are -- we shall be facing.
And this is mainly because of several factors.
Hype: Because of the press.
And also, once the disease is here and it occurs, OIE --
OIE is the organization for animal health -- is going to ban --
is going to ban exports of U.S. beef for a period of four years,
until we can prove that for six months we are disease free.
So you can see from this why, as a country, we have to be concerned;
why Department of Defense, the Department
of Agriculture are paying attention to this to this --
to this particular disease.
[ Projector frozen, inaudible dialog while working ]
>> Dr. Assaf Anyamba: So we can continue now.
Okay. So this -- these particular graphics here show you --
show you the chain of livestock trade, for example,
from Somalia, Ethiopia, Kenya.
Most of the exports go to the Middle East.
So, when you have a Rift Valley fever outbreak,
you are basically cutting off that particular trade.
In the year 2001, USAID managed to set
up a registered trading commission, and this commission is basically
to provide information on trade to these regions,
so that if countries are affected, they are able to regulate trade
and prevent the spread of the disease.
And you may also be wondering, so why is this important?
This is a region of importance to national security --
Somalia, Yemen, I'm sure you know what is going on.
So, for example, by doing this work
and stabilizing these particular regions,
especially the people are poor, we are able to prevent, say,
them being convinced by elements that are not favorable
to the United States taking root in those countries.
Now, this work would not be possible without having a global network.
So, for example, this basically shows you laboratory facilities
that are operated by DOD oversees.
And these laboratory facilities are very important, for example,
in doing validation; in being able to do --
to go out and carry out surveillance;
in being able to help different countries --
for example, the unit in Kenya, the unit in Egypt are very helpful
to these particular countries.
So this is a network that has to be maintained through time.
Now, the importance of this, looking forward, is that we are going
to be living in a world of extremes.
Either there are going -- there is going to be areas or episodes
of increasing rainfall and flooding, or temperature increasing.
And both extremes -- either very high temperatures
or very high rainfall -- are very conducive to the propagation
of vector-borne diseases.   Finally, this work, you can see it
as indicating successful inter-agency collaboration.
Basically, this work is telling you that government works,
if you didn't know [laughter]; because most people are very easy
to say, oh, government is not working.
This is a demonstration of the fact that government works,
and is not only working in one institution,
but actually involving many government agencies.
The second thing is it is promoting --
it is promoting trust in U.S. science and technology.
This work has not been done elsewhere.
This is the first time it was ever done --
as much I know -- for any disease.
Also, the important thing, since we are working with Department
of Defense, this is a mechanism in which we can be able
to achieve force health protection.
Basically, if there are troops that are going to be deployed
in a particular area that is going to be high risk, they can be advised
to take preventive measures.
The other thing is this information is usually passed
to CDC and State Department.
CDC usually will send out warnings, or State Department will send
out alerts for specific countries, if you are going there.
It is not only about terrorism.
It is also if you were -- if there is a disease outbreak,
they are going to inform the public.
It also provides good opportunity for State Department
to advertise what the country is doing, and USAID.
The other thing is international agencies are now relying
on what we are producing to do this work; and obviously that is a plus,
in terms of the U.S. image around the world.
The other thing is training and capacity-building.
Doing such interesting work attracts students to the U.S.,
and you're looking at one of them [laughter].
And also, as I said, in this world we are living in that is going to be
of extremes, this is an example of actually being able
to start studying how different diseases --
especially vector-borne diseases -- are going to behave,
in an era in which we are experiencing extreme events.
And lastly, it is for homeland protection.
And to paraphrase President George Bush,
we have to fight the mosquitoes there,
before we fight them here [laughter].
So -- and this is very important.
First, because if we know how mosquitoes behave
in endemic countries, we are able to understand the dynamics,
in case they are ever introduced here -- accidentally or intentional.
This has happened before.
We now have West Nile -- West Nile fever here.
West Nile was not endemic to the U.S. It was endemic to Sudan
and some of the Middle Eastern countries.
So, as I showed earlier on, it only takes one person to get on a flight,
if they are infected, and for a mosquito to bite
that person and spread the disease.
Right now, we currently have in the U.S. vectors -- or mosquitoes --
that can transmit Rift Valley fever.
So, you know, there -- you can basically say that the plate is hot
for the disease, in case it ever got here.
So, some of this work that we are doing is actually beneficial
to a special -- a special organization, or a special grouping
of inter-agencies in the United States is known
as the Rift Valley Field Working Group.
So this group not only is preparing plans,
in case the disease is introduced here, but also being able
to develop the resources to study it overseas and enable those people
over there to be able to manage it.
I don't know if I should show the slides,
because of the problem we are having with the --
if I should show the movie or not.   [ Inaudible discussion ]
>> So let me finish up, and then,
if the movie doesn't load up, it's no problem.
Obviously, this -- this work cannot be done alone.
I want to thank my team, especially Ken Linthicum,
and the other analysts we --
I work with at Goddard Space Flight Center are shown here.
And more importantly, we have received funding from the Department
of Defense, and this is very valuable in doing this work;
Department of Agriculture; NASA has also funded this work;
and the collaboration of different international organizations,
like WHO, FAO and CDC in Kenya have been very valuable.
And finally, I want to thank my sister -- she's here --
she's called Ebi [spelled phonetically].
She actually is the one who inspired me to come and study in the U.S.
and inspired me to do what I'm doing right now.
So I wanted to say thank you -- thank you, to her.
[Applause] Okay.
>> This has been a presentation of the Library of Congress.