Drivers of Infectious Diseases: Connections Matter

Coordinator:
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Nikki Grimsley:    
Thank you very much. Good afternoon everyone, welcome and thank you for standing by. All participants are in a listen-only mode until the question-and-answer session of today’s webinar. Today’s call is being recorded. If you have any objections, please disconnect at this time.

I am Nikki Grimsley with the Clinician Outreach and Communication Activity or COCA, with the Division of Emergency Operations at the Centers for Disease Control and Prevention. We are delighted to welcome you to today’s webinar, Drivers of Infectious Diseases: Connections Matter.

COCA is excited to offer this special call series with our partner, the American College of Veterinary Preventive Medicine. We are pleased to have with us today Dr. William Karesh.

CDC, our planners, presenters, and their spouses and partners wish to disclose they have no financial interest or other relationship with the manufacturers of commercial products, suppliers of commercial services or commercial supporters. Planners have reviewed content to ensure there is no bias. This presentation will not include any discussion of the unlabeled use of a product or product under investigational use.

At the conclusion of today’s session, you will be able to identify key drivers of zoonotic infectious disease emergence, describe approaches to identifying risk factors for zoonotic diseases, and describe the difference between disease occurrence mapping and correlation-based disease risk mapping.

At the end of the presentation, you will have the opportunity to ask the presenter questions. You may press “*-1” on the phone to put yourself in the queue for questions. Or you may submit questions to the webinar system at any time during the presentation by selecting the “Q and A” tab at the top of the webinar screen and typing in your question.

Our presenter today is Dr. William Karesh. Dr. Karesh is the Executive Vice President for Health and Policy for EcoHealth Alliance. He is also the President of the OIE Working Group on Wildlife, and he chairs the International Union for the Conservation of Nature Species Survival Commission, Wildlife Health Specialist Group, which is a network of hundreds of wildlife and health experts around the world. He serves as the inter-project liaison for the USAID Emerging Pandemic Threat, PREDICT-2 program, and is a member of the World Health Organization’s International Health Regulations roster of experts. In 2003, he coined the term “One Health” to describe the interdependence of healthy ecosystems, animals and people. He has led programs and projects in more than 45 countries and has published more than 160 scientific papers and numerous book chapters, as well as written for broader audience publications such as Foreign Affairs. At this time we welcome Dr. Karesh. Dr. Karesh, please go ahead.

Dr. William Karesh:   
Great, thank you Nikki. It’s a pleasure to have all of you online and good morning and good afternoon, depending on where you are. I think I’ll just start right in and unfortunately I cannot control the slides, so I’m going to be asking Nikki to advance them for me. I think everybody will get used to that after two or three slides. So, why don’t we go ahead and start.

The title slide. I really have a second title, and you see the disclaimer at the bottom from the CDC that I’m not representing the views of the CDC, but at the same time my second title for the talk is really, “let’s not believe everything people tell us.” And I’ll get into that a little bit because I think things I learned when I was in school, and even things we’ve seen published a few years ago on a regular basis, those are certainly being overturned and we have new findings.

But today I really want to, and if you go to the next slide on zoonoses, get into some of the things that are driving the emergence of zoonotic disease, and I don’t have to explain what those are to you, but I just did want to give this reminder that they are a significant burden of disease on the planet. So the conservative estimates we have are about a billion cases in people, and some publications have even gone up to two billion cases, but it also depends on how you define a case and how you define a zoonotic disease.

In any case, there’s certainly millions of people every year that are dying of zoonotic disease. Next slide. So there’s many ways we people are being able to look at these, and this is   a connection chart, a network chart of how different zoonotic viruses, just the viruses, not bacteria or parasites, are linked between humans with either domestic animals or wild animals. So it’s   a complicated view, but it does   show that there’s a subset that we think about with domestic animals, and you see marine mammals are really out there as an outlier. But   the majority of this network of viral sharing is with wildlife.

And on the next slide you might be more familiar with some of this data, that’s been   circulating for a while. This is some early work from EcoHealth Alliance on the analysis of patterns of EID events. These are temporal patterns, so we see the change over the last 50 or 60 years, that emerging infectious diseases are going up. A majority of those, as you see, are actually zoonotic, so the blue ones at the bottom are the non-zoonotic, so we have this   rise in zoonotic emerging infectious diseases, and the link to wildlife is a large percentage.

So nowadays we talk about 70% of these new emerging infectious diseases, somehow link back to wildlife. So that’s   some of the underlying data that we   are starting to see now.

If you go to the next slide, there’s so many ways, and I’m going to   repeat this theme throughout the discussion of the topic today, about how to illustrate what we have for data and how to analyze it of course. So this is also from Kate Jones’s paper that we did many years ago on the spatial patterns in EID events. So if you look at this, and in a very traditional approach, you say where were all the emerging disease events taking place? And you map that out, and we would have a tendency to say, well this then is the map of where the hottest or riskiest places are in the world.

But doing that, if you take historical data and analyze it or map it, what we have really is just a history lesson, and it’s heavily biased by reporting. So this distribution has probably as much to do with reporting bias, who’s actually published a paper on emerging disease event, than it has to do with a disease event at all.

If you go to the next slide, please. If you start to eliminate some of that reporting bias (so this is from some of the more recent work so we’ve   moved on to what we call Hotspots 2) you start to get a risk map based on the underlying drivers. And that’s done by each of those emerging disease events, you go and study back on that event, and see what are the characteristics, the ecological characteristics, the anthropological characteristics, socioeconomic characteristics, that go along with that emerging disease event. And then you forget about the events and you start to map out where the key characteristics are.

So then we get a predictive model of risk, and it’s a relative risk. So in this image you see the orange or the redder zones are a higher probability - or risk - of an emerging disease event, and you can see on the bottom there the various factors. So we looked at about 200, 250 globally distributed datasets, and picked out the ones that seem to correlate with these emerging disease events. And you can see the relative influence there.

So things like population, mammal diversity, biodiversity (but particularly mammal diversity) population, land use change (here it’s characterized as pasture use change, I’ll show you that a little more) are really the things that have the biggest influence on where emerging diseases are most likely to occur.

Now of course it varies by region, and it varies by the type of disease, but this is a   a generalized view to start saying if you’re interested in targeting emerging disease surveillance, certain places are more valued for targeting than other places in the world. And that starts to come out in some of this predictive modeling, rather than looking at the historical perspective, which I said before is really biased by reporting.

So on the next slide what’s going on and   the underlying theme that is going to, behind what I’m going to be talking about today, is the fact that diseases emerge, and particularly the zoonotic diseases emerge, basically on the same principles of evolution that have been working for millions of years.

So the underlying principles of ecology and evolution of infectious diseases, or anti-microbial resistance or other things, you know, that involve life form, biology, are the same. What’s changing now is   what we’re doing with the environment. And so it’s like we’re changing the playing field or we’re tilting it in one direction or another.

So if you imagine a soccer game or a football game, the rules aren’t changing in the game but the way that the playing field is shaped are changing, and that’s what we’re doing on the planet right now. So we have pretty dramatic changes in the last hundred years. So we’re calling it the anthropocene, some people refer to it as that, is this era now in time where humans are tending to dominate the landscape, and we’re affecting not the principles of evolution but the way that we’re seeing things evolve. Next slide, please.

One of these is of course the changing of land, and that’s probably the most dramatic. So this is a slide of a 100-year gap of land devoted to pasture. And this 2000 one, on the right, is what we used in the original hotspots model, but now we have datasets that showed the change every ten years, so we have decadal change, and that’s giving us high resolution.

And for a lot of the new datasets that are available, thanks to satellite observation data that’s much better than it was 15 or 20 years ago, we’re starting to get a clearer and higher-resolution view of what’s going on on the planet.

Next slide, please. So this is reflected, if you can see just the resolution between the old hotspots model, the risk of disease emergence, and the one on the bottom, which is the newer model, which we’ve included some new datasets, like livestock presence and land use change, and you get a more refined view. Or if you go to the next slide, you’ll see for a particular region, this is Southeast Asia, you can see that everything’s not as red as it used to be.

So it gives us a little more refinement on targeting surveillance, targeting prevention programs, targeting research. So a lot of the donor agencies, research agencies are actually using this in a practical application to say where should they invest their resources, where’s a higher risk, and to really guide it because there’s certainly not enough money or time or people to work evenly across the planet. So we do have to be judicious about deciding where it is we want to work.

If you go to the next slide, this is a little different approach where we’ve taken every paper, every published report of an emerging disease event. Once again, over the last 50 years or so, and we used what came from the US Institute of Medicine, it came up with some definitions of drivers of disease emergence 20 years ago.

So rather than reinventing those definitions or making up our own, we went with the definition provided by the IOM, just as something that was   neutral and wasn’t biased towards our particular work, and went through these EID reports and scored them where they fit in these categories. So it’s a little different approach than I showed you before, but you see on this one where land use changes and agricultural industry changes are the highest percentages of underlying drivers, what’s behind some of these EID events.

International travel and commerce again comes up at the top, that’s the third bar. And that’s once again because that’s not so much the disease emergence from going between an animal or person or even in a person, but EIDs are also defined of course by a known disease occurring in a new region. So Zika virus emerging in the Americas is considered EID, even though it’s a virus and a disease we’ve known about for 60 or 70 years.

So that travel and trade works in there. Medical industry changes come along in the recent years, it probably has a lot to do with implants, excuse me, implants, mostly transplants, excuse me, more commonly, war and famine, so some of these things.

I like to show this chart because for about 20 years of my work I’ve had a big focus on bushmeat or wild animal meats, particularly in Africa. And so we know bushmeat’s linked with HIV/AIDS, we know it’s the original cause of spillover for Ebola events, so there’s some pretty dramatic diseases, and in my mind, because of my personal professional bias, I always thought bushmeat was probably one of the largest drivers of disease emergence.

But lo and behold, when we take an objective approach, and I have to admit that I’ve been wrong for many years. If we look at this quantitatively and objectively, things like bushmeat actually fall down very low on these drivers of disease emergence. It doesn’t mean they’re unimportant, we wouldn’t say that HIV/AIDS is an unimportant disease, it’s just that in the number on that relative number of events, bushmeat doesn’t shake out the same way that land use and agriculture and international travel and trade does.

Now in the next slide, I was saying those are big generalities because in every country it’s really different. Now we’re starting to get into, I’m going to start walking here on some thin ice, because if we divide up the 200 or 250 or so emerging disease events over the last 50 years, the number per country starts to get very low.

So we still see patterns, so you see in central Africa there’s a little more red and pink and purple, so that’s war and famine and bushmeat, whereas in North America that’s not how it breaks down. We don’t see diseases emerging from bushmeat or consumption of wildlife in North America. You know, lucky for us, we eat deer and we go fishing, and there’s just not too many new emerging disease events that come from the common animals that Americans tend to eat when they go hunting or fishing.

Next slide, please. Another way to break down the same data is to put it into transmission pathways. And this is another  , trying to use the analytical work for some practical implications. So we see that the types of transmission pathways, sometimes grouped by diseases, you know that at CDC diseases, they tend to have specializations around vector-borne diseases or respiratory diseases, and that makes a lot of sense clinically.

So when we start to break those down this way, food-borne diseases, we can see that the types of drivers have different characteristics for the types of disease pathways that are linked to them. So land use change is highly represented like vector-borne disease. Agricultural industry change, more about direct animal contact and oral transmissions, which would make sense because we’re thinking about food animals and food production there.

And medical industry changes are of the direct animal contact type. I think in that category we also have laboratory workers that are new, diseases have been identified in lab workers with, they’re handling animals so maybe some cases of Marburg were linked to that, or other laboratory primates, or laboratory rodent diseases have gotten into people.

It’s not just interesting academically. For us it means that as areas are being developed, let’s say in South America or Africa or even in the US, if land or forest is being converted to pasture, or forests are being converted for forestry and then there’s a mining operation, the types of diseases we can expect to see there would fall in that land use change category, which are vector-borne diseases.

So the   surveillance systems that need to be put in place, or the kinds of preventive medicine that needs to get put in place, should be a little biased towards vector-borne diseases to be most cost-effective, so we can have vector control, mosquito control, those kinds of things in a mining or a forestry operation.

Whereas in agricultural industries changes, if that’s happening at a given location, we can start to say, “Well, we need to have better sanitation, better education for the workers, those kinds of things.” So the interventions start to look a little different and the prevention strategies start to look a little different. So it gives us some clues about where people should focus their efforts in different kinds of situations.

If you go to the next slide, please. Now we can do this for a variety of different things. Most of the ones I’ve been showing you earlier were zoonotic, emerging zoonotic diseases linked to wildlife. But you can do the same type of analytics on vector-borne diseases, it’s the bottom right, or drug-resistant pathogens, that’s on the bottom left.

So the trick once again, and the reminder is that, not to be just overwhelmed or swayed by the historical data, because that’s very heavily biased by reporting, but to look at the characteristics underlying those events and then map out where those characteristics also exist, and in what situations do they occur, because given without those underlying situations, the probability of this disease event occurring are very low.

Next slide please. This is a look at breaking down that same approach I showed you about the drivers of emerging disease for food-borne infectious disease events, emerging infectious disease events. And then it shifts a little, so that land use change drops down lower, but food industry change goes up.

This is pretty intuitive, but as I said before, I used to think bushmeat was so important, so this confirms our intuition about what’s doing it. But it also says where we can be targeting our efforts, so working with, directly with food industry we can really reduce the number of these events rather than saying it’s a personal hygiene effort there.

And the next slide. This might not surprise some of you, caught me a little off guard because I do a lot of work with viruses and I realize for food-borne illnesses viruses just really aren’t that important as bacteria. So once again trying to add some objective nature to the kinds of things as we discussed and letting go some of our personal professional biases based on, you know, where we spent years working.

Food-borne illness, unfortunately you won’t be able to see this video but there’s just this slide, and this is a typical market in much of the world. So probably millions of people on a daily basis are still buying their food in live animal markets. This one happens to be in Indonesia, and on the table there are domestic dogs. Below that table, I’ll describe the situation to you a little, are live dogs. So the customers will come, they’ll pick out a live dog to make sure it looks healthy, because there’s very little refrigeration available, and if the dog is dead or the animal is dead of course the purchaser, the customer doesn’t know if it was a healthy or sick animal.

So one way they protect themselves traditionally is to go to a live animal market. So they can see the animal before it’s killed and prepared for them. So these are dogs being prepared. If you, on this video, I next pan to the left, and then there’s pigs and pork. And then to a table off a little further to the left are fruit bats. So somebody’s selling hundreds and hundreds of fruit bats.

And a little further is the chicken salesperson, and they have several hundred to a thousand chickens, and then someone has fish, and then somebody else is selling rodents, so they might have 100 or 200 rodents that have been brought into the market. And all these animals are killed, and you see blood everywhere, they take the entrails so they’ll clean out the viscera for the customer, and then they feed that either to the dogs or they’ll save that later to give to the pigs, so the opportunity for viral dissemination or bacterial dissemination or any type of pathogen is pretty high.

And people are walking around in this market wearing flip flops and with their bare hands they’re touching food, and then they’re taking those products and going home and taking it home.

So I use this slide to say the only thing that seems to be surprising about these emerging disease events or that the fact that infectious diseases spill over between animals and humans, the only thing surprising about that is that we seem to be surprised every time it happens. Because the volume of this   trade is massive. So this is, like I said, in markets throughout parts of the world, Asia, Africa in particular, somewhat in South America, and those are areas of the world with billions of people. So I’m really   surprised it doesn’t happen more often, but these are very low, low probability events. Go to the next slide, please.

This is a typical picture of Ebola, Ebola virus disease. And this is   what we all   learn about, you know, this is what happens when you see this disease. Well, this what happens later on after the outbreak has started to go on for a while. If you go to the next slide, this is where Ebola hemorrhagic disease tends to start out. And I don’t know Nikki if the one billion kilos is up there, or if you have to click it again.

But the volume of bushmeat, this is just for Central Africa alone, the volume of bushmeat is tremendous, and that’s a gorilla that’s being, an arm of a gorilla, a gorilla arm and chest that’s being sold. In that picture of that community, that’s a logging camp in the Congo, Republic of Congo. And this is a very,   a typical logging industry camp, the company that owns the logging concession that they have the rights to the land for the logging, they will come in and build housing for their workers.

They might be 3,000 or so workers that are brought into a very remote area of the forest, where very few people lived before. To the left of that community, where   the ramshackle   community on the left are all the other people that start to move into the area to supply products to the workers, because the workers are getting paid cash, so a little cash community grows up around there, so you have sex workers, you have people selling soap and alcohol and tobacco. And then you have to have people that are selling food.

But since these are in remote areas, the most accessible food comes from the forest. Hence this consumption of bushmeat. So you’ll have a whole hunting communities that’ll be working the forested areas around the logging concessions, and at the end of the day they’ll come back with fresh meat for the workers to buy and process.

So this is the typical scene in forestry and it gets back to those earlier sets of data I was showing you, where what are the underlying drivers for these disease emergence. So when we say land use change is associated with disease emergence it’s not that cutting down the tree makes the disease break out, it’s this underlying behavior and activities that are linked with that land use change pattern that allows or facilitates disease emergence.

I did say these are really rare events. So what we have is, let’s say the probability of Ebola coming out of an animal and starting a spillover event in a human population, and it goes human to human, but what about that first step? What if that probability, and I don’t know what that number is, but let’s say it’s one in a billion, you know, very low probability event. But we’re talking about billions of kilograms of meat being consumed in the belt, if you’d say the area across equatorial Africa, where we know Ebola is probably lives in animals.

If you have billions of kilograms a year, so the fact that we get one spillover event into a human every few years makes perfect sense, even in extremely low probability, because we’ve really   overwhelmed the odds. It’s like if you bought a billion Powerball tickets next Saturday, you bought a billion tickets, your probability on any one ticket is just the same. But there’s a good chance if you bought a billion tickets you might win the Powerball lottery.

And I think that’s what’s going on, why we’re seeing these disease events, because I don’t think we have the appreciation of the volume or the scale of risk and exposure that’s really going on. So what seems to be a strange, rare event in fact is not very strange at all.

Go to the next slide, please. We’ve done some other analyses too, and this is  , if you start to map the similarity of diseases amongst countries. So this gets into more about the spread of diseases and who’s focusing on what. And so we   call it the biogeography of zoonotic disease. Down below is just the biogeography of viral diseases in general, they don’t have to be zoonotic. What this shows is neighboring countries, different regions of the world have a different list of diseases.

So if you’re going to go work in Africa as a clinician, your list of diseases that you’re going to see every day and be dealing with and need to be familiar with is a list, a different list of diseases than if you’re working in North or South America. And the neighboring countries share those more than others. But it also explains why we   seem to be surprised or ill-prepared for what’s happened in the last 50, 100, or 200 years about global trade and travel.

And now we have these mechanisms that allow the movement of these diseases and the introduction of diseases outside of their traditional ranges. So I think this map, we’re going to start to see over the next 100 years more of a blending or a homogenization of what we, what’s traditionally been seen in our community, in the health community, as something that   almost made us feel safe.

So I think there’s a false sense of security we have, and something that we need to be aware of. If you go to the next slide. You can actually calculate the relative risk of the movement from certain places to other places. So this is a mathematical model and we started this one with the Jones Hotspots that I started my talk with, and if you have passenger data you can put in some other factors about what might affect it.

And then if you go to the next slide, you can calculate the relative risk or probability of emerging infectious disease arriving at a given, an airport in the world with the passengers. So this would be an infected passenger arriving on an airplane. And certain airports have higher risk because of their connectivity with the hotspots, the places in the world that are more likely to have these emerging diseases.

                              So you can see the airports in Asia tend to be redder because they are more closely connected with those Asian hotspots for emerging diseases. Whereas if you come to North America, certain cities and certain airports in North America, are much lower risk because of the direct connections to higher risk parts of the world don’t exist, and then you can see   the east coast down in Florida and California, and some in the upper Midwest and Texas, where there are more international flights, show up as a little higher risk.

Once again, it helps you in   targeting where you’re going to invest your resources or your dollars or if you’re in practice where you might want to be paying a little more attention to diseases that are not traditionally in our communities but might exist in other parts of the world. Next slide.

I don’t know how this is projecting to you, but for me, I’m seeing all the dates on there. But about two years ago we ran the, using that formula that I just showed you on the risk of spread via air traffic, we could project out which countries in the world were at highest risk of person with Ebola arriving in that country.

And so this one is based on Guinea, which is a little tiny purple country right in the middle there in West Africa, and then we project it out and Nikki, you might have to click through if the dates aren’t already showing. We did this in late July, the end of July, and we shared this pretty widely with our partners around the world, and then I have, when Ebola-infected people actually did arrive following that.

So all through July and August and September, and they tended to arrive in those countries that we predicted at highest risk. Not that we’re so wonderful but what I’m trying to say is, you can actually use a quantitative analytical statistical approach to dealing with this. The insurance industry’s been doing this for many, many years and making lots of money. So we know that predictive analytics are effective, and you know we’re trying to apply it to some things that are useful to us in the health profession to be able to do this.

This system is now online and publicly available, and you can add, you can start in any country and particular disease. It doesn’t even have to be a disease if you wanted to say where’s the risk of illegal shipment of something with an airline passenger, you can calculate that too. And then CDC also has a proprietary system, it’s very similar, the results that come out of it are very similar. But that’s only available for US government staff to use, but ours is publicly available and I can send you a link to that if you want to play with that sometime. And it’s based on, it’s really based on passenger airline data over the last year and over the last month, and it’ll calculate risk for you. Next slide, please.

I want to zip through these. We also, some of the work I’ve been talking to you about is based on this, we call it Host Pathogen Database, HP3 Database. And we’ve gone, once again reviewed all the literature, and we picked out 600 viruses that are found in mammals and we identified every mammal, including humans, that was infected and we have these virus-mammal relationships.

So we can   use this to say, once you start to know where all these animals are in the world, it’s another way to   map out where the spillover events, or these diseases might emerge, or susceptible hosts in case a disease is introduced, because you have related host species in different parts of the world. So if something came to the Americas, you know, which primates, we know that primates actually for Zika virus might be able to maintain it, like they do, primates do in Africa.

This next slide if you do, and we break that down, what we do see is three   taxonomic groups of mammals share the most viruses with people. And those tend to be bats and primates and rodents. For one reason is there’s - the taxonomic group of rodents is huge, there’s probably over 2,000 species of rodents, and for bats is quite large too. There’s over 1,000 species of bats, maybe 1,400 bats.

If you go to the next slide, it has to also do not just with numbers but also our genetic relatedness. So humans share more viruses with non-human primates.  Of course we’re the more closely genetically related, but also bats and rodents are more closely genetically related to humans and other animals like gazelles or kangaroos or on here, platypus.

So as you get further away from us genetically, we share fewer viruses. So that also tells us which species we have to be more concerned about as a risk factor, or if you want to target surveillance, you know which species you can target your surveillance.

You can also use some of these analytics to   jump into future projections. So I’m just, next slide is climate change or emerging diseases, it   says, here’s a virus, Nipah virus, which we know is in Southeast Asia. As you know, it’s the reservoir is in bats and it gets into swine, into pigs, but also gets into humans, causes a hemorrhagic disease, is highly fatal. There’s really no treatment, and there’s no vaccine or prevention.

So we said okay what if Nipah virus, and what would the ecological niche for Nipah virus be in 30 years from now given certain climate change scenarios? So we used the standard scenarios, we didn’t make up our own, and we start to identify what parts of the world would be potentially suitable if that virus got introduced in the future.

So it   starts to say if you think about invasive alien species, this is   a similar thing that you can start to do with diseases to get people to start thinking ahead about this travel and trade, international trade and travel, and some of the risks that come along with that, and what we might want to do to prevent the spread of disease. Next slide, please.

So it looks like this is good, I have about ten or 15 minutes. I wanted to   pull what I’ve been talking about together and use leptospirosis,   somewhat of a case study. It’s not a virus, obviously, but so it gives us an opportunity to talk about something that’s a little different, but it’s also, I’m going to give a US, North American US example of a spread story to this.

So don’t really have to explain the background to lepto for you, because I’m sure you all understand this disease. What we were able to do was get data that was available from IDEXX laboratories for canine leptospirosis testing. So this data relates to canine leptospirosis, but I think by the end we’ll see that the underlying drivers are independent of the host. So you can think of this as somewhat species-neutral even though our preliminary data, what we’ve been working with, is from dogs and it’s a nice   robust big data set.

So we had, this is testing coverage, this is not positive test, this is just testing coverage. So where people, clinicians, in this case veterinarians, submitted samples for testing, either micro-glutenation, which is a serology test, or PCR, which was the test for positive antigen, actually being leptospirosis, shedding leptospira at the time of the test.

If you go to the next slide, if you break that down by the different test types, you can see it does vary a bit. So this is the micro-glutenation, the serology testing gives you   this pattern for the US.

And if you go to the next slide, it has a little comparison, but we did some analytical work there to say, “Where are the clusters of these positive tests?” So you see the distribution’s a little different, but these clusters are above the expected or in orange or red, or blue would be below the expected if it was randomly distributed across the United States.

So you start to see, some people say, “Okay, this is starting to give us the hotspots of disease.” But once again, remember, this is very biased on where we have test results, and on that original map you see that the people didn’t even submit test results from parts of the country. So to jump into this and say these are the hotspots of leptospirosis would be very misleading, even though these are the hotspots for positive MAT results to one particular laboratory. So they have to be, they always have to be careful in making sure we’re very clear about what assumptions are there.

We also tried to calculate dog population data to somehow normalize those results, because in one part of the country where we’re seeing a lot of positive tests, it might just be because there’s a lot of dogs. So there is no existing dog population data by county, so we extrapolated that because we were able to get dog population data by state.

And then we worked out the   the number of dogs per person in a given, in different communities and different states, and extrapolated that then to all the counties in the United States based on the human demographic data, and making some assumptions about   the numbers, the average number of dogs in either rural settings or urban settings to be able to work out. So we   have,   a cleaner map of dog populations by county in the US, so we could start comparing some of our test results with.

The next slide, you start to get a little different, so when we use it by dog population, the same test results I showed you before, you start to get a little different image of the distribution of disease when it’s actually corrected for the number of dogs. So we’re getting a little closer to knowing that but remember this is still just test data that’s available.

We looked at vaccination, thinking well maybe some parts of the country are vaccinating dogs more, and that’s why we’re getting fewer positive test results on PCR, because of great vaccination. So we did have this two data sets, one is the number of dogs vaccinated per state, over that four-year period, and on the right the numbers per estimated dog population.

The interesting thing is, you look at that as you say the records that we get from the state-level data on who’s, on how many dogs are vaccinated, gives us one picture, but when you correct that for the number of dogs per state, we get a very different picture, what percentage of the dog population is given. Once again, this is like, it always reminds me that when people show us this information, you really have to understand, to dig into what it is we’re being shown and just not immediately take it at it’s face value.

So the next slide is we wanted to look at some of the ecological and climate factors, so I think most of us were taught at one time that lepto has a lot to do with rainfall, and if you go to the next slide, we have the rainfall data from the United States, and that’s gridded out by counties, and it doesn’t align with what we saw for any of the leptos testing results. And that’s because it’s not really about annual rainfall, it’s not about mean precipitation, and we don’t see a relationship with mean temperature.

The lepto data has more to do with flooding, and flooding has very little to do with annual rainfall. Flooding has a lot to do with topography, how the land is shaped and also what the land cover is. Is it forested or is it grassland or is it bare? And so flooding is a much more complicated picture that we, some people tend to think of as linked to how much rainfall there is, but that’s not really the case.

If you go to the next slide. We also looked at, we took the test result data and saw if there were relationships between education levels or income, so we looked at some socioeconomic patterns for all the counties in the United States. Once again, we looked at about 200 datasets, and I’ll show you another picture next to see which ones lined up with where we’re seeing those test results.

Once again, like I said for the hotspots, what we wanted to do is say, “Are there some background characteristics linked with the test result data that then we could extrapolate to see where those conditions exist in the US?” And what we see is, there is actually some relationship with the test results with income and education.

Not exactly sure I can postulate a few ideas, but remember these are tests. Samples are submitted by clinical veterinarians for testing, and you know that varies by the clientele and what they’ll tolerate and pay for, for testing certainly varies by income level and maybe somewhat by educational level, and those two things we know are somewhat linked: education and income. So we might have some biases there. Next slide.

This is called a partial dependence plot, and what we did for each of those variables, we’ve plotted out how they correlated with, this is the micro-glutenation test, but we did it for the PCR and the MATs. So you can see that what we’re able to do with this is throw out a lot of these variables that don’t seem to have an effect on the test results. And pick some that do. And if we go to the next slide, we end up with these types of variables became our best predictors of a positive PCR or a positive MAT.

So now we’re starting to get into some of these underlying drivers and ecological correlates, they’re associated with positive test results. So I mentioned this  , the tree cover, the forest cover, some of those things seem to be linked with, and at the bottom on the left is median income there. But most of them are   ecologically linked and if you go to the next slide, when we project out those, this is what we start to see as a predictive model of where we should be seeing canine leptospirosis.

And this is static, this is over, we have 14 years of data that we’re working with, so when you compile the 14 years of data you see this. If you go to the next slide, which is another video you can’t see, but this shows ten years, well this is, yeah, I think, no this might be actually 14 years of data, but this is time series analysis and I can’t show you, but if you look, you squint hard you see around the New York area’s a little blue, and Chicago’s a little yellow, and California’s a little orange.

So that is for that three-month period in the year 2000 of places that are hotter than we expected, hotter than average, or cooler than average. And as this time series runs over ten or 12 years, if you run this out the leptospirosis comes and goes, so the New York area will become hot in a year or two and then it’ll go cold again.

So it really varies with time, so the disease is not a snapshot view, you can’t really predict this based on any given year, but we do actually see cycles on this, there’s probably climate relationships, but it’s also probably about infected recovered individuals when there are large outbreaks in areas, more animals tend to get vaccinated then, that probably gives them coverage for a period of time, so you see that the lepto goes away

If you go to the next slide, this is really   my conclusion is, that we can look at this thing, these things different ways. So on the bottom right is an original slide were it said here are the clusters by estimated dog population of where based on test results that we have from just IDEXX laboratory once again, you say, “Oh, those are the hotspots for canine leptospirosis.” If you look above that, you see where people are vaccinating dogs, you see even based on that, we tend to be under vaccinating dogs in some of the places in the United States, particularly out west, and we’re under-vaccinating dogs for it.

And on the left, on the far left you have our predictive   model based on the underlying drivers, where we actually expect to see leptospirosis, irrespective, it doesn’t matter where people are testing or what laboratory they’re using, that’s   what we see   as where the riskier areas for leptospirosis. There is no place in the United States with zero risk, we just have places, this is relative risk, so we have places that are lower at risk, places that are higher at risk. And that’s if you go to the next slide, just finish up there. Thank you very much for your attention.

Nikki Grimsley:    
Thank you, Dr. Karesh. We will now open the lines for the question-and-answer session. As a reminder you may press “*-1” on the phone to put yourself in a queue for questions. Please state your name and then ask a question, or you may submit questions through the webinar system by selecting the “Q-and-A” tab at the top of the webinar screen and typing in your question.

All right, I have a couple of questions. Dr. Karesh, can you expand on how climate change would expand the range of Nipah, and is it based on the best temperature range for a virus or for bats? And the second part of the question is, does it also account for climate change promoting severe weather events, such as, do things like drought or heavy rain come into the calculation?

Dr. William Karesh:   
Those are great questions. The image I showed, those calculations were based on mean precipitation and the mean high and low daily temperatures. And there were immediately some flaws inherent to that, and I discussed that one about flooding and things.

But it’s   the best datasets we had to work with at the time. So we, in answer to the second question, we didn’t have the ability to look at the increase in severe weather events, and I think it probably could be done and it would be a really cool thing to do with some of the colleagues at NOAA and NASA, because I bet they have some good projections for that.

But as far as Nipah’s concerned, our understanding of the ecology of that disease is heavily linked with bats, and they’re fruit bats, so it’s about the fruiting pattern of trees and where those bats move around, and they are somewhat migratory there.

In this projection, why could Nipah get established in the new world is because of the similar genetic relatedness of bats to it, and also the ecological niche for the bats that don’t exist currently in the new world, of them becoming established here. So it might involve the release or the introduction of a host species of that same bat into the new world.

Some people would say, “Oh, well you know, well that will never happen.” But if you think about reality is, we have a couple of hundred invasive alien species now in the New World that have been introduced in the last hundred, 200 years.

So the introduction of new species is not a very rare event, in fact it’s somewhat of a common event, and that allows the host, what those projections are really saying is the,   the guild of types of bats that are, we know can host the nipah viruses or related to them, the ecological conditions that exist in those areas in the New World and in Europe to allow those species to flourish, where currently they wouldn’t do as well. I hope that answers the question.

Nikki Grimsley:    
All right, we have another question. What are some of the informatic approaches that can assist with prevention, treatment, and control of EID events?

Dr. William Karesh:   
What are some of the, what type of …?

Nikki Grimsley:    
Informatic, informatic approaches.

Dr. William Karesh:   
Informatics. Well, I’m not sure I understand that question quite right. But if it’s about information sharing and data sharing and education and outreach, I’m just going to go down that line, thinking I’m understanding this.

But I think, I’m   an old-fashioned guy, and when everybody goes like, “Oh, there’s this new disease, and we don’t know how to treat it, and we don’t know, we don’t have the vaccine,” I’ve started to put them back in those categories that I talked about, and said, “Well, is it foodborne, is it vector-borne,” and we actually do know some things about how to prevent those diseases, and what we don’t, we seem to fall down on is sharing that information, getting it out to the public.

So Ebola, you go like, “Oh, that’s really horrible,” but if you never teach people how to avoid getting it, and that’s rural community education and outreach, then we’ll never get it under control, and in communities in the Congo, you know, I’ve worked in northern Congo, did education programs in the whole northern half of the country to every village, and explained to them that if they see a sick or a dead primate, non-human primate, don’t touch it and don’t eat it, and then stay, you know, don’t mess with the bats.

And they were so appreciative because no one had ever told them, you know, how they could reduce their risk of getting Ebola. No one, you know, so that’s not a hard thing for them to avoid. Their diet is not dependent on eating gorillas, they just do it because it’s opportunistic. They find it and they see one, they do it.

So I don’t know if that’s informatics, but I think as we get out, I’m going to link that somehow to text messaging and cell phones, because as you go through Africa now, or through Asia, everybody’s better connected, and I think these opportunities exist to start really educating the masses and not, you know, the public health, either veterinary public health or human public health, you know, should, the burden of that can’t just fall on medical professionals. You know, we have to find good ways to share that information to engage the public so that they can pick up part of that responsibility.

And then it also goes to other sectors of society too. The food industry’s aware of it more than many, but even the department of water, and the department of education need to be much more engaged in health promotion and sharing information. I hope I at least skirted close to that question, sorry.

Nikki Grimsley:    
Thank you, Dr. Karesh. Why don’t we take a break and see if there’s any questions through the phone? Amber? Are there any questions on the phone?

Coordinator:         
We do not have any phone questions yet.

Nikki Grimsley:    
Okay, then we have another question. Dr. Karesh, can you address the concerns about the ongoing Nipah infection with the tradition of drinking raw date palm sap? Meaning, how do you educate folks about how they can protect themselves from zoonotic diseases while respecting tradition?

Dr. William Karesh:   
Yes, that’s a great story, and a great example of, you know, community engagement. So for some of you that don’t know that, Nipah virus in Bangladesh, not so much in Malaysia, has a different transmission pattern route, but in Bangladesh Nipah virus goes from fruit-eating, nectar-eating bats into humans because there, because humans collect the sap of date palm trees and drink it. And they’ll put an urn or a bowl or a jar, they’ll climb up in the tree and they’ll cut it just like we tap a maple tree, they’ll tap on the side of the palm tree, and then they’ll tie the jar, the urn there to the tree and leave it overnight, and the sap will run into that, and it’s   a light, juicy, sweet sap, and they like to drink it.

Well, during the night the fruit bats also come in and they eat or drink the sap also, because it’s sweet and it’s tasty. And while they’re there, they contaminate, potentially contaminate these pots that are collecting the sap with urine and feces and saliva. And then the human outbreaks come from when people drink this sap.

Now one thing of course you could do like we do with milk is pasteurize or boil the juice before you drink it. But local people, they don’t mind that, but for them it doesn’t taste as good, it does change the flavor and it doesn’t bring the same high price as the fresh juice does, and they like to drink it very fresh before it starts to turn alcoholic, in the case of Bangladesh, where they don’t drink alcohol.

So one thing is just to educate millions of people. The challenge in Bangladesh, is it’s one of the most densely populated place in the world. On the positive side is not everybody harvests, there’s a limited group of people that harvest sap. CDC working with the, there’s a Bangladeshi institute for research on diarrheal disease, really long established, and CDC’s been working with them for years, did some very cool work. And we, the EcoHealth Alliance was involved with that a little, but the CDC, the Bangladeshi partners there did some great work using infrared cameras at night to watch the bats and really documented this contamination of the sap.

 Step two was to come away, well maybe if we can’t get them to heat the sap, maybe they could   protect the pots. So someone there developed this idea of making these skirts out of bamboo to cover the pot to keep the bat out and keep the urine and feces out. And it cost maybe ten or 15 or 20 cents for a little bamboo skirt, and then you cover the pot and then that reduces the risk, and the added benefit for the people collecting the sap was the sap is cleaner and they can actually sell it for a higher price because when they collect it in the morning, there’s not feces floating around in it.

So it’s a little cleaner, they get a little higher price, and they reduce their disease threat. It’s not a perfect system, but it’s that   innovative thinking is certainly cheaper than trying to develop a new vaccine for Nipah and vaccinating millions of people. Over.

Nikki Grimsley:    
Amber, are there any questions on the phone line?

Coordinator:         
We do not have any questions on the phone lines at this time.

Nikki Grimsley:    
Okay, thank you very much.

Dr. William Karesh:   
We’ve run a little over time, my apologies.

Nikki Grimsley:    
Oh, no problem, it was very interesting and insightful information, thank you so very much.

Dr. William Karesh:   
My pleasure.

Nikki Grimsley:    
On behalf of CDC’s Clinician Outreach and Communication Activity, I would like to thank everyone for joining us today. With a very special thank you to our presenter, Dr. Karesh. If you have additional questions about today’s call, please e-mail us at COCA@cdc.gov. Put “May 12 COCA call” in the subject, and we will get an answer or response to your question.

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Thank you again for being part of today’s webinar, and we hope everyone has a nice day.

Coordinator:         
Thank you. That concludes today’s conference, thank you for participating you may now disconnect.