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Eggheads is the go-to podcast for egg industry professionals who are interested in leadership and innovation in the egg world. Host Greg Schonefeld explores the evolving world of modern egg farming, from the latest in cage-free innovations and organic certifications to navigating the economics of large-scale production. Whether you're an egg producer, supplier, or involved in poultry genetics, this show provides the insights and expert discussions you need to thrive in the industry. Crack open the science, strategies, and stories behind the egg industry’s biggest challenges and opportunities.
Dr. Maurice Pitesky:
Data is fundamental toward understanding what the predictors of any disease outbreak are, and we don't do a very good job of collecting it and sharing it and connecting it to what's going on outside the barn.
Greg Schonefeld:
Hi there. Welcome back to Eggheads. I'm your host, Greg Schonefeld. Predicting and preventing disease outbreaks is one of the biggest challenges facing the poultry industry today. With avian influenza continuing to threaten flocks across the country, farmers and researchers alike are looking for smarter ways to stay ahead of the curve. But what if we could use technology to forecast disease risk the way we predict the weather? Our guest today is an epidemiologist and associate professor at UC Davis School of Veterinary Medicine. He's dedicated his career to studying how diseases move through animal populations, and he's taking a high-tech approach to the problem.
Dr. Maurice Pitesky:
We're dealing with the largest animal disease outbreak in the history of animal agriculture here, but the reality is that during an outbreak, that's the worst time to reach out to a company and say, "Hey, I've got this new technology I want to work with you guys on."
Greg Schonefeld:
Dr. Maurice Pitesky is also the founder of AgriNerds, a startup behind the Waterfowl Alert Network. A tool designed to help farmers predict avian influenza outbreaks by tracking migratory bird movement in real time. Today we'll explore how cutting edge mapping technology, data science, and even drones could play a role in the future of disease prevention. Plus, we'll discuss why the industry needs to rethink biosecurity, the way we collect farm data, and even how we manage our landscapes to reduce risk. You're not a farmer by background, but ended up in this industry and studying as a veterinarian in epidemiology, can you talk some about that evolution?
Dr. Maurice Pitesky:
Long story short, I grew up in a small town called Los Angeles, so I don't have much of an ag background to say the least. I was a history major at UCLA, took a biology class for non-majors, fell in love with it, got really interested in the combination of science and medicine and food, and I really like to, as best as possible, connect dots across multiple fields, engineering, social science, biology.
Greg Schonefeld:
That curiosity led Maurice into some unexpected places, including national security work.
Dr. Maurice Pitesky:
Eventually worked my way up to Lawrence Livermore National Lab and did some biodefense work during 9/11 and after that and started having to go to the vet school at UC Davis to collaborate with them on some work related to the anthrax detector that we had and really got interested in veterinary medicine.
Greg Schonefeld:
Maurice's background isn't just in veterinary medicine. He's always been drawn to connecting the dots between different fields. One of the biggest ways that curiosity has shaped his work is through mapping and spatial analysis. Understanding where and how diseases move in real time has become a major focus of his research. So how does mapping actually help predict disease transmission?
Dr. Maurice Pitesky:
I think, and I'm biased, but in the poultry industry and in ag in general, there are ways of predicting where disease is going to move that are based on these spatial predictive models. So understanding risk and assigning probabilities and making models that farmers and other stakeholders can interact with, and that can literally give them on their phone which of their facilities are at the greatest risk, that's definitely my area focus. And we're really just scratching the surface of what we can do with mapping. So when it comes to highly pathogenic avian influenza, for example, if you look at the literature and you talk to the industry, the veterinarians, the regulators, they're focused on really ultimately the facility itself, the operational and physical biosecurity of that facility. And that's important a hundred percent. That's the way we're trained, but we don't think so much about the environment outside of that facility and how that can drive disease transmission.
We live in a world where we have satellite imagery, the entire earth, every 24 hours, remote sensing of different types of climactic conditions in real time and forecasted time. We have agricultural data on what crops are being grown. We can really leverage those data across the 44,000 plus commercial poultry facilities in the US and beyond to really make very accurate predictive models to help farmers at the single farm level really figure out, Hey, this farm over the next three days is going to have a high risk of disease transmission for whatever disease we're looking at. I think we're really just scratching the surface on how to use that and how it can be leveraged, and how it can be visualized easily by normal people who have got other things to stress about, so they can literally get that red light, green light type of response for their farms that they're overseeing.
Greg Schonefeld:
So this really gets into one of your roles, which is as the head of AgriNerds. Can you talk a little bit about how AgriNerds came to be?
Dr. Maurice Pitesky:
So I'm an academic, and I've worked probably for the last 10, 15 years on this remote sensing of waterfall using weather radar and satellite and telemetry and this great infrastructure of resources that we have that was designed more for earth observation, not for highly pathogenic avian influenza or waterfowl movement, but we can leverage it for that. And I've been able to collaborate with a brilliant professor at University of Delaware, Jeff Buler. He's a radar ornithologist, and we've worked on this with USDA grants. We started with one very, very small grant that was internal from UC Davis and then we leveraged that into a much larger USDA grant. When High Path AI struck in 2022, we started having some farmers in the Midwest reach out to us and ask if they can use our technology. I scratched my head and I go, well, our USDA grant only has resources for California and the Delmarva. And they said, "Well, you don't get it. We don't want a USDA grant. We actually want that as a service."
So started thinking about this, and UC Davis to their credit, is very helpful along with a lot of other universities about spinning out technology into startups because ultimately ... And this is where I think one of the challenges that USDA needs to eventually navigate, they do give a lot of money relatively so to High Path AI research, but there's not a lot of money that's spent on how to scale that research across the industry. So ultimately what we did in order to make it accessible to this company and to other companies is we created a company, AgriNerds. You can see my sophisticated sense of humor. And then we have this tool called the Waterfowl Alert Network, which makes daily predictions of waterfowl around the 44,000 plus commercial poultry facilities.
Greg Schonefeld:
In fact, the Waterfowl Alert Network is the first tool of its kind to provide daily high-resolution predictions of waterfowl movement down to a 250-meter scale. Originally designed for poultry farmers, it's now being used by airports, solar farms, and even golf courses to manage waterfowl related risks. And with historical data going back to 1995, it's helping researchers understand long-term migration trends like never before. But for a tool like this to have its biggest impact, it needs widespread participation. That's exactly what Maurice is working toward. Making this data accessible to more farmers industries and even public health officials.
Dr. Maurice Pitesky:
My goal, I'm an epidemiologist by training and a veterinarian, so we want high participation among poultry, dairy, swine, other livestock, and now departments of public health are starting to express some interest in these types of tools. So my ultimate goal is to make it freely accessible to everyone. That's how you get biparticipation, and that's how you get these large companies that can very easily rank their farms from one to 10 or one to a hundred, or one to 1000. Which ones are historically? We can go back to the '90s to really understand what's going on at these facilities. As I mentioned to farmers all the time, the farm does not change location, but the risk does change. We can use these tools very effectively. It's going to take time to get participation. And one of the ways we can get participation is almost like similar to what MPIP does, where there's a collaboration between the states, the feds, and the companies, the industry itself. That's my dream. We're not there yet, but I think we're unfortunately in a position where we need to try some new things. We know ... USDA's own data suggests this, our data suggests us that waterfowl are part of the problem, and that understanding where waterfowl are relative to these facilities matters. That's really important. There is a way to do this, but it takes like everything. It takes some time to change behavior and things like that.
Greg Schonefeld:
Maurice saw radar ornithology being used to track birds and insects, but he realized it could have far greater applications, especially for disease prediction in agriculture. By adapting this technology, he's now helping industries from poultry to dairy monitor waterfowl movement on a global scale, identifying high risk areas before disease ever reaches North America. But how exactly does this radar system work? What allows it to distinguish birds from other objects in the sky?
Dr. Maurice Pitesky:
First of all, weather radar ... We've had radar since World War II or so, and radar was very effective at identifying birds and other environmental variables. So raindrops, for example. So radar beam goes out, it bounces off the raindrop or bounces off the bird in this case and comes back and we can process those data to identify its raining in Fresno or something like that. So there's a network of about 160 of these in the United States. Canada's got a bunch. Also, China, Taiwan, Israel, all those countries and locations have their own systems. Ours is free, which is wonderful. Free is always good. And what we do is we take historic data from those 160 radar and we scrape that off of the internet. And then those radar beams, they go out for a good a hundred kilometers or so before the earth starts curving. So when waterfowl like ducks and geese, when they leave on their roosting flights at nighttime, the radar beam goes off of them, and we can detect the intensity of that beam so we get location, directionality, and abundance.
Greg Schonefeld:
Just to break that down a bit, the radar itself isn't new. It's the same government run weather radar system used to track rain, but Maurice and his team have leveraged that existing infrastructure using software to detect birds instead of raindrops.
Dr. Maurice Pitesky:
And what we do is we take historic data to ... Let's say all those waterfowl are leaving from a flooded rice field under certain temperature, humidity type conditions, and they leave from rice fields that are close to corn crops or things like that. So we can put that in a spatial model, and we take all this historic data. So now for our model, let's say for today, if I looked in the Central Valley of California, I could ingest data from satellite imagery to detect moisture, ingest environmental data from Oregon State's Prism project, and all that goes on the back end of our model. And then on the front end, what the farmer just sees is, Hey, we've got these 10 farms. The Waterfowl Alert Network is saying that farm number seven has the highest waterfowl abundance around it, and farm number one has the lowest. We need to tighten up our operational and physical biosecurity over the next three days. So it's like a weather forecast for waterfowl.
Now, like all models, there's advantages and disadvantages. So ours works perfectly ... Semi perfectly for the five months from November to the end of March. That's when waterfowl are in greatest abundance. I'm from California, we've got eight million waterfowl in the Central Valley, but that's not entirely unique in North America this time of year. We also have a lot of dairies and poultry facilities and things like that. So what we do from our model, long story short, we look at a four kilometer diameter around each farm. And the reason we do four kilometers is because waterfowl when they leave on their feeding flights, they usually go about one and a half to three and a half kilometers from their feeding flights at nighttime. So what we do is we just add up the abundance of waterfall in that four kilometer diameter from our model, and then we just rank the farms from highest to lowest. And then what we do is we overlap that on top of High Path AI detections based on USDA surveillance, and we put that all into another model, and that's how we rank from highest to lowest, which farms are at greatest risk. So if I'm a big poultry company, I have two farms, 20 farms or 2000 farms, I really want to know which farms I need to focus my operational and physical biosecurity.
Greg Schonefeld:
This technology can track bird movement and directionality with incredible sensitivity. This innovation might be key in helping poultry farmers predict disease risk. And that's where our conversation went next. How this tech helps farmers. If your system, the Waterfowl Alert Network has identified a high risk situation, what can the farmer do at that point? Why is that valuable to the farmer?
Dr. Maurice Pitesky:
Yeah. So that's the next question that farmers always ask. So if you're a large poultry producer, you got 10 farms, a hundred farms, a thousand farms, first thing that I tell companies is let's just do a historic assessment just to see are there some farms that are always at risk and some farms that are never at risk? And then there's some in the middle that we're probably going to want to keep an eye on. So we've seen that a little. And then what the companies can do are multiple things. If I've got a list of 1% of my farms, 10% of my farms that are a little sketchy based on High Path AI detections, based on our risk analysis, based on the abundance of waterfowl on average around those farms, those are the ones I want to invest in when it comes to operational and physical biosecurity. That's the first level. Now there are some companies, they're starting to use water cannons, lasers, habitat shifting. Those are all things to also consider.
Where I'm really interested in and my pie in the sky idea along with some of the other AgriNerds ... So we're a bunch of engineers, epidemiologists and things like that is like, okay, one laser, those are expensive and it's stationary. So if we really ultimately have some facilities that we know are at risk, we want to protect, those are the ones ... What we could do is we can get a drone and our software could talk to the drone. That drone now can be deployed and it can use noise, it could use lasers, but there's some FAA issues about that. But we can use that to push birds in strategic locations away from our commercial poultry. Depending on the type of drone we use and the density of the farms that drone could be used for dozens of facilities to protect it. The other thing you can do is if our software says, "Hey, you've got birds that are coming to your location right now." Because we can look at directionality, you can have that go to your control panel and your control panel can roll up curtains. It could change ventilation, it could turn on UVC. All these other things that companies are considering that are maybe a little too expensive.
I know some facilities have started fiddling with HVAC systems, but those are, from an energetic perspective, super expensive. But there are ways to turn things on and off based upon what's going on outside. The other thing I'll mention too, I'm from California. We've lost 95% of our natural wetlands. So one of the problems we're dealing with out here is that waterfowl are using what I call suboptimal habitat. They're using dairy lagoons. They're using things that were flooded by humans instead of natural wetlands. What we should be doing is strategically re flooding our wetlands to push waterfowl away from these facilities. We know how to do that. We actually do do that in California, just not in a strategic sense yet. There's always going to be farms that are always in these hotspots. But there are ways to mitigate that overlap, that spatial overlap between wild birds and our dairy facilities, which don't have any physical biosecurity, no fencing, no walls or anything like that in our poultry facility. So we know how to do that, and we need mapping and waterfowl modeling to figure out strategically how to do that.
Greg Schonefeld:
I guess you say a lot of these farms are doing all they can. Well then if they're already doing all they can, what does this do?
Dr. Maurice Pitesky:
I think the challenge is ... And that's a great question. So challenge of, I have two farms and both of them haven't been retrofitted. We don't have concrete floors or our ventilation system hasn't been updated, or we don't have a Dutch entry on both of them. What this does is it allows you to decide how to triage which of those farms you're going to update. So we have one company that was using our technology to figure out where they were going to put vehicle wash stations of their 150 or so farms because they didn't have any of them. They can't put them in all the places. There was another company that was also using it to figure out where to do some habitat shifting. Basically just at a very hyper-local level. They have organic facilities so that a lot of outdoor access, they were figuring out which of their facilities they really wanted to push habitat away from the facility. So I don't think everyone's doing everything they can. It's really hard to do this during an outbreak. In a perfect world, we would've made some of these investments a decade or so ago and started training people to really start thinking broader. Right now, it's very hard to talk to producers and to get on to try to help mitigate these things because they're dealing with all kinds of things, including the largest animal disease outbreak in the history of animal agriculture.
Greg Schonefeld:
That's a really vital point Maurice makes. Producers on one hand want to get smarter, building better systems and using data to guide decisions. But on the other hand, they're in the middle of a crisis. Right now they're managing the largest animal disease outbreak in history, making it incredibly difficult to think long-term while they're focused on surviving in the present. Can you pretty accurately predict then, okay, I'm seeing in my system this man, I think there's probably going to be a pretty good chance we have this or that farm hit. Have you seen that kind of thing?
Dr. Maurice Pitesky:
Yeah. So we have lots of good examples of locations. So first of all, the problem is that in order to protect farmers, the USDA and the state departments of ag won't share data. They'll just share county level data, and in some cases just state level data. So I've made this argument that they're doing a good thing protecting farmers to an illogical conclusion. All that data is siloed at USDA and at the state departments of ag, and no one's really looking at it in a robust level because they're dealing with the outbreak. So that being said, we've got Google Earth, we have all kinds of radar and telemetry models. We have some companies that are participatory in our system through our USDA grant or through their own funding. So yes, we do have examples where a facility had high waterfowl abundance and then also overlapped with counties where we detect High Path AI, where we found farms that popped with High Path AI. You do get those examples.
Now, full disclosure, you get the other scenario too, and our models are more predictive. We actually have ... Not to get too into the weeds here, but our models are about three and a half times more predictive than if you just use the farm data that some other folks will use. So that's good. But you will get places that don't pop with High Path AI, even though our model's saying, my god, this place is a ticking time bomb. My own feeling is like, that's good. So if we have farms, and I'm telling you these farms have a lot of birds around them and there's High Path AI in the area, you got to worry about that. If it doesn't pop, that's great. It just gives us that sense and that warning of what's going on outside the farm.
Greg Schonefeld:
When you're seeing the high risk and then it doesn't pop, does that potentially speak to certain practices by that farm?
Dr. Maurice Pitesky:
So when we look at company data, every company has their own data. One of the challenges is a lot of companies, they don't update their data. So we have a lot of missing data, which is a real problem. But in a general sense, when you look at what's going on outside the farm and what's going on with that operational and physical biosecurity at a company level, you can see things that are going on on the farm predictors and off the farm that are predictive, but it doesn't cross the industry. So if you looked at a turkey facility in the upper Midwest and a broiler facility in the Mid-Atlantic and a layer facility in California on the outside, the waterfowl abundance and High Path AI, that's part of their risk model. That makes sense. And that's universal based on our analysis. But what's going on on the farm that's not as universal. It doesn't triangulate as much.
And part of that's probably because each company collects data slightly differently. A lot of companies don't really ... I always joke with companies that I wish I had the money that you're wasting by not really leveraging all the data that's available nutritionally and things like that. Because the culture, especially at the live side, to not always collect all these type of data and to not make them accessible. But sometimes it's in a big clunky old spreadsheet that hasn't really been looked at for a while and no one knows when those facilities were last inspected and things like that.
Greg Schonefeld:
I do want to get into that. So when it comes to the data, I guess first when I heard you talk about this in the pre-interview, I thought this was specifically related to avian influenza, but I guess you're going beyond that. Maybe this is the question I'm looking for is what would be your vision of data collection? What that ought to look like? Maybe just a list of what you think of farms should be collecting.
Dr. Maurice Pitesky:
In a general sense. The information networks that we have for a disease like avian influenza are not robust in the sense that if a company, just for example, wants to see where High Path AI detections have been in their county and to automate that process, that doesn't exist. If you're in a county that had a High Path AI detection and you've got a thousand farms, you're not going to know, oh, well that county just got hit, USDA's saying. I just got an email or I looked so now what our technology does, if your farms are all over a state or an area, any county and surrounding county that your facilities are in, we send an email alert and then re-rank those farms by risk. But that technology from the USDA doesn't exist. So you would like it to be this scenario where all your farms are somehow you can scrape off any detections of High Path AI and then you know which farms are at risk. We do that. That's very simple. Other folks can do that too at the company level, but even at just the simplest level. That's one challenge. So we're not getting this information dissemination to companies very well.
At the company level there are so many different types of data that companies collect and that they're very, very good at. So nutrition stuff they're usually really good at, because that's the main operating expense. They're also very good at anything going on in the processing plant because you're in a different environment. It's easy to collect those data. They can be summarized to understand how many birds, eggs, all that type of stuff you're producing. Feed conversion ratio, going back to nutrition, we're great at that. The nutritionists are amazing. But when it comes to understanding what's going on in the barn, when biosecurity audits were done, what flooring is there? What ventilation system? When's the last time a vet's been out to that facility? Has anyone walked the barns and what observations have they seen about fencing? Those data just don't really exist, not in a robust way. And ultimately when you look at that physical and operational biosecurity, those data have value in identifying which of your facilities are at greatest risk. I think it's a cultural thing at a certain level, and it's a challenging piece of data to collect, but it's really important that we start creating apps and tools to automate part of this and to facilitate humans that are taking these data.
Greg Schonefeld:
So that data, I guess first and foremost, it would be useful in disease prevention.
Dr. Maurice Pitesky:
I've floated the idea, for example, and I'm not a policy person, but to me when I look at indemnification and how USDA now is starting to require a biosecurity audit to be eligible for indemnification, that's a good first step in my mind. The devil's in the details. But to me, I think indemnification should be tied to sharing those data with researchers. So when they're collecting all this biosecurity data from these facilities, that data needs to be de-identified, which basically protects the farmer and then needs to be shared with researchers. So we can start looking at what's going on on these facilities that are popping versus what's going on in facilities that aren't popping? Is it a hundred percent what's going on outside the facility or is it 30% this 70% that? We don't have information networks that really facilitate that. I think USDA could start nudging industry toward doing that by creating incentives, economic incentives in this case that would encourage more data sharing.
Greg Schonefeld:
Does this get into epidemiology?
Dr. Maurice Pitesky:
Yep. Absolutely. In this case, we can look at thousands of farms and we can look at time and space and waterfowl abundance and High Path AI detections and telemetry analysis to understand when new birds are moving in. So we're aware that there might be new variants of the virus coming in. Those are the kinds of data streams we really need in order to really identify the geography of which places are at risk, the operating challenges of certain places. All those risk factors, we really need robust data to get to. And right now it's siloed by USDA with the goal of protecting farmers, but I think it's having some unintended consequences, unfortunately.
Greg Schonefeld:
So even if your system can detect risk in the event where there is an outbreak, it's still hard to know how it got from point A to point B.
Dr. Maurice Pitesky:
Oh yeah. Absolutely.
Greg Schonefeld:
And maybe this internal data, if it was collected better and organized a certain way, we would just have more points of reference to potentially have a better idea of how exactly this spreads.
Dr. Maurice Pitesky:
Data is fundamental toward understanding what the predictors of any disease outbreak are, and we don't do a very good job of collecting it and sharing it and connecting it to what's going on outside the barn. Now, the stuff I do, we can make, like I said, probably 70% of the US poultry and swine industry, we can have on our model within a month or so. There's ways for all these farmers to really start making historical analysis and forecasted analysis about which of their facilities are at risk. That's the easy part. The hard part is going to be eventually connecting the data better at the company level. I keep seeing companies hire more and more veterinarians, great, love that, but I don't see them always hiring a veterinarian that has expertise in database management, epidemiology, things like that. So those are the folks we need a few more of in my opinion.
Greg Schonefeld:
when it comes to disease prevention, data is everything but right now, the industry isn't doing a great job of collecting, sharing or connecting the dots between what's happening inside a facility and what's happening outside of it. The technology to predict risk is already here, but without better collaboration and epidemiological expertise within companies, we're potentially missing an opportunity to get ahead of these outbreaks.
Dr. Maurice Pitesky:
Most of the veterinarians are focused on pathology or virology or bacteriology, and you don't see many poultry veterinarians, which I find interesting that have a background and or expertise in computer science, software development, engineering, epidemiology. Those are really, really important skill sets, and we just don't have very many of those. I think we need to start not just thinking like we need more vets, but we need to really focus on what do we want a 21st century poultry vet to look like? What skills do they want? What language skills? What software and engineering skills do we want? And I think we're a long ways from that right now.
Greg Schonefeld:
So some of it could be done through education, like you're saying. In educating people on the veterinarian track differently. Could say a vet outfit, just add some of these skills to their outfit? Maybe the same way you've partnered with a radar specialist, is there some way to just partner with certain expertises?
Dr. Maurice Pitesky:
You're a hundred percent right. So we also want to get people that want to go outside of their field. So I actually like being the stupid guy in the room and being around a bunch of engineers and computer scientists and radar biologists and things like that because I think it's really interesting getting those people together. There's multiple ways we can do this. Sometimes we stay in our lane maybe a little too much. But if you have the right personality and you like learning new things, absolutely we can get some vets that maybe don't have that skill level. And to your point, I work with researchers all the time and farmers that say, "Well, I don't exactly understand their technology, but here's how I want to use it." And that's perfect. Then we can start collaborating. They're trying to connect the dots just like we are.
Greg Schonefeld:
At the heart of all this is data accessibility. How to make risk predictions useful for farmers while ensuring privacy. Maurice believes the key is a real-time risk scoring system. One that not only helps individual farms, but also connects data across borders.
Dr. Maurice Pitesky:
I want to make it all accessible. The only thing that stays private is the farm location. So ultimately we create this waterfowl abundance, High Path AI risk score across the US. Farmers put in their farms just like they put in on the weather channel or whatever they use for their weather app, and they can see which of their farms are at highest risk. To me, that seems like a logical step, and it's also a very logical step to tie that in with Canada because Canada is really interested in what's going on now before spring migration. We're really interested in what's going on in Canada before fall migration. I get a little chuckle every time I go to the Canadian or the US websites and there's a line separating our two countries. So from a geopolitical perspective, that line is relevant, but from a waterfowl High Path AI perspective, not relevant at all, and we need to link those two together. That's really fundamental and that's really low hanging fruit that we just haven't done yet.
Greg Schonefeld:
For poultry producers, managing disease risk has always been a challenge, but today, with highly pathogenic avian influence of continuing to spread, the stakes are higher than ever. Maurice's research highlights a crucial reality. Biosecurity isn't just about what happens inside the farm, the landscape surrounding these facilities, the movement of wild birds and the ability to predict disease risk in real time all can play a role in fighting this disease.That's where tools like the Waterfowl Alert Network come in, giving producers a red light green light system to assess their risks day by day. But as Maurice points out, data alone isn't enough. The industry also needs better collaboration, smarter vet training, and a shift in how we think about farm locations relative to high risk areas.
Ultimately, tackling avian influenza isn't about a single silver bullet. It's about layering solutions, mapping, biosecurity, surveillance, and maybe even some creative approaches like using hunting pressure to move birds away from farms. As the virus continues to evolve, so must the industry. Investing in better tools, stronger data sharing, and proactive risk management isn't just about protecting farms today. It's about setting up the industry for the next 10, 20, or 30 years.
A big thank you to Dr. Maurice Pitesky for joining us today. His expertise and ability to break down such a complex issue highlights the critical role data and technology play in disease prevention. We appreciate his insights and look forward to seeing how tools like the Waterfowl Alert Network continue to evolve and shape the future of biosecurity. Be sure to follow Eggheads on Spotify or Apple Podcasts and connect with us on Instagram and LinkedIn. Got a topic idea or want to be a guest? Drop us a message. We'd love to hear from you. Until next time, I'm Greg Schonefeld. One more question for you, Maurice. How do you prefer your eggs?
Dr. Maurice Pitesky:
I do love eggs. So I like a runny egg sandwich with something cheesy and spicy in it in a perfect world.
Greg Schonefeld:
Oh man, so extra over easy on a sandwich.
Dr. Maurice Pitesky:
I think the estimate from CDC is one out of every 10,000 eggs has salmonella enteritidis in it, so I'm willing to take those odds.
Greg Schonefeld:
That sandwich is so good you'll take the risk.
Dr. Maurice Pitesky:
I know.