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Support for this podcast comes from Deloitte. Welcome to The Age of with predict what's possible in the age of with, then translate insight into trustworthy performance. Deloitte brings together end to end offerings with domain and industry insight to drive stronger outcomes through human and machine collaboration. This is Amitay Technology Review. You don't need me to tell you covid-19 changed everything, it's changed the way we work, it's changed the way we school our kids, it's also changed the way we move around.


It's no longer a simple decision to hop a flight or jump on public transit. It's forcing us to get creative with technology and in some cases speeding up innovation. People are trying to use artificial intelligence and all sorts of ways to help make sense of this new world and hopefully restore some normalcy. I'm Jennifer Strong. In this episode, we're heading down into the New York subway because even as life as we knew it ground to a halt, researchers in over 100 cities came together in train cars and sewers, on hospital walls and buttons of ATMs.


They're collecting data for A.I. to help them hunt for covid-19 and other pathogens.


So there's this continual genetic map being generated and it's in snapshots. But if we did it literally daily or weekly, we would be able to see the emergence of new pathogens as they're happening and track them. And so I think we will use this as a a warning system where we could be prepared by taking samples and sequencing the genetics.


Researchers can watch pathogens spread like what happened after a biotech conference in Boston that sent the virus across the state, across the country, across the world. And we know that because of the genetics, this work entails mountains of data, which I can help make more useful by finding patterns, making predictions and maybe spotting future pandemics and ways to treat them earlier. Hmm, well, let's go in machines we trust, I'm listening to a podcast about the automation of everything.


You have reached your destination. In October of 1918, flu pandemic that would be the most severe in modern history was starting to break out in New York, the city's board of health was trying to figure out how to slow the spread and keep businesses open without the Internet and promises of remote work. The constraints were a bit different and a heated debate followed.


Crowded subways were a potential disaster in the making, and about a billion and a half trips were taken on the subway that year, which is about the same as more recent years. They settled on an unusual decision to stagger the operating hours of different types of jobs and businesses in order to relieve crowding on the subway. Textile manufacturing would open at Nine Jewelers at seven 45 wholesalers at eight fifteen. But the subway kept running.


People kept writing and it appeared to work. Ultimately, the city's death toll per capita was much lower than in Philadelphia or Boston.


Fast forward to the present as the coronavirus pandemic rages across the world, the debate over how to best manage the movement of people and goods does, too, along with an assumption that public transit systems are petri dishes for infection and may be largely responsible for the spread of covid-19 in New York City. And if you're wondering what all this has to do with artificial intelligence, it's called microbial surveillance or the tracking of populations of microbes and other things we can't see how predictive can we be looking at fragments of DNA left on the soles of someone's shoes?


You know, the thing about, like, curious scientists in masks and gloves and swabs, what does that have to do with, you know, machine learning or artificial intelligence or mathematical modeling? But it actually is the raw substrate of the data that we've been using to track where a virus is going. What are they becoming resistant to?


Christopher Mason is a professor of genetics and computational biology at Weill Cornell Medicine in New York City and a very curious person looking at DNA and RNA of all kinds.


This type of science uses biological data to train machine learning algorithms and develop models. It requires significant computing power to help make sense out of huge amounts of information.


So what does it take to surveil a pathogen? Well, there's the tracking of changes in microscopic populations detecting mutations and if a treatment exists, figuring out whether a strain is resistant to it. Plus, investigating outbreaks. And in the case of coronavirus, researchers are also hurrying to identify surfaces where it may be active, like a handrail on public transit even before this new reality. Maybe you've wondered what's on those surfaces. Christopher Mason certainly did. He started looking for answers by swabbing subways long before this pandemic.


You want to know, was there?


So there's an innate curiosity that was a part of it. I've also been living in New York now for almost 15 years. And sometimes you touch a railing and it's weirdly moist when you thought it would be dry or it's sticky or, you know, there's always things you find. So there's a curiosity. I also when my daughter got old enough to ride the subway one day, she was kind of licking the pole and she was very young. So I really had this moment of complete parental terror.


But you know what's happened? Something has transpired. Some microbial transmission has certainly just happened. But I just wanted to know what it was back then.


There was almost no information about what was on these surfaces. He founded a community with other like minded researchers, and together they created genetic maps of city transit systems. The groups called up.


So metastable stands for the metagenomics of subways and urban biomes. And the concept is to build this genetic map of the world around us that has previously really just been invisible, started with just a handful of cities. Now it's one hundred and six cities around the world, all profiling, swabbing, cataloging, mapping and then modeling the data we find.


He says this work could help cities better understand the spread and eventually the recovery. So he mobilized researchers from the meta subgroup already on the hunt for pathogens to start looking for the virus.


We realized it was a unique opportunity to leverage an existing network of scientists, clinicians, city planners, epidemiologists and other researchers to say, OK, well, let's set out a uniform protocol.


And as the outbreak is ramping up again, sampling in South Korea, looking in Poland, looking in the U.K., across the United States, looking in Brazil and Nigeria, looking, you know, anywhere we could, that anyone who is ready to go and it could still get out of their labs and their houses would be back and say, I'm where I'm based in New York City, where those first samples were collected.


And on March 17th, five days after Broadway turned off the lights, I went hunting for the coronavirus with researchers in Times Square and.


Before the pandemic, I passed through this station several times a week, that sound you hear is a man with a tambourine and a harmonica and a regular part of the ambience. I'm here with David Denko, a PhD student in Christopher Mason's lab. And we're making our way from the platform to a spot in the station just off a major walkway. Purely a proactive concern, right? Ideally, we would want to sample the busiest area, but you can't just sit there and swab and block all the people.


So we're right off of that. And we sample three sites here. We sample a floor sample, a handwriting sample and a column sample. And the idea is to map sort of as you go higher up in the station, are we going to find more things on the ground? We're going to find more things on the handrail or we're going to find more things in the column. We expect to pick up slightly different things. That's scratching sound is him swapping at the time of this taping, researchers were only sampling Times Square and Grand Central Station.


Penn Station beneath Madison Square Garden came later and now testing is taking place in 10 different stations with researchers swabbing the turnstiles and kiosks to OK, just to kind of describe the scene, he's swabbing this really filthy floor. But we want to emphasize that what we pick up with microbes is not the same as what most people would consider to be dirty. What we consider dust of humans is not is not really alive for the most part. It's not a very technical set up, long swabs that resemble giant cutups, a wooden block with some strategically placed nails to help prop them up, gloves to protect the samples from anything living on his hands, a duffel bag to hold it all, four more swabs or swabs and doing a handrail now and patients to get anything at all off of the subway surface.


We have to really just do this for a really long time and cover a pretty wide surface area. And even then we don't get so all the time. Machine learning is used to more accurately identify the genetics. It can help pick out coronavirus from other viruses and bacteria picked up by the swab from human hands, rodents or whatever else you would expect to find on the surfaces of a New York subway. I can also be used to figure out mutations and where geographically a pathogen came from.


So what did they find in those samples we took? We find out right after the break. Support for this podcast comes from Deloitte at Deloitte. We believe the age of width is upon us. What's happening around us? Shared data, digital assistance, cloud platforms, connected devices. It's not about people versus A.I. It's about the potential for people to collaborate with A.I. to discover and solve complex problems. But for organisations to enable innovation and break boundaries, they must harness this power responsibly.


Deloitte Trustworthy, a framework, is a common language for leaders and organisations to articulate the responsible use of A.I. across internal and external stakeholders prepared to turn possibility into performance.


To take a closer look at the Deloitte Trustworthy Framework, visit Deloitte Dotcom Slash U.S. Trust EHI. Deloitte has no influence over the content of this podcast. Basically, our first batch of several dozen samples didn't show any covid-19 virus.


Once again, Christopher Mason from Weill Cornell Medicine. So I can't yet say which is the safest route in terms of transit, except to say that any mode of transit where you can keep your distance is the best. We do see flu. We see other variations of things like rhinoviruses. We do see other winter related infectious respiratory viruses. So we can see that things are there. But the coronavirus is relatively wimpy virus once it lands on a surface. You know, there's been these reports that the virus can survive for many days.


In some cases, if one or two days, as much as seven or eight days on different kinds of services. But in our testing, we do is called infectivity studies.


In other words, his team looks for microbes, but then also tests whether what they find can actually make somebody sick.


You need to have not only the virus, which has the genetic information, but wrapped up inside the nucleus capsid. So it has all the components of how it can get into your cells, like the spike protein. People heard about all the functional essentially proteins that comprise this infectious virus particle. And if it doesn't have everything there, it can't be. In fact, you actually, which is a bit of good news. And also the MTA has been really ramping up the cleaning people have been wearing masks.


Morsal, all these things help.


From what we've seen so far, there's no easy way to do this kind of testing. You take a sample and see how fast it grows and infect cells. And this has to be done in a particular kind of lab that's safe for this work. Otherwise, you run the risk of making a whole bunch of infectious viruses. They're also mapping out microscopic life in medical environments. And that, perhaps not surprisingly, is where his group is finding this virus in hospitals.


And what's interesting is there we do see some cases, 50, 60 percent of those areas. When you sample them, you can actually see it there. And that's not surprising because it's obviously the medical environment where patients are sick and where they're coming in, getting testing obviously ill. And there it's been really striking, really distinct is that you can see in some cases all over the room.


There have been other surprises with this work, too, like that cities share a core urban microbiome or a set of several dozen species of microbes that appear in 95 percent of all the samples taken anywhere in the world.


And while he knew he'd find the kinds of bacteria normally on people's skin and airways, it's a much smaller part of the picture than anyone expected.


We found that about on average, half of the DNA doesn't match any known species. Never been seen before until we sequenced that DNA. And that was striking because we thought maybe it'd be five percent, maybe 20 percent. But it really depends on where you look.


You'll find anywhere from half to even 80 or 90 percent of the DNA is from some things we've never seen before, even though they're literally under our fingertips, we've never actually catalogued them to see what's there until recently, he says this kind of surveillance was viewed as an expensive and labor intensive way to look for things like bioterrorism and antibiotic resistance.


But in the future, I think everyone's appetite for continual surveillance as a means of public safety will probably become much more appreciated and even standard. We all take it for granted that there is a continual mapping and monitoring of any storm that's rising up in the Atlantic Ocean because we want to know what's coming. We want to know if there's any risk that we should prepare for. We no longer have to be subject to the fancies of the universe. We can actually be predictive.


And so I think a really simple analogy would be we started doing sampling of sewage as well with all the meta subsidies. We're doing sewage, we're doing the air, we're doing the cities, doing the hospitals. So there's this continual genetic map being generated and it's in snapshots. But if we did it literally daily or weekly, we would be able to see the emergence of new pathogens, you know, as they're happening and track them.


And so I think we will use this as a a warning system where we could be prepared, like with that hurricane example, proper planning and tracking could save lives by helping us be better prepared for future pandemics rather than just react to outbreaks.


We're also attempting to use I and a whole host of other ways in this pandemic. We're trying to have it do things like listen to coughs and diagnose patients, let people know when they've been exposed to covid and supercharge the hunt for drugs that might prevent or treat it.


Someone needed to become the Google of biomedical information.


Baroness Joanna Shields is the CEO of Benevolent dayI. The company wants to change how medicines are discovered and brought to market. She worked at Google in the early days when they were creating algorithms to help people search the Internet. She also ran Facebook in Europe. This is about how do you curate all the world's relevant information? To create an environment for scientists, to innovate and to come up with new discoveries, so far this year, more than fifty thousand papers have been published about covid-19 alone.


No scientist could possibly read the thousands of journals that are produced every day, if not, you know, and tens of thousands. And you can't keep up with any field. So what we aim to do is by developing natural language processing algorithms to give them those tools to enable them to identify things that wouldn't wouldn't necessarily be obvious to the human brain.


Benevolent dayI uses machine learning to sort through vast amounts of medical literature and find patterns that doctors and researchers would likely miss. It then maps those relationships into a knowledge graph.


Imagine the kind of graph you draw to map out your relationships on Facebook, but for the connection between viruses, drugs and proteins in the knowledge graph, we have 24 different biomedical entities that we pull together, enable scientists to visually see how those entities interact, you know, genes and proteins and how they interact and what what gene is up regulated in that disease and what processes dysregulated in the body that causes that disease to happen, and understanding the underlying cause of disease that enables the scientists to develop a treatment that will work and be much more effective.


So in covid-19 hit the Buzzi London based company put its tools to use.


The challenge that our scientists took on is how do we look at the existing treatments that are out there? And if there's any way that we'll be able to make an impact or lessen the severity of the disease in a patient. I called one of our leading scientists and I said, Peter, what? What can we do? And our graph is not optimized for infectious diseases. You know, infectious diseases is a completely different discipline. So I wasn't I didn't have extremely high hopes that he would be able to find something immediately.


He said, I've been working on that all weekend.


They combed through scientific papers on the virus and added what's known about how it hijacks the body's proteins. Then they sifted through existing drugs to see if any of them might stop that hijacking process and help reduce the severity of covid symptoms. Those drugs still have to go through clinical trials to prove they can actually address covid, but they can pass through the safety phase much faster than brand new drugs. The team were able to identify a drug that that is currently being used for rheumatoid arthritis.


We think that could potentially be a treatment. We've published in The Lancet that our research has led us to this, but obviously with the caveat that we need to do the testing.


The drug she's talking about is Barisich from Eli Lilly. It's now in phase three trials. Benevolent Eye isn't the only company working on this problem, even before the pandemic, researchers started applying machine learning to the drug discovery process in hopes of speeding it up, covid just gave it more urgency. But this work is still incredibly hard. After years of research and development and raising hundreds of millions of dollars, they haven't announced any existing drugs that have passed trials for new diseases.


As far as we know, no other teams have either. There's about 10000 diseases that don't have treatments.


So there's so much work to be done. It's almost when you think of competition in this area, I think excellent. Please, I hope you're having great success because, you know, there's so many people suffering from disease and there's over 300 million people suffering from rare disease that unless we change dramatically the economic model or we can increase the efficacy or we can reduce the time it takes to get drugs to market, we're not going to be able to address this.


So you need technology to make a big impact along the way so that we can work on diseases that may only have a few thousand patients.


Next episode, how should a self-driving car tell you when it isn't driving anymore, the notification to let you know that the Tesla no longer has control his four beeps and we thought it doesn't seem like enough information considering the neural processing and stuff that we go through when we're driving a car.


And we started to look at what does this mean? How do you trust what's in front of you and how can we help the humans in the car?


We'll also find out how Google's self-driving unit WAMMO composes the sounds for its cars in a particular key, hoping to help us relax.


This episode was reported and produced by me, Tate, Ryan Mosley, Emma Silicon's and Karen How We Had Help from Benji Rosen were edited by Michael Riley and Gideon Lichfield, our technical director is Jacob Gorsky. Thanks for listening. I'm Jennifer Strong. This is Amitay Technology Review.