Science Friday - COVID-19 By The Numbers, 1918 Flu. May 1, 2020, Part 1

Episode Date: May 1, 2020

Navigating COVID-19 By The Numbers Ever since the first news about a new virus in China, we’ve been seeing projections, or models predicting how it might spread. But how are those models created? There’s a lot of math that goes into understanding what might come next. Ira turns to a group of scientists who make their living in crunching the numbers—the people who make mathematical models to approximate different scenarios, trying to minimize loss of life. Sarah Cobey from the University of Chicago and Jeffrey Shaman from Columbia University share their work on the past, present and future of coronavirus spread, and explain how to understand the many models all trying to bring clarity to this very difficult pandemic. A Pandemic Precedent—Set in 1918 In the spring of 1918, a new and virulent flu strain was documented at a military base in Kansas. Within weeks it had been observed in Queens, New York—and soon, spread all over the globe. By the time the flu petered out a year later, the world had suffered three distinct waves, killing somewhere between 17 and 50 million people, and heaping a fresh disaster atop the losses of World War I.  How well does the present resemble history—and are we at risk of repeating the staggering toll of the 1918 flu? Historian Catharine Arnold talks to Ira about stories from the past, and the events and choices that drove additional waves of infection and death. Plus, Science Diction host Johanna Mayer on why the 1918 flu wasn’t really ‘Spanish’ at all. Look through images taken during the 1918 flu, from the U.S. National Archives, in a gallery article. Strokes In COVID-19 Patients, Plus Trauma In Healthcare Workers This week, a group of researchers observed five younger patients under the age of fifty that suffered from strokes. These patients either were asymptomatic or had mild symptoms. Their results were published online in a letter in the New England Journal of Medicine. Reporter Sophie Bushwick talks about this story, plus the trauma that frontline healthcare workers face during the pandemic, and other new research from the week. Erosion Threatens A Unique Ecosystem Indiana’s Lake Michigan shoreline is one of the most biodiverse places in the country. But that biodiversity is now washing away. Rebecca Thiele, energy and environment reporter at Indiana Public Broadcasting, unpacks the story.  Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.

This is Science Friday. I'm Ira Flato. This week, the CDC added six new COVID-19 symptoms to its official list. The virus is creating havoc in the body with new effects appearing that doctors are still trying to understand, like strokes in younger patients who all had the virus. A group of scientists published their observations this week in an online letter in the New England Journal of Medicine. Sophie Bushwick is here to fill us in on that's. story and other science headlines. She's technology editor at Scientific American. Welcome back, Sophie. Thanks, Ira. Let's talk about this. What are the doctors seeing in these patients? So some doctors in the New York area had found that they were seeing people under the age of 50 coming in with strokes at a much higher rate than usual. So they said that during a typical two-week period,

they would see on average maybe 0.7 people in that age group coming in with strokes. And more recently in a two-week period, they had five different patients coming in. And all of them were also tested positive for COVID-19. So they think that strokes now could be a symptom of this disease and that it could also appear in patients whose other symptoms appear to be relatively mild. So this could be related to blood clots. They've noticed that a lot of COVID-19 patients seem to have more clotting going on, and that could be causing these strokes. And if you've got blood clots, it could be causing other stuff that we really don't even know about yet, perhaps.

Oh, that's right. They've found that blood clots are causing kidney issues and that there's also been cases of loss of circulation to limbs because blood clots getting caught there as well. Researchers released results of a trial of the drug remdesivir and the effects were limited, reducing recovery. time in patients. Another study came out this week in nature looking at repurposing an entire catalog of existing drugs, right? They're going through the drugs now, seeing what might work, right? Right. So this is really, really helpful because there are many drugs that have been tested in humans to treat completely different diseases and issues, but because they've been tested like this, we know about their side effects and we know whether they're potentially toxic and what doses are safe. And so that makes them good candidates for testing against COVID-19. But you don't

just want to throw everything at it and see what works. So what they've done is they've looked at proteins in the human cell that the novel coronavirus is using in order to take control of our cells. And they've said, what drugs target these particular proteins? And so based on this, they found 47 different drugs that they tested. And of those, they winnowd, it down to 10 different candidates that might be useful for attacking the novel coronavirus and that they might want to continue further testing. So what are the next steps for the drugs that have been identified as possible candidates? So so far they've only really tested them in a kind of a petri-ish type situation. So they're going to want to test them in humans. And the other thing is that some of these

drugs are already being used in clinical trials. Some of them have been FDA approved. But the ones that are still in clinical trials, they'll want to make it. make sure that there's not any side effects that could be even more damaging. So, for example, the drug hydroxychloroquine has gotten a lot of hype as a treatment, but it also can have really bad side effects and can damage the heart. So when you test drugs like this, you need to make sure that you're not hurting your patient with the drug itself. There was a tragic story about a New York City emergency room doctor who died by suicide, Dr. Lorna Breen. We're reaching about the second month of the quarantine here in the U.S., and health care workers have been working through this for

just as long. Your next story looks at the mental health impacts of those frontline workers, doesn't it? That's right. The journalist Gillian Mock wrote a story for Scientific American about the psychological trauma that health care workers are going through right now, and the fact that this might have repercussions for them even after the worst of the pandemic is passed. So a lot of ER doctors and nurses will say that everyone has bad days. But what's happening now in places that are overwhelmed, every day is a bad day. And they're just being exposed to all kinds of stresses that normally they would have time to decompress from. But because of the volume of COVID-19 patients, they're not able to do that. And so the worry is that during this crisis and afterward,

that there are going to be problems like depression and anxiety and insomnia that are going to be further damaging the people who are in the front line against the virus. Moving on, you have a story out looking at a new type of footstep sensor. Tell me about that. So this is really cool. Apparently everyone has a unique way of walking, and you can actually use that to identify people the way you use a fingerprint or a face ID. And research, Researchers have studied this before using wearable sensors like Fitbits or pressure mats that you have to walk across on the floor. And that's great, but you can't really implement that in a widespread way. So this new line of research is developing sensors that just sit on the floor and they sense vibrations coming through the floor and the building itself.

And they can use that to track specific individuals and to identify them. And even the researchers say to identify health problems they might be having. This would be, it sounds like something that you would have for the elderly, you know, trying to see how well they're getting around. That's exactly what the researchers suggest. I mean, they point out that many people who want to live independently but are at risk for falls or for health problems. You know, they don't want to have a camera sitting in the corner of their home. They might not want to wear a sensor or, you know, a bracelet or a necklace that senses their steps. But if you could have a footstep sensor in their home, you could pick up when someone starts walking off their. balance in a way that could indicate something like a stroke, for example, or you could sense

when they take a fall that might need immediate treatment. And even in some cases might be able to tell when they are going to fall, when they're off balance in a way that could be dangerous. Well, it wouldn't be Science Friday without us talking about a dinosaur discovery. A new dinosaur fossil discovery, Sophie? That's right. The spinosaurus is a swimming dinosaur. And this is really cool because researchers knew about the spinosaurus for a while now, and they hadn't found enough of its tail, though. And so what happened most recently is they found a few years ago a fossil that its tail was about 80% complete. And they found that this tail is shaped like a paddle and that it was flexible so it could kind of undulate through the water like an eel. And before this, they thought that

this dinosaur ate fish because of the shape of its teeth. But they thought maybe it's like a grizzly bear, standing at the edge of the water and catching fish out of it. But because of this paddle-like tail, they think this dinosaur actually liked swimming and it could move through the water very comfortably. So its tail is probably not like a fish, but more like a crocodile or something. It actually, it's shaped kind of like a paddle turned on its side,

and then it could kind of like wiggle back and forth from side to side. But yeah, like you say, it wouldn't have had the skin of a fish over it. That is cool. Thank you, Sophie. Thanks, Ira. Sophie Bushwick. is technology editor at Scientific American. And now it's time to check in on the state of science.

This is KERNO. St. Louis Public Radio News. Iowa Public Radio News. Local science stories of national significance. Indiana's Lake Michigan Shoreline is a boon for tourism in the Midwestern state, but due to climate change, shoreline erosion is increasing, creating problems for homeowners, beachgoers, and infrastructure. Joining me to talk about this story is Rebecca Thiel, Energy and Environment Reporter for Indiana Public Broadcasting in Bloomington, Indiana.

Welcome to Science Friday. Thanks, Ira. So tell us what's going on there. I paint us a picture of how bad this erosion problem has become. So you have homes along Lake Michigan that are just feet from the water. Those people who have seawalls, the water is washing over those seawalls and washing out the soil behind the wall. Wow. And most importantly, you know, that erosion is getting so close to these homes that it's threatening water and gas lines. So, you know, if these people don't have water, if they don't have heat, they're probably not going to be able to use those homes anymore. Now, tell us for people who don't live there, why is the water so high? Is this an unusual thing? Is it climate change? What's going on there?

So there are a couple of different factors, but one thing that we know is that climate change is having an effect on the Great Lakes. We're seeing. more extreme high lake levels, and, you know, just a few years prior, say about 2011, extreme low levels. So it's that variability, that fluctuation, and then also you couple that with the fact that we don't really have that ice shelf that we see in the winter. So those really big storms on the lake in the winter, those are just directly hitting those beaches, those dunes, and then those homes. What have residents said about this issue? What do they have to say?

I think a lot of the residents have recognized that that ice is gone, certainly. I think that that's a big thing for them. I think that really, you know, in terms of what they can do, some are looking into building up their protections like seawalls, but a lot of them feel pretty trapped and unable to do a lot about this. And what are the people doing? to remedy this issue locally. So, you know, as I said, some people are looking into more protections like sea walls, like what they

call stone toe protection. So those are those stones that you might see along a pier or something like that. Others have looked into what they call beach nourishment, which is where you put sand directly on the beach. And that's a little bit of a more, quote unquote, natural way to try and control erosion. But it's very expensive. Beverly Shores, for example, the community that I talk about in the story, they tried to do that and the sand washed away just a few days later. And, you know, that's really heartbreaking for those communities. Yeah, that's something I'm familiar with coming from a beach community nearby. That's what happens. It giveth and taketh away when the water comes with sand. Right. I should say also that, you know, I did talk with a professor of geosophiles. who said that really the best long-term solution is to move away from the lake.

But, you know, of course, that's a really hard sell for these communities. And are they getting any federal help, you know, sort of a disaster relief or something like that? Not right now. A lot of these individual towns have declared their own emergencies, but they really want the state to declare an emergency to open them up to that federal funding. Tell me about the Indiana Dunes area. I understand it has a unique ecosystem. Yeah, so it's one of the most biodiverse places in the country.

It's actually really neat. You can see cold weather plants like the Arctic bearberry plant next to a plant you would find in the desert, like the prickly pear cactus. And, you know, according to the National Parks Conservation Association, there are 90 different endangered species of plants. So this is a really special, rare kind of ecosystem, these freshwater dunes along the Great Lakes. Thank you for taking time to tell us the news about the lakes, Rebecca. Thank you, Ira. Rebecca Thiel is an energy and environment reporter for Indiana Public Broadcasting in Bloomington, Indiana. After the break, what can history teach us about global pandemics?

An historian on how the 1918 flu pandemic resembles COVID-19. Stay with us. This is Science Friday. I'm Ira Flato. In March of 1918, soldiers at a military base in Haskell, Kansas began dying of an unusually deadly strain of influenza. A week later, the same illness was reported in Queens, New York, and quickly proliferated into the worst global pandemic since the Black death, killing tens of millions of people. This strain of H1N1 flu virus

would strike in three separate deadly waves around the world before petering out in the summer of 2019. We're going to dig deeper into that horrific pandemic. and its relevance today. But a good place to start is, well, let's clear something up. The 1918 pandemic was known as the Spanish flu, except it really wasn't Spanish at all. Johanna Mayer is the host of Science Diction, our podcast about words and the science stories behind them.

She's here to explain where the misnomer came from. Welcome back, Johanna. Thanks, Ira. Well, let's talk about this. Give us the story behind this. Well, remember, the 1918 pandemic, happened smack in the middle of World War I. So troops were moving around. That did not help things because movement of troops obviously means movement of the virus. It's kind of a nightmare.

So Germany, the U.S., Britain, they were all seeing really horrific outbreaks of this flu. But they did not want their enemies to know about that, like morale, the appearance of troops dropping like flies, weakness, not something that you want to let your enemies. in on. But one country that didn't have to worry about any of that was Spain, because Spain was neutral in World War I. So when the virus hit Spain and even the king there got sick, the Spanish press did what basically any newspaper would do under normal circumstances, which is that they reported on this outbreak. And Spain was simply the first country to regularly and consistently report about this flu and they got stuck with the name Spanish flu and it stuck.

But the thing is, researchers aren't totally sure where the flu actually originated. I think the jury's really still out on that. Some people say France. Others say the American Midwest. Some say China. But one thing that they're pretty certain about is that it was not Spain. So I'm curious about what they called it in Spain. Good question. A bunch of places around the world had their own probably inaccurate names for this outbreak. So in Madrid, they called it the Naples soldier. But in Poland, they called it the Bolshevik disease. In Germany, they called it the Flanders fever. And in other places, it was sometimes referred to as the German germs. So basically, everyone had their favorite scapegoat for this outbreak. That's terrific. It's great that you've cleared all of that up.

Thank you for dropping by to dispense those pearls of wisdom. Thanks, Ira. Johanna Mayer is the host of Science Diction, our new podcast that unearths the surprising origins of words and phrases in common use. And her newest episode is all about the 1918 pandemic, this so-called Spanish flu. You can find it wherever you get your podcasts. And now on to the rest of the story. As I said before, the 1918 pandemic killed millions. It tore through the globe in record time, left people wearing masks and social distancing.

Well, that feels kind of familiar, right? So is there something we can learn from that disaster? Or is the parallel to the present just skin deep? My next guest is an historian, Catherine Arnold, author of the book Pandemic, 1918. Eyewitness accounts from the greatest medical holocaust in modern history. She joins me from Nottingham in England. Welcome to Science Friday. Thank you very much.

We were just talking about how World War I was pit, in the naming of the 1918 pandemic. But how did it contribute to the spread of the virus? I think the Great War, as they called it in those days, made an enormous impact. The Spanish flu would eventually have spread around the world in any case, just as a black death did and the plague in the 17th century. The difference was that all the troop movements turned the globe into a giant petri dish. So there wasn't one continent, one area of the globe that went unvisited.

by a Spanish flu, or as they sometimes fancifully called it, the Spanish lady. So how fast did it actually spread around the globe? The big spread really came with a second wave in May June of 1918. Before that, a strange new infection had sprung up, as was mentioned earlier, in the army camps, and in some parts of the United States. And then it mysteriously disappeared only to return. turn in a second wave, far more invigorated and deadly towards the end of the summer of 1918. Was any one country hit the hardest by the flu? Well, as I say, it's spread around the

globe and certainly the continental areas of India and Africa suffered particularly hard because of the levels of poverty there and the subsistence levels of living. However, the US and Canada were also hit pretty hard. Was there any logic to which countries were hit the hardest? Yes, on an international basis, I would say, and so would many other researchers in the field, that the countries which were hit the hardest were the poorest, the ones that were struggling economically that had very, very few resources to cope with the impact of a pandemic on this scale, very few medical resources and a population already riddled with deprivation and

disease. And what kinds of measures seem to be the most successful in containing it? The most successful measures in containing it were almost identical to the ones being used today, and that is quarantine. And they didn't have the term social distancing in those days. It had not yet been invented, but the concept was there. For instance, in some parts of the United States, citizens were asked to undergo quarantine measures. Schools were closed, pool halls, cinemas and theatres. But strangely, some large gatherings such as Liberty Bond drives and victory parades still went ahead. As you mentioned, there was the second wave of the flu.

It wasn't just one hit, but it actually got worse in the summer of 1918. Can you tell us why did that happen as far as the historians understand? There are various explanations for this. I've never been able to come down with a tangible one. My best one, my best guess would be that in previous waves of flu, which have gone in waves of one, two, three, the second wave has always been stronger and more deadlier than the last. This may be because some citizens had already acquired immunity, but the virus evolved and picked off the youngest and the fittest. The most noticeable thing about Spanish flu as opposed to coronavirus, which is more of a pneumonia, the Spanish flu attacked the young and the fit. And the younger

and the fitter you were, for instance, if you're a young soldier or a pregnant mother, the more likely you were to become a victim. This is because Spanish flu set up an autoimmune reaction and as your body fought off the virus, it quite often would kill you too. Wow, that is amazing. I think most people didn't know that. It seemed that most deaths were occurring at the age of 28? Correct. And of course, being flu, being a highly contagious disease, it flourished in barracks and prisons, and it flourished in urban areas with the highest level of the highest index of deprivation.

places where people were crowded together in squalid conditions with few resources in substandard housing. So when I started this, I thought that Spanish flu would be pretty egalitarian in its approach to killing. But the more I researched, the more I realized that it was the poor and the sick who suffered most. But it eventually petered out. It went away. Do we know why that happened? The most compelling explanation for the way that it kind of petered out would be a thing they call herd immunity, which is the idea that the more people become infected, the weaker the virus grows. Although herd immunity has a bad rep at the moment because it's not, to my mind, the best way of containing the current outbreak of pandemic, herd immunity probably accounts for the way that Spanish flu petered out.

As it became widespread, its impact became lessened, it became weaker. We are seeing some sort of pushback by some groups who are against wearing masks and social distancing. Did that happen also with people in the 1918 pandemic? Indeed, there were protests. against quarantining, social distancing and mask wearing. In San Francisco, an organisation developed called the Anti-Mask League. And this was a bunch of physicians, public health experts, skeptics and cranks, who believed that masks were not necessary and in fact might actually be dilatious.

The Anti-Mask League developed after the Armistice. It developed with the least talked about third wave of flu which hit San Francisco in January 1919. And the mayor, Mr. Rolfe and the chief officer of health, Haskell, said, right, masks back on people. We need these masks because they will keep the death rate down. So the anti-mask league not only refused to wear masks and said that they were unconstitutional and flew in the face of civil liberties. but one person who was just named himself John sent an improvised explosive device to the government offices saying here's a present from me. It was composed of an alarm clock, various bits of shrapnel and springs and if it had gone off it would have killed many, many people. Fortunately it was defused.

But this was some indication of the strength of feeling about being forced to wear masks. In the end, happened in the interest of democracy is that a petition was submitted to the local council and the council ruled that yes, it was no longer mandatory to wear masks. This was around about January 1919. Mask came off. People got sicker. So it proved that there was some validity in wearing masks after all. So there really is, and there are parallels between that pandemic and what we're going through now in terms of social reaction to it?

Oh, yes, I would say so. And obviously, having done all this research and published in the field, I was quite astonished when I saw the footage of protesters taking to the streets in the US with the give me liberty or give me death slogans, where it's very easy to think, well, actually, if you take your masks off, death is just what you might get. So it's strange to me. But I guess everybody sort of feels that they have their own rights to consider.

And perhaps understandable, given the trauma that we're all living through, the uncertainty. I'm Ira Flato, and this is Science Friday from WNYC Studios. Talking to historian Catherine Arnold about the pandemic of 1918. Interesting that a full third of the globe in that 1918 pandemic is thought to have been infected, do you think we should be worrying that we might see this kind of history repeated? Well, there are a number of explanations for this. I think that we will probably endure further outbreaks of coronavirus. In the Far East, regular outbreaks of killer flu have become such a feature of daily life that it's never surprising to see people say in mainland China wearing masks.

I don't think this is a one-off. I think it will become a feature of our daily life just the way that sadly terrorism has done. But I don't think that we will suffer quite the mortality rate that they did in 1918. If only for the reason that we are a bit more prepared now, we have more resources. And the majority of people seem to be taking on the slogans and doing the social distancing and staying home and realizing that they have an obligation, a moral right,

not just to look after themselves, but to preserve the lives of those around them. Do historians view this as a really seminal event in their occupation? And are they taking special care to preserve records or to keep track of the event as they're unfolding? I believe so. Yes, I certainly am. I think overall, the great question is, how did this start? You know, who let this out? Where did it come from? and people will want those questions answered. And it's also going to have an impact on research as well.

After Spanish flu, after World War II, there was an increase in research into infectious diseases and viruses of this sort. Well, that's going to redouble now because there'll be a desire also to develop a vaccine. When your book first came out a few years ago, you were asked about the legacy of the 1918 pandemic, what it was. What did you say then, and how would you edit that now? Well, one thing I was asked to do, making a TV program, was basically to Shroudwave. The producer wanted me to say that there would be another pandemic on the scale of Spanish flu,

or at least the biggest thing since Spanish flu. At the time, I was very cautious because I wanted to seem, I wanted to maintain a certain historical detachment. Now I can see that the pandemic has been worse than anything we could have envisaged. But sadly, part of this is because in many, many Western countries, the preparedness was not there, the resources were not there to cope with, an outbreak of pandemic pneumonia on this scale. And that's for reasons of governmental decisions and understaffing and under resources. Before you go, I want you to tell me a little bit more about the story of the Manchester public parade that they had there

where health officials were warning against letting people congregate, scientists were saying, don't do it, and they went ahead and had this big parade anyhow. That's absolutely correct. On Armistice Night, on November 11th, 1918, The medical officer of health told the council in Manchester, whatever you do, you must avoid mass gatherings to celebrate armistice, because this will result in a spike in the death rate.

The council ignored him, although they'd taken his advice in the past and it had saved lives. So thousands of people poured into Albert Square in Manchester to celebrate the armistice. And a week later, the death rate had soared. and already 300 people were dead. And the Manchester evening news said, this is one of the greatest disasters that's ever befallen Manchester. And the saddest thing of all is that Dr James Niven,

the Chief Medical Officer of Health, who had told the Council not to allow this to go ahead, later killed himself. And it's always been thought that he felt that he'd failed all those thousands of people who contracted it later on and died. What a lesson for the ages, and hopefully history not repeating itself. I want to thank you very much, Catherine, for taking time to be with us today.

Well, thank you for inviting me. I've had a really interesting time. Thank you. You're welcome, Catherine Arnold, a historian, and the author of Pandemic, 1918. She joined us from Nottingham, England. After the break, we go from the past to the future, how scientific models turn to data into possible outcomes, and what they can tell us about how the coronavirus may spread, all depending on the choices we make now. This is Science Friday. I'm Iroflato. Just one quick note. We're trying to figure out why people do citizen science or don't. And this means it's research time, so please help us out

at sciencefriety.com slash citizen science. And now, ever since the first words about a new virus spreading around the world reached our ears, we have been seeing projections or models predicting how it would spread, how many people would be infected, you know, those curves we keep talking about flattening. Well, just how are these models created? How do factors like social distancing or staying home change their shapes? And what about the future? The possibility of a second wave of coronavirus. Remember the 1918 flu pandemic we were just discussing? The second wave was everything then. There's a lot of math that goes into understanding what might come out next, so we turn to a group of scientists who make their living in plausible scenarios, people who

actually make the models. How can they help us understand the best course of action? Here to explain are my guest model makers, Dr. Jeffrey Schaeman, Professor of Environmental Health Science and Director of the Climate and Health Program at Columbia University in New York, and Dr. Sarah Kobe, Associate Professor of Ecology and Evolution at the University of Chicago. Welcome both to Science Friday. Thank you for having me. Yeah, glad to be here. You're welcome.

Sarah, let me begin with you. And exactly what is the definition of a model? So I have a fairly philosophical view on this. And I think that any time someone's making some claim about what's happened in the past or what's going to happen in the future, they're modeling. The difference is some people are really careful and, explicit about their assumptions that go into those explanations of what has happened or what's going to happen in the future. And we tend to call ourselves professional modelers when we get really

hung up on those assumptions and especially when we're trying to understand these things in a quantitative way. All right. Let me use a recipe analogy here with the ingredients that goes into baking a cake. What are the ingredients that go into making a model? Well, they can be variable. You know, it depends, I suppose, whether you're making a main course or a dessert. What we sometimes see are models that are built on mathematical equations that try to represent processes, be they physical or chemical or biological. But you also have other models that are built using fuzzy logic or statistics or any number of other methods that you may hear that try to represent the system and to make some sort of deduction or very often inference based off that system in order to better understand it. And then use that information and that understanding possibly to make some sort of deduction or very often inference based off that system in order to better understand it. and then use that information and that understanding possibly to make projections about what might be happening right now or be happening in the future.

Sarah, let me continue with my recipe analogy. If I don't use the finest ingredients, I'm not getting the finest of cake. How does that pertain to a model? Yeah, that's exactly the right analogy. As a professional modeler, I have worked with ingredients are varying quality. And one of the things that is challenging about this situation, especially for modelers, I think in the United States, is that a lot of the data streams that we're working with, you know, we're not really developed with this kind of scenario in mind.

And so we spend a lot of time thinking about maybe more than we would in the kitchen, but a lot of time thinking about how we can compensate, I think, for deficiencies in the data and building that uncertainty into the models so that when we're, you know, presenting some forecast, we can say we know that we were undersampling deaths or definitely missing probably most cases, most of the time, but still giving. given that, you know, we're fairly certain that this is a reasonable outcome or this is a reasonable conclusion. How important is that to get to know as many cases as possible? It's definitely important for understanding, like, what the true fatality ratio or what the, you know,

true virulence of the coronavirus is. It's also very useful for knowing how many people have been infected, which is going to tell you something about, you know, the rate of spread in a population and potentially how well different interventions are working, and especially how close we might be to that, you know, mythical herd immunity. But we have a lot of ways indirectly that we can get at that while not knowing for sure what the asymptomatic fraction is, for instance, or, you know, while not knowing exactly how many of the symptomatic cases are being recorded.

Jeffrey, you're a co-author on work that asked a big question, how many people may have undocumented or undiagnosed cases, of COVID-19 and how responsible those people were for spreading the virus. How do you go about answering that question in a model? So what we do to make this estimation is what we bring in the data. We bring in those confirmed case data, that documented data, and we actually assimilate it into the model that, in effect, educates the model so that it can find the combination of conditions, they're called parameters within the model, that allow it to best represent what actually went on.

Once we have that solution and we can verify that we're able to consistently get the correct definition. But when we apply it to the real data, we get that solution, we then plug those numbers into the model without the algorithm and without the data and see if it can replicate what actually happened, which is what it's able to do. And when we did this, we found that only one in seven persons in China from January 10th to 23rd was being actually documented. The 86% were undocumented. And further, we found that per person, those undocumented infections were on average about half as contagious as the documented infections. But because there were so many more of them, they were actually responsible for the lion's share of the transmission. We also found that there was good evidence of pre-symptomatic shedding amongst those who did become symptomatic and were documented. So there were many routes by which this virus could spread silently or in stealth.

People not knowing that they're contagious, not knowing that they have the disease, We're going out and about in the community, and they're doing what you or I would do if we didn't know we had infection or if we just had a little sniffle or a mild sore throat. We'd still get on public transportation. We still go to work. We still would take business trips. And by doing that, we're facilitating the spread of the virus broadly within the community and allowing it to really take off. Sarah, your work focuses on the rate of spread in the state of Illinois. What are you learning about the rate of spread?

And what are the models saying about the effectiveness of different? interventions? So one of the things that's surprised us is that we're not seeing dramatic differences in the rate of spread between the more urban and rural regions right now in Illinois. And, you know, in some ways, that's actually not so surprising, you know, because there's still a high levels of mobility, you know, within smaller towns and between towns. We generally expect cities to be the primary places where, you know, a pathogen is going to be introduced. But, you know, we're seeing in Illinois that there's no place that's obviously very far behind. And we're also seeing a really nice impact, as we would hope, because we're all, you know, suffering under stay-at-home orders right

now. So we're seeing a very nice impact of those interventions and a large reduction in the reproductive number, which probably at least have, probably actually it's gone down even more than that. But we're also still not quite out of the woods. So I think we have continued shelter in place through the end of May, I think that's probably a good idea. And what kind of variables could influence the answers that you're getting? We're spending a lot of time, again, thinking about how well cases and hospitalizations and deaths are reported in different regions. So that right now is probably our biggest source of uncertainty and something that we're focused on primarily. But there are some general questions that I think

modelers all over the world are grappling with that have a huge. impact on policy going forward. For instance, you know, how important are children for, you know, spreading the disease and how susceptible are they? And, you know, just how do different age groups and different settings contribute to transmission? That's the sort of thing that right now we can't really, like, deconvolve or pull out of our models, but will be extremely important to understand going forward. When we look at the different curves of infection in different countries or regions, New Zealand, Italy, the U.S. Can the models explain why they are so different?

We can certainly estimate differences in how aggressively it's moving through the population. There are some differences due to population density, though they're not maybe as large as people might think. Certainly in a place like New York City and particularly in Manhattan, where you have lots of people on top of each other and many, many contacts you would expect a more aggressive growth of the virus and strong. expenditure growth early on, potentially, than you would in some place like rural Texas. But as you move across cultures and you move to different societies, you're running into multiple issues that can make it actually difficult to really estimate what's going on. Certainly, there are components of the country where it may be introduced and not in others. So for instance,

in Italy, it was in Lombardia where they got most of their infections initially. That was the real epicenter of activity for that country. So it may be worthwhile considering what they have done there and what the population density and characteristics, what the demographics are, how many elderly people they have. Italy has a very aged population. The other things, of course, are that we need to get a sense of what's going on in terms of policy and compliance with policy and response. And we've had enormous variability from country to country and what they're doing. New Zealand was very proactive. Sweden has chosen to opt to try for herd immunity for the most part and let the virus essentially roll over them and steamroll them.

We have some natural experiments going on. Certainly the models can tell us what does this mean the characteristics of the virus over time? What happens to that basic reproductive number? Does it drop off when time because of social distancing practices that we know are in place? Sarah, earlier I rattled off a few models that people might have seen media coverage. How are we who are really not trained to study curves and modeling? How are we supposed to digest these numbers? And if they're the result of math, what kind of trust should we put in there?

That's a really good question. Because, you're right, there are a lot of models that are in development right now, and that's a great thing, but I can see how also it's a little awful to watch if this isn't the sort of thing you're used to thinking about all the time. I think the good ones are going to be grounded in some understanding of the biological processes. So they're going to be transmission models. you know, I think the first thing to do is just to look at what some of the basic assumptions in the model are and, you know, there should be some sort of biological motivation for them.

So I think those are the models to trust when you're thinking about forecasts, but of course those models are forecasting very different things. And what's happening here really is there's so much uncertainty that all of the modelers are dealing with right now. We have uncertainty in the underlying biology, like the role of children. We have uncertainty in the specific transmission rates or parameters in different regions. And then again, we have uncertainty and the quality of the data that we're working with. So that, yeah, so that's where the messiness comes from. But I am sure that as new data come in and as modelers, you know, have a moment to catch their breath

and start comparing their model to other models, and there are wonderful initiatives to do this sort of comparison that we're going to see more convergence and more of a consensus. I'm Ira Plato, and this is Science Friday from WNYC Studios. This is, I really think, the first time the public has been inundated with waves and graphs and things and modeling. Do you think they're coming away with a better understanding of modeling? Yeah, I would love to know the answer to that question. I hope that by listening to shows such as this one, that the public is coming away with a better impression and a deeper understanding of modeling, I'm quite worried about some of the models, one particular model that has received a lot of attention,

a particular, a lot of attention from the White House, that I think is not going to be one of the more reliable, you know, useful models going forward. And I hope that there can be a shift for the wider diversity of models that are out there, and especially the models that are being produced by people who have been doing this for decades. So, Sarah, are you saying that the White House predictions are not very helpful because they change all the time? I'm saying that one of the main model that they relied on, yes, is a very poor model that is very brittle and is a very statistical model that does not capture the underlying process. And so, yes, the predictions change frequently, like daily and dramatically often. people are already talking about a second wave coming this fall. Is there any way to predict whether our actions today, whether it's social distancing or what, might predict what a second wave

might look like? Well, Ira, it's a very difficult question. We've had this novel infectious agent dropped into our midst, but we really have an incomplete picture of it. And unfortunately, some of that incompleteness factors into the question that you asked. We don't know, for instance, if this virus, truly is seasonal. Other endemic coronaviruses are. They peak in the northern hemisphere in January, February, just like the flu. If this virus were to follow that pattern, we might see an ablation, a diminution of the transmissibility of the virus in July and August, and catch a break from it here in the United States. And that then may peel back as we get into colder and drier months in the fall,

and that may encourage a wave, a second wave, such as what we've seen, with pandemic influenza. But we don't know if that's really the case. And layered on that again, are all these sociological uncertainties. If we get a break from it for a while, are we then going to start frequenting restaurants and open businesses and get too much back to normal? And then as the transmissibility of the virus increases again, are we going to see a second wave? And even without seasonality associated with it, those same processes may come into effect. You know, right now, Georgia has reopened its movie theaters and nail salons and restaurants, but not all the businesses are opening. Some of them are too cautious about it and they're concerned about the virus so they don't.

And not all the clientele are even going to go in and fill those restaurants. But if this experiment goes forward and they don't see a rapid rebound in cases, the confidence to go and go out to a restaurant and go to a movie theater is going to increase and more and more people are going to utilize them. The unfortunate reality, though, is that there are these delays baked into this system in which a person who inquires the infection today won't be confirmed if they do become symptomatic and seek clinical care for another week or two. And that gives the virus a couple weeks to actually take off for a time. So we're in some very risky areas right now. And for both social reasonings for relaxation of social distancing and the way behave and interact,

as well as maybe due to this innate seasonality if it does exist, we could see, second waves or third waves? This is where actually we're getting to a whole new level of modeling that most of us in the infectious disease world, like we don't have a lot of experience in that. I think we basically choose when the second wave is going to happen in the sense that it's so determined by changes in our own behavior. And we're clearly dealing with a response to, you know, infectious disease outbreak that is, you know, unprecedented in its magnitude right now. And so it is going to be tricky, I think, for any model right now to say what's going to happen. in late summer or the fall because you somehow have to be able to forecast policy at the same time

and also forecast responses to that policy. So I hope that people can appreciate that complexity while simultaneously understanding that there are certain fundamental like biological processes that almost all the models do agree on, one of them being, for instance, that unless there's this very strong seasonal depression in transmission rates that's occurring in the summer, you know, in the Northern Hemisphere, if we pull back on a lot of these interventions and try to go back to business as usual, there's almost certainly going to be a second wave almost immediately. I want to thank both of you this hour, Dr. Jeffrey Schaeman, Professor of Environmental Health Science and Director of the Climate and Health Program at Columbia University in New York,

and Dr. Sarah Kobe, Associate Professor of Ecology and Evolution at the University of Chicago. Thank you both for taking time to be with us today. Thank you. Thank you for having us. That's about all the time we have for this hour. Charles Burquist is our director. Our producers are Alexa Lim, Christy Taylor, Katie Feather, and Kathleen Davis.

B.J. Leatherman composed our theme music. And if you missed, of course, any part of this program or would like to hear it again, subscribe to our podcasts, or ask your smart speaker now to play Science Friday. I'm Ira Flato. Have a safe weekend.