 So, we kind of spent the weekend putting together some thoughts about some of the problems we saw and how people were thinking about this and tackling this. And I was, I can't believe what happened. As you see, over half a million people read this thing, which we've never reached that many people with anything before. So clearly there's kind of an orders of magnitude more interest in what we have to say about COVID-19 in the community youth and deep learning, so I should probably talk about it briefly. One of the things we talked about in this, this post is now translated into like 15 languages thanks to amazing volunteers. I did a little Twitter summary of it, I'll mention a couple of the things we said. Now that this is eight days old, a lot of this is pretty well understood. But for those people that aren't yet locked down like San Francisco is, you should probably be thinking, acting as if you are certainly cancelling events, getting together with more than 10 people is giving an opportunity to basically kill them and their families because you don't know if you're infected. So work from home if you can, not everybody can, we'll talk a bit about sanitation. It's actually not that easy to do properly. Most importantly, a lot of people watching this are in a position of authority and so I particularly want to talk to you and say, you know, provide sick leave, make meetings virtual, cancel events, I'll make them online. You're the folks who can save the most lives, perhaps other than the medical people. There are still people saying this is just like the flu eight days ago. Most people seem to be saying that, but the flu has a death rate of about 0.1%. The Director of Center for Communicable Diseases at Harvard estimates that for COVID-19 it's like one or two percent. So 10 or 20 times more infectious, we don't know that, but this is a guess from somebody who knows what he's talking about and it's not, you know, it's somewhat widely accepted amongst epidemiologists. Something gets higher, something gets lower. Other modeling suggested it was 1.6% in China in February. So it's much, much more infectious, sorry, much, much more deadly than the flu. One of the really terrifying things also is it spreads incredibly quickly. So it's, there's nothing really, you know, from an epidemiological point of view, there's nothing that suggests anything you understand about the flu is going to help you understand COVID-19. A lot of people say they're basically their responses don't panic. We find that a super unhelpful response. It can tend to lead to kind of apathy or hubris. In China, by the time they were where we, US was when we wrote this eight days ago, tens of millions of people were already on lockdown and two more hospitals were built. China, you know, did this amaze, I mean, they did plenty of wrong things at the start in terms of not actually listening to the whistleblowers and in fact arresting them. But then they did an astonishing response, Italy, not so much. And at the time we were writing this, they had to go to the point of locking down 16 million people too late. We'll talk more about that in a moment. So we don't really know much about anything. And for scientists, one of the challenges here is we tend to not like to do things when we don't know anything. We demand high levels of evidence and, you know, this is not peer review level stuff. But we're not in that world anymore. We're in the world of like, what do we know? What don't we know? That's the kind of, from a Bayesian point of view, figure out the priors. How likely is it that this thing is true, how likely is it that it's false, what would be the utility impact of doing these two things, kind of model that out. So we need to learn to behave in the, you know, in a kind of an optimal way under uncertainty, which is not easy and not very compatible with kind of usual scientific approaches. But we can see empirically, you know, what's actually happening in Italy, for example. So we don't have to guess totally about where things will be in 10 days time, because we can see what's happening in countries that are 10 days ahead of the US or the UK. One of the horrific things about this we pointed out in our post is that in the US in particular those impacted are those who can least afford to be. So the lowest 10% of wage earners in the US of that group only a third have paid sick leave. So one of the things that happens in a country like the US that doesn't have very strong to say the least social safety nets is we force people to go to work financially and as a result we force them to spread their possible infection to everybody else at work, which is terrible for the economy and terrible for society. So these are some things to be aware of. In terms of like what does this look like, it seems like it's something that people have trouble taking on board emotionally like this was a and I'm definitely not hassling this person. I want to know this person's been super brave, but I want to point out this guy's a doctor and he was tweeting just at the start of March, hey, don't worry about this. It's, you know, it's it's a cold virus is going to stop this fear mongering. This is insanity. Come on guys, look at this in perspective, you know, like it's so not deadly. Why are we panicking about other things? You know, look at all these other things which are much worse than COVID-19. And then, you know, good on him. He said, my hospital asked physicians to form a response team. So I volunteered. Let's let you know how it goes. And then, you know, this is a terrifying bit. A few days later, oh, I've now seen this and I can tell you in 18 years of medical practice, I've never seen anything like this. And if you now look at this, look at this guy's Twitter account, it's full of messages saying, take this seriously. This is terrifying. I've never seen anything like it. So the difference, you know, when you see people on the ground, how they're reacting, I find that pretty powerful. So, you know, the the data and I'm focusing on the US because I know most of our people watching this are in the US. But if you look at somewhere like Italy and you say, what was Italy looking like in terms of the number of cases on the 23rd of February, United States on the 5th of March had about the same number of cases. And so then if you go a day forward, Italy and United States a day later have a similar number of cases. Another day later, they have a similar number of cases and so forth. So this is just for Italy and the US, but actually if you draw this graph for most Western nations in kind of cool to temperate climates, it looks exactly the same. Everybody is following Italy just by a different number of days. And so that means that we know that if we respond in the same way as Italy, we're basically going to follow the same shape. So Italy is a very useful role model to understand. So as data scientists, here's some great data. So turning that data into reality, here's what people were saying on the 28th of February. So the 28th of February is here. Italy had 888 cases, the equivalent of the US on the 10th of March. So tourists are hanging out. People are chilling, 28th of February. And then by yesterday, Italy was losing 368 lives in a single day, more than China ever lost in a single day. Many of those lives being lost in a fairly small number of locations. So what does that look like? I mean, you see it in so many ways. You see it as, oh, hi folks, I'm sorry. But our president of the medical group just died. The director of our training school just died. Doctors are dying quickly. These are pictures from Italy. Everybody is just working all the time. There is not nearly enough beds. And doctors are having to make horrific decisions where they have to make a judgment call about who is it worth them treating? Who is too old that they're going to die anyway? They just let them die. Who is anybody under 65? Last time I heard was basically not being looked at on the assumption that they should be, you know, that they might be fine. So this is scary. There are countries that look particularly scary, perhaps the most scary outside of Italy at the moment is the UK. The Imperial College just released their latest modeling, which actually had some looks like some errors that might have underestimated the number of fatalities. But even with that, this red bar here represents the total hospital capacity in the UK. And these different lines represent the number of people that need hospitalization under the assumption of UK does nothing, versus UK closes schools and universities, versus UK isolates cases, versus UK isolates cases and quarantines households, versus UK isolates cases, quarantines at home, and has social distancing. In every case, the difference between the number of people required to be in hospital versus the amount of people that can be in hospital is vast. So what does that mean in the UK? This is basically showing what percentage of symptomatic cases require hospitalization. And in the over 50 group, it's, well, you know, 1 in 10 to 1 in 4. And of that group, how many require critical care? It goes from 1 in 10 to 70%. Critical care basically means ventilation, oxygenation. If you don't get it, I don't know exactly what happens, but the answer seems to be very likely you die. I mean, you basically suffocate. So this is scary stuff. And the UK obviously did nothing for too long. And countries like the Netherlands and Sweden have explicitly made a decision at this point to follow the UK's example. Even though the UK is now, it was actually the Imperial College that did the original incorrect, totally incorrect, modeling that caused the incorrect response from the UK. This is based on their new modeling. And so people who are still saying, let's do what the UK did are heading in this direction and are basing things off totally wrong information. Talking of information, it's very hard to be data scientists in this environment because the data is not there. In the United States, the number of tests we're doing per million people, 23, compared to South Korea, 3,700. Things are starting to ramp up now, but it's a huge shortage. And just to orient on this graph, the blue circles are showing the number of tests and the per capita per million, and the red circles are showing the population size. Thanks, Rachel. So one of the things that's really interesting is to look at South Korea and China because these are countries that have a very different experience, as you'll see, to places like the UK and the US and Italy. And so we'll talk about that in a moment. In fact, let's look at it now. This is from today. And you can see we start basically at 0.0 for each country is when they have 10 deaths. And then this is a cumulative number of deaths. So this is a very lagging indicator from a data point of view. This is something that happens a long, you know, you've been infected, you've got sick, you've gone to hospital, there's hospital beds, and eventually, if things go badly, you die. So on this very lagging indicator, you can see there's very, very different shapes. There's the Spain and UK shape, which is still early, but it's just, you can see these shapes, once they are on a certain direction, they tend to kind of stick in that direction for quite a while. And then there's the Italy, here we are, there's the Italy shape, which is pretty similar, not quite as bad. And we've already talked about where that shape's ended up. Iran's kind of similar to Italy. The US is a pretty big country, pretty spread out. So yes, Rachel? Your cursor is showing offset from the curves you're talking about. Oh, yeah, it kind of just, it just goes a little slowly. OK, so I'm. So maybe just focus on the color of the curves you're highlighting? Yeah. I guess if I kind of sit there, yeah. OK, so the pink one, US, I mean, you guys can read, which one it is. Thanks, Rachel. It's a very different geography, so it looks a little different. So there's kind of one set. On the other hand, look at South Korea and Japan. Now, South Korea is super interesting, because they haven't had a lockdown. So what's going on there? South Korea has not had a lockdown. So economically, they're keeping their economy going. And, you know, societally, they're keeping their society going. So studying them seems like a great idea. And the things they're doing are very different to at least what the US is doing. In South Korea, they have massively invested in tests, in testing and in tests. So, you know, the US is good at investing in things and making things. But currently, the US is not investing in that. They also are investing in masks. Everybody is wearing masks in South Korea. And everybody is getting tested regularly. And that anybody who's tested with the virus is getting quarantine. So South Korea is great to look at data, see outliers, and figure out what the hell is going on. So South Korea is a great outlier. You can also see China. China has taken their curve and flattened it. And they did this by a very early lockdown. And also, what many would consider draconian measures. So there seems to be a couple of different models we can follow. There's lots of masks, lots of tests model of South Korea. Or there's the kind of major lockdowns. But China is now at the point where the disease is not really spreading anymore. And they're starting to remove the lockdowns and wind the economy back up again. So the impact of these interventions is huge. We don't have to give up, right? R0, which is the measure of each person. How many people do they infect, on average, was way up around four in Wuhan. And after the interventions in Wuhan, it went down to 0.3. So when you go down from one person infecting four to one person infecting 0.3, you're going from a disease which is growing to a disease which is shrinking. Oh, we had a question about the previous graph. What's the significance of the 33% daily increase line? I'm not sure. I think my understanding is it's just like an estimate. It's kind of like an average of what seems to be happening in places that aren't really doing much around response. Somehow, Spain and UK are even worse than that. But at the time they were adding this, it was before Spain and UK were appearing much. So it's kind of like, oh, Iran, early China, Italy are all kind of, that seemed to be a kind of a default, I guess, that people were following. So early response. How important is it? Well, these are two regions of Italy. And one region, Lodi, the green one, did a very early lockdown. And you can see what happened as a result. Their number of cases has not increased exponentially. Bergamo, on the other hand, has. And the reason this matters is going back to the graph we saw earlier. If you can keep this going up slowly enough, you can try to keep it under the red line, which means people that need to be hospitalized get hospitalized. So it makes a huge difference to the number of deaths. And we saw that in the 1918 flu where regions like, it was St. Louis, right, that responded early, I'm sure, and did on the whole manage to get people into hospitals and ended up with a much lower death rate than places like Philadelphia that literally put on a parade for 250,000 people just as the pandemic was spreading. And this was for the flu of 1918. 1918, that's right. So it's not like we don't have data or history to learn from, we do. One of the challenges is it's kind of, and in Rachel's work as the founding director of the Center for Applied Data Ethics, she talks a lot about disinformation and trust. It's very hard at the moment because we are in a low trust environment for good reason. For example, the big UK newspaper, The Daily Mail, internally writes, official government advice is no longer adequate enough to safeguard our staff. At the same time that they write in the newspaper regarding Boris and his boffins, we must trust their judgment and do as they say. So we're seeing this low trust environment being created where very clearly media and governments are not always telling the truth. It makes it very difficult to know where we get good information from. So an example of this is like masks. I mentioned that kind of from an empirical point of view, we can see that countries that are using masks widely are doing much better than those that aren't. Correlation is not causation, but as I mentioned at the start of this discussion, we are not looking for proof here. We're looking for like, what makes sense, right? And compare the cost of a mask, or a bunch of masks, to the cost of a lockdown. It's a big difference, right? If we can be South Korea, our economy is going to do much better. And of course, our society is going to be much better than if we have a lockdown and even that turns out not to be enough. So if we have to go on the information we have, we can't look for proof. We can't look for, there is no peer-reviewed, definitive answer, right? The fact is, when people cough, there's a lot of coughing involved in this disease. It's one of the most common symptoms. There's a dry cough. Droplets head out there, somewhere up to 8 meters. And if you've got a mask on, it's your mask, rather than hitting your, well, particularly if you've also got glasses or goggles, rather than hitting your eyes or nose or whatever. So on the other hand, a lot of governments are saying they're not effective. Now, we don't know that, as I mentioned. But there's a good reason for them to say this, which is there's a shortage of them. Yes, Rachel? I was just going to know, saying up to Fecci wrote an excellent op-ed on this in the New York Times today, called Why Telling People They Don't Need Mask Backfired. Yeah, it's not great for all kinds of reasons. I mean, she mentioned the obvious things like, hey, you're saying that we don't need masks, so you should stop buying them so that then doctors can have them because they need masks. It's like, what? And she's kind of mentioning, oh, it's because you can't possibly know how to put them on. So come on, it's a five minute YouTube video showing how to put on a mask. It's basically a government response where they don't want to do the stuff that's been happening in Southeast Asia, which is in Southeast Asia, they basically say, OK, the government has decided that this many masks have to be given to hospitals, so they've allocated them. And for whatever reason, we're not doing that in the West on the whole. And also in China, they bought 38 million masks right at the start of this. The US is not investing in increasing capacity. So when you're not doing those things, it can seem like the right response is to tell people not to buy them, to tell people they don't need them. But probably not a good long-term solution. So as I say, the trust situation is such that doctors are saying they're learning more from Facebook and Twitter than peer reviewed and even pre-pubbed medical research literature at the moment, which is not a great situation. But let's accept that this is what it is. And it partly is because there's a great community response of doctors and virologists and epidemiologists saying, OK, I don't have time to write a paper, but let me tell you, I just went to this place. I did this study. I found this thing. I talked to this person. It's fast-moving information, but we do have information about masks. Not for COVID-19, but for other things that involve coughing and droplets moving around. So there is some research, not perfect, but can tell us a bit about what kind of masks work. And interestingly, this particular example showed that just simple surgical masks were just as useful as N95 masks for influenza virus. Virologists are telling us exposure dose matters, and therefore masks help. And because when you have a mask, it means less of those droplets end up in your nose and mouth. And if that happens, it keeps the peak viral load lower. And so it does less damage. The immune system starts responding earlier and can flatten the curve. So there's a big difference between, at least in the West, the official information that's generally being passed on and the information that we can see in research papers and from experts. Having said all that, it's certainly true that there isn't enough masks for doctors. And so one of the things, if people can find ways to acquire more masks, create more masks, figure out better ways to reuse masks, I don't know. This is something that everybody is trying to figure out right now, because it could make a big difference. Particularly because it's becoming increasingly clear that people who are infected, so most people who are infected don't have symptoms. But the problem is that it's seeming pretty clear that those people are spreading the disease. So again, there's a lot of official advice saying if you've got symptoms, stay home. If you've got symptoms, self isolate, so forth. But the data we currently have does not show that to make sense. It seems actually like regardless of whether you have symptoms, if you have the virus, you need to be not spreading it. So that means again, more testing, more masks, more self isolation, particularly if we have testing, like super wide testing. Another thing I wanted to talk about was young people. People under 50 probably won't die. Some do. The kinds of people who might, you probably know if it's you because they're kind of immunocompromised people and so forth. But let's be honest. You probably won't die if you're under 50. You might pass it on to your grandparents or parents and kill them or your colleagues at work. But the other thing to mention is these kinds of diseases tend to show long term impacts for people when they get older. So less lung function, neurological symptoms, depression. There's tended to be for this kind of class of viruses. There's tended to be pretty debilitating lifetime impacts even for young people. So if it's not enough to say, hey, stay home so that you don't kill your community and family, then maybe, OK, well, stay home so that you don't make your rest of your life much crappier than it otherwise would have been. I was also just going to add that you probably have more co-workers and acquaintances who have chronic illnesses that you're not aware of because many people don't share this information out of a very reasonable fear of discrimination. OK, there is something which is terrifyingly complicated, which is in the 1918 flu, the second and third seasons were much worse than the first. That's what a lot of people are worried might happen. I don't think anybody has a good idea really of what to do about this. It was because of this that the UK did the original disastrous response of, let's not close down events. Let's not close down schools. And now there, as we saw there, and a lot of trouble. And indeed that same group, Imperial College, is now saying in the US, there's a chance that if you isolate cases and household quarantine and social distancing, we might be able to, for the initial period, we might be able to keep things under the red line. This is a super concerning, dangerous approach because if we don't close schools and universities and we don't calculate the exponentials right and nobody actually knows, then we won't find out that we failed to get this right until we're well up along the black line. Particularly because we can't really do case isolation because we don't have testing capacity. So this kind of modeling that's being done to say like maybe we don't have to do too much right now is not based on reality of what we can do. And it's also kind of assuming data we don't have. And most importantly, from a Bayesian point of view, we don't have confidence intervals here. So if you actually do this modeling with confidence intervals, you'll realize that kind of probability that each of these things is successful, a number of cases that we actually find through testing, the bounds we have on our understanding of what the death rate is and so forth, basically mean that the kind of confidence interval of this brown and green line kind of go from down here to way up here. And of course, the utility of each direction varies a lot. So I kind of just wanted to mention that when you look at this kind of modeling, I haven't seen any done yet with that kind of full confidence interval approach and considering the utility of each of the possibilities. And so I'd say be very, very careful of this. And that's what the UK got so very wrong so far. Finally, I wanted to talk about stuff that you can do. One thing you can do is not stress about the supply chain. There's plenty of food. There's plenty of toilet paper. The supply chain is not threatened. And by the way, I'm using a lot of slides here from other people without credit, which I normally wouldn't do. But I wanted to put together something that's very up to date and very quick. And I apologize, but I'm trying to put people's names, at least including them, where they were already on the slide. So this is from Michael Lin. So over the next few weeks, as people settle down, you should see your shops. I mean, I'm still already starting to see shops, starting to see goods again. This is an opportunity to take advantage of some casual racism. You'll find that Chinese Asian grocery shops are absolutely stacked right now. So Rachel and I went shopping at one the other day and got everything that we wanted straightaway. So yeah, don't worry about that. It is hard to find hand sanitizer, which is super important. If you can find something with at least 60 or 70% alcohol, you can mix it up with glycerin or aloe vera and create your own. There's actually an official WHO sanitizer recipe. Here's another one. It is a bit difficult to kind of handle things properly without hand sanitizer. I did post on Twitter a list of Singapore's official list of chemicals which kill the virus that you can find in basically household cleaners. And I found we had like five household cleaners at home, of which four turned out to have the ingredients recommended. So you might be surprised at how many things you can kind of use. The tricky thing here is thinking about how to stay safe is Rachel and I kind of play this game. Think of this computer. Think of it like a computer game. We call it the Code Red game, where somebody else has sat at a table and they were infected with COVID-19. And so you then sit at that table. So that table, we would say it's red, right? And you have to assume that everything that you haven't personally cleaned thoroughly is red because you don't know if it is or it isn't, right? So that's your red. So you have to assume that's red. So you go to a table at a restaurant. You sit down and your hand touches it. So now think of a computer game where like every time something touches a red thing, it becomes red as well. So your hand touches the table, it's now red, by which I mean potentially infected. You pick up your phone, bing, that's now red. You take the phone home, you put it on your desk, bing, that's now red. You pick it up off the desk and then you put your computer where it was sitting, bing, that's now red, right? So that's kind of like how you have to think about this. And so the thing is now you then say like, oh, forgot to wash my hands after I went out to the restaurant. So you clean them and hands are now green. But this kind of now whole chain of things which touch things, which touch things, which touch things, they're all red, right? So this is one of the reasons that kind of everything's taking us a long time at the moment to just get around the place is we have to make sure that everything red becomes green in a way that we don't make other things red in the process and sometimes we forget. And then we realize there's a whole chain of things that happened and then we have to go and do all of those over again. But if you get good at this, then you can kind of like, as long as you stay distanced from people, you can kind of go about your day to day life. You can go and get takeout or whatever. But for this to work, you need hand sanitizer. It helps to have proper, what are they called? Nitrile gloves, nitrate gloves. Some nitrile gloves. And of course, cleaning fluids that you can just spray on a piece of paper, on a piece of cleaning paper and wipe things down. So like when we get shopping, we just pop it on the front deck, wipe down everything with alcohol or bleach before we bring it in and so forth. So it's a hassle, but you can totally handle the situation this way. There's a lot of opportunities for data scientists to help. People have been reaching out to me and so this gentleman said like, hey, Lombardi is, I mean, Lombardi is really the place mainly struggling the most. We founded a nonprofit group of data scientists where we're trying to help. So you can go to defeatcovid19.org to find out what kind of help they need. There are ways to deal with a little bit, some of the lack of testing to at least figure out a bit of what's going on, for example, by, so if you don't follow Eric Fagelding, he has lots of great information about this. Folks like him have been looking at what's the kind of reporting of flu-like symptoms based on just the regular reporting that we have coming out of hospitals and comparing that to the amount of flu diagnoses. And you can kind of get creative like that to figure out like, oh, maybe the difference is because of COVID-19. And the bad news is based on that kind of back of the envelope analysis, things look much worse than the official figures. Pete Scomarock kind of had the idea, you know, as other people have had similar ideas, hey, maybe things like the Google business system that tells you when it's busy or not could be kind of reused to try and help do some kind of pandemic social distance measurement stuff. This kind of stuff we got to be super careful of creating some kind of dystopian surveillance society that we then can't untangle again at the end. But, you know, there's opportunities for data scientists and kind of software developers to think creatively about ways to help fill in these gaps, fill in the gaps around testing, around keeping data available. So, one of the things to think about is how can we help improve testing? The data that's available is suggesting that, so this is from Science, that the vast majority of infections were undocumented and that's led folks like Jeff Dean to ask, what's been the economic cost of delaying testing in the US? I reckon it might be hundreds of billions or trillions of dollars. You can see the difference between South Korea and United States that both saw their first cases within a day of each other. South Korea tested massively. United States just starting to test super late. So, you know, if you can help us find ways to do more tests, that would be super helpful. Also, I mean, you know, we're data scientists, but there's people with all kinds of backgrounds here. If you've got a background in hardware or 3D printing, people are saving lives here. These folks bought a 3D printer to a hospital in Italy and there are people now breathing who previously couldn't, thanks to that 3D printing. The world being what it is, these people have now been sued by a patent troll, which I guess was probably predictable, but yeah, I guess a lot of people are trying to get creative and sometimes working around regulations as necessary to save lives, which hopefully will turn out to be a good thing, not a bad thing. Ventilators is one thing a lot of people are trying to build at the moment, so maybe you can help. So the director of the Johns Hopkins Center for Health Security is saying we need a wartime mobilization to make mass number of ventilators. And to get enough oxygen. So wartime mobilization means a lot of creativity and a lot of not being quite as precise about things as we might have used to have been, but just trying things out and seeing what works. Mind you, the first thing we could do in the US is just buy the capacity we already have. There's actually an opportunity to ramp up production five-fold right now, but the US just hasn't actually ordered the product. So make of that what you will. Some of these folks reach out to me and ask for help finding people to help with open source medical supplies. They're sharing CAD files and there's now been responses all around the world to that and that's going pretty well. Okay, so that is my little spiel about COVID-19 ritual. Is there anything you wanted to add? I was planning to, yeah. Okay, so for those of you that aren't interested in that topic, I apologize for taking up your time. Hopefully some of you found that useful. For those of you that did and are interested in contributing, I have created a forum category for this. Here it is, COVID-19. Now, I think there's a lot of opportunities to, I don't want to say I think, maybe there's opportunities to apply some of the stuff we're learning in this course to do projects that are related to COVID-19. It would be nice, wouldn't it, to spend these two months learning a new thing but also meeting other people, working together on something that seems important. So if you're interested in doing that, this is the place to put it because it's not a closed category like our course category is. If you do, of course, don't like say, hey, I've just learned this thing in the course to people who aren't in the course because they're gonna feel jealous or whatever. So just remember you're talking to folks who aren't necessarily part of the course. But yeah, this is a great place to help write screen scrapers to put data sets together or set up automation of things or do a survival analysis that incorporates uncertainty in the way that I don't think I've seen done yet or so on and so forth. So hopefully that turns out to be useful to some people. Okay, so I'm back again and I just had a quick huddle with Rachel and we decided to put this thing online publicly, not just for our course. So I figured I'd better just pop in again for folks that aren't part of the course and are watching this to fill in some gaps here. When I was talking about the forums, I'm talking about this website here, forums.fast.ai We normally use it to talk about deep learning, not to talk about COVID-19, but we can talk about COVID-19 as well. We've got a category for it there. If you're somebody who's not a data scientist but is interested in having a conversation about COVID-19, particularly if it's something that's more on the technical side or data-driven or practical, which is kind of our things. We would love to hear from you even if it's not a data science perspective, if you're a 3D printing person, if you're a chemist who knows good ways to create oxygen, if you're somebody who knows a good source of free agents for testing, whatever. We would love to hear from you there. So thanks very much for listening to this extremely non-deep learning related video about COVID-19. And I guess the other thing to mention is you may have no idea who I am. I'm Jeremy Howard. This is my Twitter, JeremyP Howard and Rachel is mathRachel, math underscore Rachel. Don't forget the P in JeremyP Howard. I'm gonna be pretty busy trying to create this deep learning course over the next few days. So I don't promise I'll be putting much on Twitter but I will do my best to share things which I think are of interest. So thanks a lot for listening.