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 orders of magnitude more interest in what we have to say about COVID-19 in the community than deep learning, so should probably talk about it briefly. You know, 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 canceling 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, give, provide sick leave, make meetings virtual, cancel events, so 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 the Center for Communicable Diseases at Harvard estimates that for COVID-19 it's like 1 or 2%.

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, some think it's higher, some think it's 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, 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 from all these hospitals were built.

China did this amazing, 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 about 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? What'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 this is 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 can, 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 and, you know, 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, you 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 a cold virus.

He's got 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 aren't 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, "Ah, 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 different, you know, when you see people on the ground, how they're reacting, I find that pretty powerful. So, you know, 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, and 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 cold or 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 you know, 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, okay? Italy had 888 cases, the equivalent of the US on the 10th of March. So you know, 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, you know, 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, it's, you know, 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 it's 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 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. It's based on their new modeling.

And so people who are still saying, let's do what the UK did heading in this direction and 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, you know, 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 the cumulative number of deaths. So this is a very lagging indicator from a data point of view.

This is something that happens along, 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, right? 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, right?

And then there's the, where's Italy, here we are, there's the Italy shape, which is pretty similar, not quite as bad, right? 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?

Oh, your cursor is showing offset from the curves you're talking about. Whoa! Yeah, it kind of just, it just goes a little slowly, okay. 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. Okay, 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, for society, 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 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 anybody who, you know, everybody's getting tested regularly and that anybody who's tested with the virus is getting quarantined.

So South Korea is, you know, it's 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 the 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 they are, 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 4 in Wuhan. And after the interventions in Wuhan, it went down to 0.3. So when you go down from one person infecting 4 to one person infecting 0.3, you're going from a disease which is growing to a disease which is shrinking.

But 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 was just like a kind of 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, Rachel? Responded early 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, you know, 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, you know, 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, you know, 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 be 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 we kind of, 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 as a dry cough. Droplets head out there somewhere up to eight meters.

And if you've got a mask on it, it's your mask rather than, you know, hitting your, well, particularly if you've also got glasses or goggles rather than hitting your eyes and nose or whatever. So you know, that 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, St. Upto Effect, she wrote an excellent op-ed on this in the New York Times today called "Why Telling People They Don't Need Mass Backfired?" Yeah, it's not great for all kinds of reasons.

I mean, you know, 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? You know, and she's kind of mentioning, oh, it's because you can't possibly know how to put them on.

So come on, you know, 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, okay, 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, tell people they don't need them, but probably not a good long-term solution.

So as I say, that the trust situation is such that doctors are saying they're learning more from Facebook and Twitter than peer-reviewed and even pre-pub medical research literature at the moment, which is, it's not a great situation, but let's accept that this is what it is, right? And it partly is because there's a great community response, you know, of doctors and virologists and epidemiologists saying like, okay, 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, you know, it's fast-moving information, you know, for, 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, you know, 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, you know, 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, from experts. Having said all that, it's certainly true that there isn't enough masks for doctors. And so one of the things, you know, if people can find ways to acquire more masks, create more masks, figure out better ways to reuse masks, I don't know, like this is something 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, it's seeming pretty clear that those people are spreading the disease. So again, there's a lot of official advice saying, you know, if you've got symptoms, stay home, if you've got symptoms, self isolate, so forth. But the kind of 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 you know, let's, 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 have tend to shown long term impacts for people when they get older. So less lung function, neurological symptoms, depression, you know, 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 you know, if it's not enough to say, hey, stay home so that you don't kill your community and family, then you know, maybe okay, we'll 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 coworkers 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.

Okay, 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 in a lot of trouble.

And indeed that same group, Imperial College, is now saying in the U.S. 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 kind of 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 kind of the black line, you know, 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, the 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 straight away. 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 percent, sorry, with something with at least 90 percent 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. I 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 you know, 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 distance from people, you can kind of go about your day to day life, you know, 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? Night trial gloves. Night trial gloves. And of course, you know, cleaning fluids that you can just spray on a piece of paper on a piece of, you know, 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 know, it's, 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's really the place is nearly struggling the most.

We founded a nonprofit group of data scientists where we're trying to help. So you can go to defeat covid19.org to find out what kind of help they need. There are ways to deal with, you know, 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 Fagel-Ding, 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 covid19.

And the bad news is based on that kind of back of the envelope analysis, things look much worse than the official figures. Pete Skomrock 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've 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 kind of wartime mobilization means a lot of creativity and a lot of you know 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 fivefold right now but the US just hasn't actually ordered the product. So make of that what you will. Some of these folks reached out to me and asked 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. Rachel 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 matter I wouldn't 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 going to 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 you know do a survival analysis that incorporates uncertainty in the way that I don't think I've seen done yet or you know so on and so forth.

So hopefully that turns out to be useful to some people. 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 to 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 reagents 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 @jeremyphoward and Rachel is @mathratial_ratial. Don't forget the P in Jeremy P Howard.

I'm going to 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.