Welcome everybody to the Department of Statistics. As Dorel just put it to me in this age when we don't need experts,
actually the world is hanging on the voices of experts and we are lucky enough in this very worrying scenario to have two experts of our very own.
So today, as many of you will know, two cases of the coronavirus have been confirmed in the UK,
and our colleagues, Crystal and Robin, are going to give us some insights into world of real time epidemiology.
This is what actually happens when you have thoroughly embedded in the subject.
So I thought I would start by addressing what are the issues that we might want to know at this stage because this is a a new disease,
a novel infectious agent. So what are the things you might start with?
What are the symptoms?
You have to design a case definition, you know, you're not going to test everybody that has every possible symptoms, so you've got to work on that,
identifying what the causal agent is and Robin will speak about how you can compare the characteristics
the symptoms with for this pathogen with what has been seen profiles and previous ones.
How many cases are there? How many cases might there be in future?
That is, of course, the key thing, but that's always projecting into the future is the what if.
And while mathematics is very good at giving us the opportunity to think, what if under several different scenarios,
because of course, we'll only just see one, there is inherent uncertainty and challenges in doing that.
How serious might this disease be? So we could look at projecting future number of cases and thinking about how many people might ever be affected.
But we are also interested in knowing what the case fatality ratio is.
I mean, that's important in terms of measuring severity and determining what's a proportionate response, as well as how many people will be infected.
But it also is important because people will start testing different treatments.
And so knowing what the baseline case severity is is important.
The incubation period distribution is important because if it turns out that I've
exposed someone and I get find out tomorrow that I've got a particular disease,
the incubation period is the time from when they were infected until when they show the signs of disease,
and that knowing that distribution tells you if you're following them, how long.
You should keep checking on Alison to see if she's still OK before you can give her the all clear.
And finally, then of course, you're talking about the opportunities for control.
We can talk characterise transmission, but what is the point of doing that if we don't also help identify and evaluate possible control measures?
The numbers here are numbers that we made public through made with collaboration with colleagues at Imperial College London,
where we have an infectious disease outbreak response unit.
And so we collaborate with different people. So what we did here was follow up,
similar work that we'd done originally when I was collaborating with Christophe and others on the influenza pandemic.
So in that case, we were looking at the number of people who were coming out of Mexico and been confirmed with pendant.
What then became pandemic influenza and comparing those to the number of people who had been identified as cases in Mexico.
There was this mismatch.
Of it seemed like more people were coming out infected than you might have expected, given the number of cases that have been reported.
So we produced a report on the 17th of January where we looked at the fact that there had been three cases that had been exported.
This is now in this case, exported out of China.
And so in order to figure out what that means, so you can think of the population of infected people as being a sample of N or a population of size N,
and then there's some possibility that the individuals who are infected,
each of those individuals have a probability of travelling internationally and being detected as a case somewhere else.
And so how do we think about doing that?
One of the possibilities is we used data that we had previous access to on the number of people travelling internationally out of airports.
And so you can look at Wuhan International Airport. You then have to think, OK, what is the number of people?
What's the denominator for people who would use that airport?
And we can consider either the the city population, which is 11 million or the metropolitan area population, which is 19 million.
So this is a huge city and try to use that as the denominator for the probability that people would go out.
Now there are all kinds of uncertainties here because we've assumed that everybody sort of the same in this.
The probability that you got infected is is the same as everybody else.
And the problem is so that the travellers are, assume a random sample of of the people there of that 19 or 11 million now.
You know, all sorts of things about yourself that you're not you're probably not representative of everybody.
And so, you know, people have raised the issue well for the market.
Initially, those people who might have bought or gone to a market to look at wildlife, to eat,
that they might be people who were more likely to travel if they were more affluent, for example.
So there are uncertainties. But if we assume that everybody is equally likely and look at a detection window of the delay for cases,
we estimated on the basis this baseline scenario, three exported cases implied central estimate of about 7500 cases that range between
four hundred and four thousand when we updated the report on the 22nd of January.
By that point, there were seven exported cases and that gave a central estimate of four thousand,
and you see the sensitivity to the different assumptions based on the detection window and the catchment area.
But to put this in context, the number of reported confirmed cases in China on the 16th of January was forty one.
So that showed that they were would likely this analysis showed that there was an indication
that it was very unlikely that there were only forty one confirmed cases at that point.
Now that is not surprising because you would expect if it's a novel infectious agent, it takes time delays to identifying it, taking samples.
In this case, sequencing it and identifying it as a new agent and so on.
But just to give a sort of magnitude of what was going on. We subsequently looked at estimates where we looked at a range of are not value, so.
We said, OK, so our not is the it's the basic reproduction number.
It's the average number of people one infected person will cause in a fully susceptible population.
If you think for this novel agent that the population is fully susceptible, it's being introduced.
Question is what reproduction number would you have to have to get from a zoo, not a exposure, and you have to assume a particular date of that.
Then you might say the end of December or some point in between there.
What reproduction number would you have to have to be consistent with four thousand cases?
And so the way we did that was through simulation and you see a set of simulations over there on the side.
And that was for the case where they are not was equal to two point six,
and that's where you get where the median of all those simulation gives four thousand cases.
Now why do we look at four thousand?
Well, that was because that was our central estimate that was published on the 22nd of January and corresponded to the 18th of January for the number.
So what does this? What is two point six mean?
Well, if you're going to control an infectious disease, you need to get the reproduction number down from whatever it was to start with.
In this case, maybe two point six. You see different scenarios.
The the lowest one comes when you assume that our lower limit, which is only one thousand cases,
and that the initial number exposed at that market infected by the zoonotic source of the genetic sources,
those animals infecting humans was two hundred. So to get from two hundred two thousand you don't need are not be that high.
But if, for example, are not equals two,
you need to prevent at least 50 percent of the transmission in order to control it and bring an increasing situation into a decreasing one.
Obviously, all of us would like to bring the transmission to zero.
And that's an hour of zero. But it doesn't have to get to zero in order to stop an epidemic.
It just has to be pushed below one. Oh, I also wanted to talk about estimating the case fatality rate.
So this is a figure that I pulled off of a Thai website.
So, so the date of this was the 28th of January.
And it was comparing the number of cases that have been seen so far for the confirmed for the coronavirus,
which was just under three thousand and that there'd been eighty two deaths.
So they took a ratio of those and said, OK, the mortality is two point eight percent.
They compared that to sailors with the number of deaths and confirmed cases and said, OK, so ours is nine point six.
So where did this come from? So if you look at worldwide, if you look at the W.H.O. website,
you get these numbers off of for the eight thousand ninety eight cases and the number of deaths for sailors.
I'll talk specifically about work that we did in looking at Hong Kong because that was Kristof and I were both
involved in the analysis of Hong Kong data in collaboration with the University of Hong Kong colleagues.
And there we found actually that the CFR was 17 percent, so considerably higher.
So the rest of what I'm going to say, keep in that context.
This shows you by day the number of cases in the Hong Kong SARS outbreak, so this is two thousand and three,
so it was a an unusual, atypical pneumonia that had been detected in China in late 2002, almost exactly the same time of year.
As things unfolded this year, this is how it unfolded in Hong Kong.
So the first cases was the 22nd to February. Cases peaked in end of March, early April.
So if you were looking at the number of deaths divided by the number of cases and you
decided to do that on the 2nd of April because you look at that by the 2nd of April,
there were nine hundred and twenty five cases. Almost eighty six percent of them were censored in that they were still in hospital
and you didn't know if they were going to end up being a death or recovery.
So there are then considerable risks associated with your estimate being the number of deaths so far divided by the number of cases so far.
And that was how this was being tracked in terms of the case fatality ratio.
So imagine that this said D divided by C.
So that's method one is deaths so far divided by cases so far?
We then considered alternatives. This is again collaborating.
This is in two thousand three, and Method three was in collaboration with also with David Cox, who gave us, as always, very sage advice.
But Method two.
So one of the things that people said with such a simple estimate, it's so clear it's transparent cases divided by so deaths divided by cases.
But if the time from becoming a case to dying takes a long time, there will be that gap where there's a delay and it will be biased downwards.
An alternative that's almost as simple is deaths divided by deaths plus recoveries.
And in that case, if the time from becoming a case to dying is similar in distribution to become a case to recovering, then this should be unbiased.
We also looked, as I said, developing with David Cox,
a method where we used its non-parametric and uses a Kaplan-Meier like estimate that is not that example is not four stars,
but showing that you can look at the how the probability of recovery goes up and how the probability of survival goes down.
And you end up somewhere in the middle with those two lines meeting when you've seen all of the outcomes.
And so we looked at the performance of these looking back at the end of the SAR's epidemic,
looking back so epidemic for Hong Kong and showed that if you look at the April second,
so remember, that's when eighty six percent of the cases are censored.
This is the estimate based on deaths so far divided by cases so far.
And it has a very tight confidence interval. It's still a couple of percent this in black, this black square.
This is the eventual case fatality rate, so between 13 14 percent for all the people that had become cases by the 2nd of April.
And what you see if you just follow the one with the diamond, which is the simple one of deaths divided by cases over time, this will go up.
It will eventually get to the right place if you observe all your outcomes because of all the deaths divided by all the cases.
But it's very biased down initially our our simple alternative, which was death divided by death plus recovery.
That's the one with the triangle here. And of course, it shows considerably more uncertainty because there weren't nearly 900 people who had outcomes.
And so it's less certain, but it's not biased. If the time from becoming a case to dying was not similar to the time from being a case to recovering,
which could happen if you were looking at having to hold people for a couple of weeks to be absolutely certain they weren't infectious,
that can make a big difference. And then our fancy statistical one would be the way to go.
How did that get interpreted? The press picked up that the estimate was increasing over time,
that it was becoming deadlier if it was mutating to become more deadly, which is quite scary.
And so we published our case fatality ratio estimates, and this is how it was reported on the 26th of April.
So the research by a British scientist, it wasn't one, but there were several is due to be published and said that the virus could kill.
So the case fatality ratio,
the confidence interval at that point between eight and 15 percent W.H.O. had predicted or estimated between five and six percent at that point.
And they said its estimate was more reliable because it was based on cases worldwide instead of just looking at Hong Kong.
There was some discussion in the next hour by which the research was by Professor Roy Anderson, not a British scientist, but again and colleagues.
But then this WTO spokesman said that Roy Anderson was a top class professional and his findings were probably accurate.
So sometimes it's not the message. Sometimes it's the messenger.
And I just wanted to show you before I hand over to Robyn. The case numbers as of today, it's nearly 10000 cases.
This is by colleagues at Johns Hopkins.
But I wanted to show you I have to try not to stand in the way.
So if we look at deaths over deaths plus recoveries, it does not look good.
But again, I hasten to add before people jump to conclusions on that.
It's going to be very different if the time from being a case to dying is very different from the time, from being a case to recovering.
And if we look at the examples, I look here because I can see them as opposed to looking at this angle.
If you look at who by which is the first on each side, they've had two hundred deaths and one hundred and sixteen recoveries.
But if you look at Guangdong. They've had zero deaths and 11 recovered,
so it really depends on how long the epidemic has been there and also could vary depending
on how long people are being who who aren't critical and dying are being kept in hospital.
OK, I will hand over to Robyn this. Hi, everybody.
Thank you very much for this opportunity to update you on the work that we've been doing in the Mathematical Institute to analyse this outbreak,
as Crystal said, in real time. So Crystal put up this really great set of questions at the beginning,
which are the kinds of questions that mathematical modelling can be used to answer in real time during an outbreak.
I'm going to focus on two of those in terms of the work that we've done.
The first one is this one here. So this was in the very first few days of the outbreak.
And the question was, can we use modelling to identify what the underlying pathogen is?
So can we say this is perhaps the smallest corona virus? Or is it instead something new that we've never seen before?
So that was the first question.
And then the second question that we've been focussing on more recently relates to this question of how many cases might there be and in particular,
if the virus goes to a new location. So, for example, if it comes to the U.K., as it's now done,
what's the chance of then seeing a chain of sustained human to human transmission in that new location as opposed to not seeing any more cases?
So therefore that the two questions I'm going to kind of concentrate on.
The first thing is, like I say, it was what we were trying to do in the very first few days of the outbreak and in particular,
the first I heard of this outbreak was right back at the beginning of January. So I was alerted to it by colleagues at Hokkaido University in Japan.
On what they'd seen is they'd seen this notice here in China and in particular, I don't speak Chinese,
but I've been informed that this apparently says, well, there's been a cluster of atypical pneumonia cases in the city of Wuhan.
But the source of these pneumonia cases, so exactly what the pathogen is, is as yet unknown.
So I might, as I say, my Chinese isn't that good, but apparently that's what that says.
And the other thing that was known at that time or what appeared to be the case
was that all of those reported cases were linked to a particular seafood market.
This one here the Huanan seafood market in Wuhan. So that was right back at the beginning.
This is a kind of updates relating to the kinds of figures Krystal was just talking about.
So back then, there were very few cases. I think something like twenty seven cases at the end of December, as you can see now,
the number of reported cases has been accelerating quite rapidly. So going back, as I say, it's the beginning of the outbreak.
This was the scenario, so cases kind of accumulated during December 2019,
and when we got to the 31st of December, there were 27 cases of viral pneumonia that had been found.
As I say, almost all of these cases were linked to this one on seafood market.
And so with that in mind, there was no evidence at that point of any human to human transmission.
So what was kind of suspected at that stage was that all of these people had picked up the virus from the market, from an animal reservoir.
And that's as far as we knew. There was no evidence of any onward transmission between people.
So what we could do at that stage, obviously,
people want to do is they want to actually test the virus and find out exactly what the virus is that's driving the ongoing outbreak.
And if you want to identify unambiguously what the virus is, that's what you have to do.
Unfortunately, that takes some time.
And so in just those first few days, we were saying, well, there's a huge amount of data that's available via social media sites,
so via websites like flu tracker and other medical social media sites, and they give a rough idea of characteristics of the outbreak.
As you can see, so some of some of the dates, you can see a kind of not necessarily that accurate.
So for example, the fact that we thought maybe there wasn't any human to human transmission, but there was a vast quantity of possible data like this.
So what I've got in this in this column here is a vector of what was observed at that point in the outbreak.
So in particular. So there's a one indicating that there were lots of cases of atypical pneumonia.
There's a zero here, which indicates that stage.
It didn't appear like there was much human to human transmission and a whole bunch of other different characteristics that had been observed.
So what you can then do is you can go back and look at previous outbreaks like, for example,
the SAR's outbreak and you can write down an equivalent vector based on what was observed in that outbreak.
And then what if what you want to do is you want to determine whether or not the current outbreak is indeed due to the source coronavirus?
Essentially, what you want to do is a pairwise comparison between this vector here and this vector here.
And if those two vectors are very similar, then you might conclude that in fact, the current outbreak is an outbreak of cells.
If those two vectors are very different, then you might conclude that, well, perhaps it's not SAR's, and it's something a bit different,
either one of the other possible pathogens that are on your list or in fact, not one of these and something that we've never seen before.
So this is what we did and what it all essentially comes down to is quite a simple application of base rule.
So the idea is what you want to say is you want to say, well,
what is the probability that the outbreak you've got is due to some disease, for example, SA's given the observed characteristics.
So given the vector of stuff that we've seen in the ongoing outbreak.
And then if you apply based rule, you can say that, well,
that's equal to the probability of seeing this set of observed characteristics, given that it is, for example,
SA's times by an a priori probability that this outbreak is SA's divided by the equivalent terms for
all of the possible candidate diseases on your list of diseases that it could hypothetically be.
So anyway, we kind of did this calculation, you have to make a few assumptions, in particular,
you have to make some assumptions about the distance between these vectors and how that relates to the
probability that the observe characteristics are due to the disease that you're comparing against.
So I would say that at the moment, this is quite a rough kind of method.
But what this rough method gives you is it gives you a kind of priority list of potential pathogens that could be causing the ongoing outbreak.
So unless that looks a little bit like this going from the one that appears to be most likely given the characteristics
you've observed two diseases that are in fact less likely pathogens that are less likely to be the causal agent.
I mean, if you want to see what you can do is you can extend this analysis and you can say, well, what given these vectors,
what is the probability that in fact this outbreak is being driven by a pathogen that is not any one of those pathogens that are on your list?
And if you do that,
then what you obtain is you obtain a kind of risk score for the probability that this that this outbreak is driven by none of these pathogens.
And in particular, what you can see here is this is called disease X. This is how I refer to this.
This means a pathogen that you haven't previously seen what you see as you see something that changes your probability of disease.
X is something that changes throughout the outbreak as more information comes in.
So as you see more characteristics, that suggests that this perhaps isn't stars, for example,
then you'll probability that this is in fact an entirely new thing is something that jumps up through time.
OK, so that was the first kind of bit of analysis that we did, the second one is something that we've done more recently.
And so like I said, the question here is, well, if in the U.K. you go somewhere else.
So if a case goes to a new location, what's the probability that we then see sustained transmission in that new location?
And so I should say this figure has nothing to do with coronavirus at all, but I just wanted to demonstrate the idea.
So this is a model that many of you might be familiar with. So this is a simulation of an FBI all model and in particular, a stochastic RSI all model.
The idea here is that you flip a coin lots of times and according to the result of each coin flip,
you either generate a new infection in your population or you assume that one
of the infected individuals is that you've got recovers or becomes isolated. In either case gets removed from the current outbreak.
And so you flip a coin lots of times.
And if you do this once, according to a very simple model like this, you might see an outbreak projection that looks a little bit like this one here.
If you do exactly the same thing again, under absolutely identical conditions, you might see something that looks very slightly different.
So like this blue line here, this blue line is exactly the same thing, but very slightly different sequence of coin flips.
But then if you do this again. So once again, on the absolutely identical conditions, then you might instead see something a bit like this.
So this is starting with one infected case and in the red case,
it just happens to fade out and you don't see an outbreak driven by sustained human to human transmission.
And again, this is just based on the coin flips that you have.
So if your first coin flip is someone getting removed rather than someone generating a new infection,
then you'd see a very small outbreak like the red one here.
So this leads kind of naturally, this is just to introduce the idea that when someone brings the virus to a new location,
there's some probability that what you see is an outbreak driven by sustained human to human transmission.
So in other words, you see something like the blue or the black, as opposed to something that looks like the red.
And so if you had a forecasting model in theory,
what you could do is you could simulate this lots and lots of times count the proportion of times you get something like the blue and the black,
as opposed to the proportion of times you get something like the red.
And that gives you then the probability of getting sustained transmission when the virus is in a new location.
That's kind of the idea. It turns out of simple models like the iPhone model, you can calculate this probability analytically.
So in fact, for the asylum model, it comes down to solving a quadratic equation.
But for more complex models, it comes down to doing more complex calculations. But essentially what you see is something like this.
The probability of sustained transmission is some function of how transmissible the pathogen driving the outbreak is.
And if you have a very transmissible pathogen, then the probability of getting a sustained outbreak is quite high.
Whereas if you have a pathogen that's not very transmissible, then the probability of getting a sustained outbreak is substantially lower.
And this measure of pathogen transmissibility on the x axis, so Crystal referred to this earlier,
but this is just the product of the rate at which new infections are being generated by
each infected hosts and the length of time that an infected host is infectious for.
So in other words, if you have if you have a pathogen for which hosts are infectious for a very long time,
then obviously the pathogen is going to be very transmissible.
Similarly, if you have a pathogen for which infected hosts generate new infections very quickly,
then again you get a pathogen that's very transmissible. So that's kind of the idea.
We applied some of these methods to data from the ongoing outbreak.
So in particular, one of the things that we wanted to quantify was how long individuals are infectious.
Four. And so the way that we did that was we took data from forty seven patients from very early in the outbreak.
The data that were recorded were the number of days in between individuals showing symptoms and then being taken to hospital and isolated.
So in other words, this is the measure of how long people could have been out in the community and spreading the virus for.
So this gives you some sort of metric of of the duration of infection. And then from that,
you can use methods like the ones I just showed you to calculate the probability of getting a sustained outbreak when the virus goes to a new place.
And then what we did is we looked at, well, what happens if you take this distribution here and you squash it?
So in particular, what happens if you can cut down the amount of time between symptom onset and isolation?
So, for example, by having more rigorous surveillance of symptomatic hosts and if you have more rigorous surveillance of symptomatic hosts,
then your risk of getting sustained outbreaks is substantially smaller.
And so, as I said today, this is kind of quite a topical analysis because today we've obviously seen these two cases in the UK.
This is one of the first preprints, one of the first analyses of these outbreaks that was put out online and so slightly.
Strangely, this is my first experience of any press attention.
So in particular, this graph appeared on BBC News Live from my living room, which is kind of strange.
But anyway, so the idea is that now this is a very topical analysis. And so I guess these kinds of calculations are particularly relevant right now.
So just one thing to say then is about an extension to this work that we're now looking at.
So my PhD student, Francesca Reid, is now doing some work on this in particular.
That's in response to this article here.
And evidence that came from China to what's been found now is that some infections appear to be happening before the in fact becomes symptomatic.
So there some possibility that infections can spread, not from people that are showing signs of having the disease, but instead before that time.
And with that in mind, this distribution here isn't a very good characterisation, perhaps of of how long people are infectious for.
So what we're doing is we're updating this analysis so that we don't have individuals that have the disease
going from a state in which they're not symptomatic and not infectious to being symptomatic and infectious.
So this was our assumption before.
But now we've got something slightly different in which individuals go from being not symptomatic and not infectious to being not symptomatic,
but potentially infectious. And then it's only later that they become both symptomatic and infectious.
And so you can then extend the kinds of analysis that I just showed you.
The key conclusion that came out of this is the exactly how you might look to try and control
pathogen with different levels of pre-symptomatic transmission might be very different.
So if you have a pathogen that for which a huge proportion of infections are occurring before symptoms occur,
then that might that might affect what you do.
In particular, a control strategy that shortens the period of symptomatic infectiousness might not there might not be very effective any more.
So actually,
just going and trying to find symptomatic people might not be sufficient if there's a lot of pre-symptomatic infection to try and control the virus.
So I'm going to leave it there. I'm going to Haslam's Christophe and I look forward questions at the end of things.
So I think I'll always remember the day when Crystal gave a couple of us who
were working with her on such a quick lesson in survival analysis and bias,
and right truncated data showing that the CFR estimates case fatality rates estimates one,
two and a half percent, but between seven and 15 percent where she didn't say was at the time.
There was also a change in the case definition for the inclusion in databases, which doubled the amount of reported cases that afternoon.
So it was a memorable afternoon and illustrates the two points,
which is that important decisions need to be made for controlling an epidemic with very imperfect observations.
And governments need to expose themselves to making difficult decisions where it's very
hard to say whether they were the right decisions or not on the basis of incomplete data.
So the ability to try to adjust and to come up with statistical estimates,
which are corrected for bias is absolutely critical for improving the nature of decision making.
So one of the sort of, having worked out mentioned just very briefly two perspectives one from the SA's epidemic and one from the 2009
flu pandemic in terms of some of the really difficult questions that need to be answered with imperfect data.
So one of the things I think that's that's new this time is a lot of prior knowledge and very open and rapid sharing,
both of data of analysis and of genome data.
So the first thing is, I think there's an interesting question as to whether we should be sort of thinking about this in a slightly Bayesian manner.
There are sort of priors that can help us sort through the fog of uncertainty and the genetic data from
this new virus was shared within days and showed two things to sort of data points which really help.
So the first is that the genetic data from the first sequences from Wuhan and from travellers were basically all genetically identical,
separated at most by a single mutation and a relatively large genome in error prone and replication.
So we're pretty confident that the virus hadn't been circulating cryptically for months and really did emerge at the earliest and late November,
and probably most likely in mid-December. So that's different from previous outbreaks, where the cryptic transmission,
unreported transmission both in the case of SaaS and in the case of the flu pandemic had been going on for weeks or months.
So this really is sort of Real-Time reporting at the same time in the epidemic for those previous epidemics.
None of these analysis had even started, and none of the data had been shared.
So to put that in context. And the second thing is that the origins of both this new coronavirus are not supposed to stand,
and the new corona virus and of the SARS virus are both within the same clade of bat viruses.
So they share a common origin. And the genetic distances between the new coronaviruses and the source viruses are relatively small.
They're small enough that they're less than the difference between influenza A and influenza B.
And we would both consider those to be the same virus in the case of HIV.
So taxonomic taxonomy, there was a legitimate question as to whether there should be Soulsby or a new corona virus,
and they've settled on new coronavirus. But a prior might have been to assume or might be to assume that this is SA's like.
Now what's the big difference between SA's and influenza that's relevant?
First of all, with SA's containment was a sort of massive success story.
And serological studies after the outbreak confirmed that there was basically no asymptomatic transmission and very little transmission in children.
So the isolation measures and the quarantine measures and the contact tracing measures,
we were relying on transmission that occurred before symptoms on the x axis.
Here I've got the proportion of infections that occur or transmissions that occur prior to symptoms or due to asymptomatic infection.
And on the y axis I've got are not. So you'll forgive the graphics, which were from 2004.
I hesitated about whether to update them, but basically SaaS was fairly infectious but fairly easy to control because most transmissions occur.
After symptoms, as Robin said, so being able to estimate that a clear indicator of that is that lots of
transmissions occurred in hospitals and health care workers where increased risk.
So the influenza estimate here is completely wrong. From this paper in 2004, the transmissibility is much lower.
But there's transmission from very mild cases. So the challenge in 2009 was that it was clear that transmission was going on.
It was clear that there were severe cases, but there was no idea when the critical decision needed to be made,
whether the severe cases were one in 100 cases, one in a thousand cases.
As it turned out, the estimate was probably one in 100000. Infections was leading to a severe to a severe case.
And therefore, in the case of influenza, the decision was gradually made to actually not try to control the virus because more harm was
being done by the social isolation measures needed to control the virus than the virus itself.
And there's some juicy mathematics that you can engage with to to predict the impact of isolation and contact tracing
showing what what the main of this parameter space leads to infection control for different measures or not.
So this is important for deciding whether you're going to go for a reactive control
policy where you follow up symptomatic cases or whether you need to go for a mass.
As as Robyn mentioned, a mass control policy where you just reduce contacts between all individuals in order to contain the epidemic.
So the question is, is this new Kovarik coronavirus, which is genetically similar to us?
Is it a virus that has very high pathogenicity with very few mild infections, very few pre-symptomatic infections?
Or is it to the right of here?
Or is it all of the way like HIV, where most transmissions occur from pre-symptomatic or mildly symptomatic individuals?
This very impressive New England Journal of Medicine paper was published yesterday afternoon,
which is an analysis of the first 450 or so cases from where your hand might be starting to give some indication.
And I refer you to the paper loss estimates of our note, which are consistent with we have published.
But one critical observation here is that in the latest data,
nearly three quarters of all people who have been reported as infections and were hand have not did not at the point of being interviewed,
have exposure to either the market where the infection started or a person with respiratory symptoms.
So there is these are the first data set to move us away from the source prior
to a more important role for pre-symptomatic and asymptomatic transmission.
There's also been well documented cases, such as the cases in Germany,
where an asymptomatic individual who then develops symptoms travelling back to China had infected two people within in Germany.
So the reality is that we still have considerable uncertainty about the severity of infection and the severity.
Infection could be close to the case fatality rate, which is clearly quite high as as Crystal has been indicated,
or it could be much lower if there are many more mild or asymptomatic infections.
So one of the lessons of 2009 is it took nearly four months to sort out this uncertainty,
during which time many decisions had to be made and some statistical lessons were very useful.
And in particular, these are the the labels here refer to the flu to 2009 flu,
but this was a statistical paper by statisticians and was revisited by several different groups
and showing the property of estimates so-called sort of hierarchical pyramidal estimates,
and showing that you can converge on robust estimates very quickly by multiplying the
numbers much more quickly than by doing very large surveys and well-designed surveys,
looking at sort of the degree of asymptomatic infection and then looking at the
degree of severe infection and so on and multiplying those numbers together.
So I think right now, in order to make the key decisions, in addition to the data showing,
we sort of very urgently need to know these kind of data which are going to be reliant on screening the appropriate populations at risk.
I think you.