Modeling the pandemic: a treasure trove for data geeks

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Modeling the Pandemic
19 March 2020
Azeem interviews Adam Kucharski, a mathematician and epidemiologist at the London School of Hygiene
and Tropical Medicine, to discuss the state of the outbreak. Adam is actively working on modeling the
spread of the virus and figuring out how to respond.
Adam is the author of The Rules of Contagion: Why Things Spread - and Why They Stop which explores
the underlying principles behind contagion.
In this interview Azeem and Adam discuss:
• What are the key drivers of epidemic curves—and predictions are put together
• How behavioural changes affect modelling and prediction
• When is a population ‘virus-free’
• What levels of testing are sufficient to tackle the current crisis
NOTE: this transcript was created from the raw audio and was not edited (which is Azeem's usual
procedure) in order to provide the content as soon as possible. There will be a few transcription errors but
we believe the substantive content is solid.
Azeem Azhar: Hello there. It's the Exponential View podcast. I'm doing a very, quick discussion
today with a very special guest. I'm with Adam Kucharski. He's an associate
professor at The London School of Hygiene and Tropical Medicine. He works on
the mathematical analysis of infectious disease outbreaks. Very relevant for
today. He's just written a book called The Rules of Contagion, which is here, so
it's visible, and it's really on the subject of why things spread and why they stop.
A very readable book recommended at this moment, if you could bear to get
something more on contagions.
Azeem Azhar: Adam, thank you for making the time today.
Adam Kucharski: No, thanks for the invitation.
Azeem Azhar: Yeah. Are you busy at the moment?
Adam Kucharski: Very busy. Obviously a lot going on in terms of just understanding what's
happening, looking at next steps, different scenarios and really just trying to fill
in the gaps that we've still got in our knowledge.
Azeem Azhar: As we get started, let's get some terminology right. Shall we talk about the
current pandemic as a pandemic of COVID-19, which is a disease of SARS-CoV-2,
which is a virus that is actually spreading around, or something else?

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Adam Kucharski: So I think commonly people are calling it COVID-19. We often distinguish
between the disease that people are getting ill with … COVID-19 …. but they
could get any infected with SARS-CoV-2, which is the virus and often you would
refer to the infection as that. But I think in wide usage we're calling it COVID-19.
Azeem Azhar: COVID-19 it is. Okay, so let's get started with helping understand what a
mathematician working in epidemiology actually does.
Adam Kucharski: There's a few different elements to the work that we would do on an outbreak
like this. I think one is understanding the transmission patterns, because
typically you'll see the number of cases appearing over time, but that won't
necessarily directly tell you how many cases the person infected is creating, for
example. And then, you'll often have in your data a lot of biases. And you won't
be seeing all your cases. You're having some under reporting.
Adam Kucharski: So the analysis that we do and many others do, tries to adjust out for those
delays, those biases, and to get a more accurate picture of what transmission is
doing. And then when you're thinking about things like disease severity and
fatality, again, all of those kind of data issues are in there.
Adam Kucharski: So, how can you use in the early stages some quite simple methods to adjust
that and get a more reasonable picture. But then as you go on, you build in
some of those key processes that you think are important, and explore
scenarios for what may happen next.
Azeem Azhar: So if we start with some of the basics from this. When you think about a disease
and understanding how rapidly it can spread, what are the characteristics that
go into the model? I mean, people talk about R-0, they talk about those
susceptibile for the infection to spread and to take hold. They talk about
incubation periods. You must have a model with a bunch of variables that go
into it. What are the key drivers? Because I'm sure we'll come back to it in the
course of the conversation.
Adam Kucharski: I think at the top level there's two, real things you need to understand in an
epidemic curve, and one, as you said, is the reproduction number. On average
for each person, how many infections did they generate? If you get infected, on
average how many did you give it to?
Adam Kucharski: And the other thing that we use is what's known as the serial interval, which is if
you get ill on a particular day and you infect someone else, and then they get ill
a few days later, how long is that gap between one infection on the next? And if
you think about it, the reproduction number really gives you an idea of the
magnitude of contagion. In each case, how much it's growing in the next step,
and then the serial interval gives you the timescale to work on. So at the very
simplest level, those two values would enable you to extrapolate forward, so
you could fit it to data and then extrapolate forward maybe a couple of weeks,
and see what you're looking at.

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Adam Kucharski: As you said, there's things like incubation period. If we're looking at more
detailed questions, we build those in. But I think fundamentally for situation
awareness, it's really the reproduction number and the serial interval that are
the early data that we really care about.
Azeem Azhar: One of the things that seems to be happening with COVID-19 is that it's not like
the kind of pandemics we see in the zombie movies. In the zombie movies, the
person gets the condition and then immediately turns into a zombie, and you
can see they've got the condition, and then they pass it on. But COVID-19 seems
to have this strange attribute where it's not visible for a good number of days.
How would you work on that basis?
Adam Kucharski: Yeah, and that attribute is one of the reasons this thing has been so tough to
control, because in these fictional scenarios, you know who is infected. It's often
quite clear, and actually in reality in infections like SARS, Ebola, typically a lot of
the infection happens when people have very distinctive symptoms and they're
more ill, and that means that you can identify who's got it, who might have
been at risk.
Adam Kucharski: The problem with COVID from the early data, the evidence we have so far, a lot
of this infection seems to happen either just before people show symptoms or
when they're very, mildly ill. So actually by the time someone's showing up ill in
hospital and you can isolate them, and you can do infection control, they've
probably done most of their transmission already. So really, in all the cases of
the severely ill you have appearing, you're really seeing events that have
happened in the past and transmission has already gone uncontrolled.
Azeem Azhar: That's a big problem. So if we think back to your model of this, which has been
emerging over the past eight or 10 weeks, I'm curious about how the epidemic
is spreading, Based on your original models of it, is it above that model or below
that model?
Adam Kucharski: One of the first clues that this was a bigger problem than was when we had
reports from China of 41 pneumonia cases, and then Imperial College published
this analysis that showed, based on exported cases for a few other countries, it's
not really plausible you only have 41 cases, that three other countries already
have exported cases from the center of the outbreak. They estimated probably
about 1400 cases were, in reality, there.
Adam Kucharski: So if you did a very simple back-of-the-envelope calculation with that
reproduction number, and with a serial interval, and assuming it's like SARS, we
went back then and did it on that on growth. This was like literally a two minute
calculation anyone can do, just like an exponential curve.
Adam Kucharski: We thought, we've not seen large numbers, so probably the reproduction
number was about two here. If that's a genuine increase in cases seen s that's
happened over a month or two. But then we did a lot of workshops trying to

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find that, and really the problem is that a lot of the cases stated in China, you're
only seeing the severe end of the spectrum, the reporting change. There was a
lot of speculation at the end of January. Are the cases actually slowing down?
What's actually happening there?
Adam Kucharski: So we built models that could capture, or rather could output, values that could
be compared to. For example, we would output a subgroup that would look at
how many exported cases there are. It would output something. If you're doing
evacuation flights, what prevalence would you expect? It would output when a
case is getting it, or when are they showing up in the data, and all these kinds of
values.
Adam Kucharski: And then, we could essentially combine about half a dozen data sets that alone,
each of which we don't really trust and we're not fully convinced about, and we
could try and work out what's the most plausible set of transmission dynamics
that could explain all of those different data sets.
Azeem Azhar: Right, so it's like the six blind men looking at the elephant, right? Every one of
them print a piece, and hope you can put it together.
Adam Kucharski: And that's the big challenge, because in that early data, each piece of data told
you a different story. Actually towards the end of January we were trying to
evaluate the data coming in, the intervals were coming in, we're just trying to
estimate what they're doing. And the China data was slowing down. At the time,
people were thinking they've probably hit testing saturation. Maybe isn't really
still climbing.
Adam Kucharski: So we ignored actually the data that was coming out of China, and we looked at
evacuation flights, etc. And from those we pieced together a drop. The
reproduction number was originally about two and a half, and it dropped to
about one in that week or two.
Adam Kucharski: And actually now we've got the data, and hindsight's a wonderful thing. It's very
obvious that, that's what's happened, but at the time we were trying to work
out -
Azeem Azhar: The reproduction number was lower than we had thought?
Adam Kucharski: Yeah. Yeah, because now if you look at the data, clearly the outbreak peaked,
and then it really slowed down in Wuhan. So the fact that transmission had
declined, now you look at the curve, you're like, "Well of course it did," because
of course you couldn't have had that exponential growth that slowed down.
Adam Kucharski: But what we were trying to do early on is get some kind of external validation
that even if we don't trust that data, can we get that same conclusion with
other data sources.

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Azeem Azhar: Right. I've been reading a lot of the papers that have been published in The
Lancet, and in the New England Journal of Medicine, and on the pre-print
surveys, and even things like the Imperial College piece, the paper from earlier
this week. I hadn't grasped, and maybe that's slow of me, that we were starting
to revise down our estimates of this R-0, which is the critical number, right? It's
the number of people, someone who has COVID-19 is likely to infect. So people
are still running around saying it's two, to two and a half. Are you able to say
where you think the current range is?
Adam Kucharski: Yes. I guess we'd call it an uncontrolled scenario. It's two to three, if the
population is just going about their daily life. It's like Italy was to be honest, like
the U.K. was probably a couple of weeks ago, where no one's really taking it that
seriously. Everyone's just going about their business. You'd be looking at this
kind of thing as two to three, as it doubles every five days or so.
Adam Kucharski: But of course behavior does change that, because the number of infections you
give it to, depends on your behavior. It depends on what you do. It depends on,
are you washing your hands, this kind of stuff. We did see a decline. It was
about two and a half in China and the measures, basically shutting people in
their flats, reducing infection, drops it.
Adam Kucharski: You don't really need a model to tell you that you'd expect to see some effect,
but it's useful to be able to quantify it, and we're currently tracking in Italy. Italy,
a week or two ago had a reproduction of about two or three, much as many
other countries currently do. It's now down to near one. It's not quite there yet,
but we think you do have this lag, because obviously you stop transmission now,
and then people have to become ill, and then they show up in the data. So it
takes a week or two, but, but we are seeing that slow down happen in other
places now.
Azeem Azhar: That's fascinating. Let's talk a little bit about the U.K.. You're based in the U.K.
Could you just help listeners understand how your group at the London School
of Hygiene and Medicine has an impact on the kind of decisions that
government ends up making?
Adam Kucharski: Yeah. There's a few steps to the government decision making. You have COBRA
which is the one most of us have heard of, which the PM chairs, and that's the
kind of emergency committee that makes these decisions.
Adam Kucharski: For an emergency you'll have SAGE which is a group of advisors, scientific
experts in that particular topic that will feed evidence and advice into COBRA,
and then that group of experts will have subgroups, folks in different things. It
has a subgroup on modeling, it has a subgroup on behavioral science, and
obviously a whole bunch of other economic considerations. Other things would
be going in.

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Adam Kucharski: But the modeling subgroup, there's about 10, 12 maybe, groups in the U.K. that
all contribute an evidence base to that. So it's not that we're saying, "The
government should do this." We will be looking at a whole range of questions.
Adam Kucharski: Early on, even just stuff about what's the transmission. We'd be looking at does
arrival screening, does temperature screening at airports work? All these kind of
things, and then those groups will try and make some kind of evidence
consensus. So it's not a kind of proposed decision, but it's saying, "Based on
available evidence we think this situation would have this effect. This situation
would expect to see this," and then that feeds up the chain, and combined with
all the other evidence, forms some kind of basis for decision making. So it's
really trying to be an independent group feeding in, and a lot of what we do
may not agree with what others do, but we hope we can find some kind of
consensus in the broad evidence that's being advised.
Azeem Azhar: I think for the average person in the U.K. and for people watching what's
happening in the U.K., there seems to be a sense, fairly or not, that we have
what could be called a lurch strategy, which is sort of do one thing and then
lurch over and do something else, and then lurch over and do something else.
Maybe that's just a failure in communication, but I'm curious about trying to
understand what that is.
Azeem Azhar: I mean the dinner party discussion, were we to still be having dinner parties,
would be, "What was the impact of the Ferguson Imperial paper, which talks
about suppression versus mitigation earlier this week? Why does Singapore or
Hong Kong or Taiwan all have such a different experience to Italy, or the
Netherlands, or Switzerland, or the U.K., because it's the same disease and
we're all the same people?"
Adam Kucharski: I think first of all, the idea that there's lots of groups doing lots of different bits
of pieces of evidence is not as easy to communicate as a simple narrative that
there was one paper that fixed it, and the whole thing changed.
Adam Kucharski: I think there's often been this idea that there's one model that's driven all of
U.K.'s policy and there's this hidden model that no one knows about, and in
reality it's not. The decision, for example, of the temperature screening at
arrival, and we've had papers on that, and many other groups have. You'll find it
hard to find an epidemiologist that says having temperature screening at arrival
will capture any large proportion of your cases. They miss the vast majority, and
yet potentially you can get complacency as a result. We saw it in the U.S. that
they focused really heavily on China arrivals, and missed the large, domestic
outbreak.
Adam Kucharski: In terms of what's happened in the U.K. though, obviously Imperial or one of
these groups that sit on these boards, they will have throughout, but I think the
really tough position we've had, and the thing that our group and many others
have been grappling with over the last few weeks, is we know what happened in
China worked. We know that locking down, as I said, we found that result early

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on, and others have since validated it, that lockdown reduced transmission. But
the question is are there any other combination of things that can have a similar
impact on controlling transmission?
Adam Kucharski: We know that countries like Singapore, Hong Kong, Korea, have done a really
good job of intensive contact tracing, a lot of testing. Hong Kong combining with
more social distancing measures, but those countries do have a lot more
experience, resources, capacity to do that, and they have some structural and
political advantages. They have a lot more population buy-in. If you look at
population trust in government, it's much, much, higher than it is in the U.K..
Adam Kucharski: Theoretical considerations aside, we did have contact tracing in the U.K.. We
were doing that kind of stuff with incoming cases, and the approach we used
and the capacity we had at the time we were doing it, demonstratively was not
sufficient to contain it. And every other country in Europe that was doing
contact tracing, whatever they were doing at the time didn't work.
Adam Kucharski: So we have this situation where we know a lockdown works. We know that
contact tracing that was implemented in the U.K. last month didn't contain this.
So where does that leave us? Do we just lockdown indefinitely or do we, do we
try and explore scenarios where maybe we have to do it? And it is emphasizing
that even countries like Singapore, that have done an incredible job of
containing this, are seeing their critical case numbers creep up the last few days.
More imported cases, it's becoming harder and harder. The resources that are
required to keep that under the lid will climb up, as more cases happen.
Azeem Azhar: I guess what we're going see and appreciate is the amount you have to spend if
you don't clamp down on it, I mean £330 billion, it pays for a lot of contact
traces, because I think that to contact trace one super spreader, you might need
five full time staff and a bunch of access to mobile phone data. I mean they're
going to be paid really well for it to more expensive than £330 billion.
Adam Kucharski: And it's just the classic thing in outbreak prevention that no one wants to really
put the money in beforehand, but then as soon as you get the outbreak, you get
absolutely hammered with these large costs, and it's everything elaborate. We
see the same concept.
Azeem Azhar: There were a few interesting things that you raised there, which I think are
important to recognize about our own circumstances. I'm thinking about the
long term. We have this lower trust in government. We have a particular set of
maybe cultural attributes. We haven't had the experience of SARS. All of those
things make us more susceptible to gathering around and dealing with this
early.
Azeem Azhar: But we are where we are now, so I think of a dumb question. I mean I'm sorry,
but you're the expert. I'm kind of just a guy at the end of the microphone. It's a

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couple of weeks right, fom beginning to end of COVID-19, when you get it,
through to your recovery, so it's a two week period.
Azeem Azhar: If you prevent people contacting outside of say, their family groups, because we
can't separate wives from husbands, and children from fathers and mothers, for
say, three weeks, three and a half weeks - don't you get to a pretty high degree
of certainty that there's no more of this virus running around? Excluding
imports.
Adam Kucharski: Yes, if we move into a hypothetical world for a bit. If you completely shut your
borders totally, no one in, and no one out for this, you do perhaps some good,
and you can get people shut down for two or three weeks, you might need to
shut down. Again household size, maybe you get some households of five or six.
So if you imagine a chain of transmission, and each one, yeah there's a bit of
variability, but it could be five days, it could be slightly longer. You get a bit of
variance in that. You might expect that maybe with a two month lockdown of
everyone shutting their flats, you would have no more infection in your
population. And then obviously from that point, if you kept your border shut,
then you know you could go about.
Adam Kucharski: But in reality though, that's requiring every, single person in the population to
be in that situation. That as soon as you have any mixing through core services,
through health care, because of course remember some people will be ill and
needing to go into health systems, and that will create some mixing.
Adam Kucharski: So I think in theory that will do it, and I mean actually you'd have the knock-on
effect that you'd probably eliminate measles and all these other things that
might be circulated. You'd knock a lot of diseases out if you ever did this, but the
feasibility of having every, single person in your population shut away, borders
closed, is unfortunately something that in reality it would be very tough to
implement fully.
Azeem Azhar: Absolutely. Yeah, the practicality is extremely low, which makes me think about
the nature of what we model. The very basic model that you described right at
the beginning, just with the serial interval and R-0 is what I would think of as a
kind of aggregate model. It's the sort of thing that you could build in Excel,
where you just look at ... it's like modeling a gas using Boyle's gas laws, right? It's
very, very, aggregate, average, and it's mean, but what we know in social
networks is that they're much more complex than that. You don't necessarily
see normal distributions around how promiscuous people are in their lives. It's
much more of a power law. You see this I guess in epidemiology as well, where
you get super spreaders who ... like the Korean patient 31 right? Who sent it out
to 10, 20, 50 people, and you get a whole bunch of people who are not
spreading it very much even if they are infected.
Azeem Azhar: And you also have these locality effects. You get clusters emerging
geographically. I mean if there's a cluster I live in the borough of Brent. If there's
a cluster in Brent and it's going crazy, it's not really going to affect a cluster that

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happens to be in Manchester, or 200 miles away, or frankly even in Chelmsford,
which is 50 miles away.
Azeem Azhar: So what can we do in terms of using models to capture that degree of network
structure, and if we're able to use those models, does that tell us more about
the kinds of more localized interventions we can take?
Adam Kucharski: Yeah, I think that individual variation, particularly in transmission and contact
structure, is incredibly important, but it really depends on the question we're
interested in answering. For example, if we're looking at the early stage
dynamics and trying to get an estimate of the reproduction number, once you're
into the thousands of cases, the main effect of incorporating that variation, if
you're just interested in that overall reproduction estimate, is it just increases
uncertainty.
Adam Kucharski: There were some early studies, some of which just basically fitted into an [Excel]
curve through the data and got a reproduction number of just above two for
that China data. And there were some that did it more in a statistically robust
way, incorporating individual level variation, but they got the same point
estimate but they're obviously just got more sensible uncertainty around that
estimate. So you could argue that the headline conclusion was the same for
both of those, but obviously one handled the uncertainty better.
Adam Kucharski: I think as you said, there are situations though, where it's very important to
have that idea. I think one is if you're looking at contact tracing study so we had
one on basically when does contact tracing break down as an intervention, and
having that individual level variation is really important then, because you're not
saying everyone has two contacts. You're having somebody you might miss as a
contact, and they generate 10, 20 infections, and that can be important if you're
trying to work out if this is a feasible measure or not, and how many people you
can get away with missing.
Adam Kucharski: But then there's other things that it depends on the level of aggregation. We've
done in the past quite detailed studies where we do really social behavior
diaries for large numbers of people. Really track how many interactions they
have, and we also look at their infection risk. We found for example, June of
2009 pandemic, that for kids it was actually the average level mixing of their age
group that drove infection risk, and not their individual contacts. So in other
words, the kid who says they have no social contacts in a day, has a very similar
infection risk to the kid who says they're really popular, because intuitively you
get environmental things, and the virus gets on surfaces, and in the air. So it's
not just about the kind of social network structure, it's also the shared
environment.
Adam Kucharski: What we found is that actually once you're incorporating that degree of age
resolution, you can actually explain most of your risk for individuals with a very,
very, simple model, like 50, an age group model, rather than a model where you
have each individual with their full contact structure. So it kind of relates to the

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question you're interested in. If the thing you want to capture can be explained
with a simpler structure, then that's obviously preferable to including very
detailed network structure that doesn't actually increase your predictability.
Azeem Azhar: So I suppose what I was trying to vector towards was whether there are ways of
having localized interventions. Contact tracing being the most localized, right?
Azeem Azhar: Because we find you and all your mates, and we figure it out. That can be
effective given what you currently know about the nature of a COVID-19, rather
than broad-based measures which are essentially what we're having to do now
by shutting down the economy and so on.
Azeem Azhar: I think of that as a difference between blasting a cancer patient with radiation
everywhere, versus having a really precise gamma knife that knows exactly
where the tumor is. And, are there ways we can give you, people like you, the
supporting information that can allow more localized interventions? And if,
what are those ways? And if there were more localized interventions, would
that be something that would be more sustainable over time?
Adam Kucharski: Totally. That's a key point. In an ideal world, you want the most effective, least
disruptive intervention you can come up with, and the ones that are really
targeting those, the things like other corona viruses, SARS and MERS. A lot of
these super spreading events were in health environments, so once those were
clamped down on, you know you're knocking out 80% of your transmission,
because that they're so centralized on a handful of events.
Adam Kucharski: The current challenge we have is there are some evidence that certain
environments … out of Japan, for instance … there's some recent data showing
that gyms, and nightclubs, and these kind of environments seem to be
disproportionate in transmission. But at the moment it's not clear that you have
a nice kind of 80/20 rule or something where the vast majority of transmission is
happening from a handful of events and those are predictable.
Adam Kucharski: At the moment, we know these kinds of mass exposure events occur, but we
know about them in hindsight. I think we actually had a similar thing during the
Ebola epidemic in 2014 and '15, that we did a lot of analysis of super spreading
events and tried to work out : are there any characteristics of individuals or
settings that would have enabled us to predict that ahead of time?
Adam Kucharski: And basically the biggest predictor we could find was the people who generate a
lot of secondary exposures are generally the ones who haven't been contact
traced. So, in other words, the people just going around their routine and just
happened to kind of end up spreading it to more people. Which is not really a
very useful health thing, to say, "We can predict the people who are going to
spread it, and they're the ones that you don't know about."

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Adam Kucharski: I think that's the situation. I really, really hope we'll get a better handle on some
of these things because if it turns out, for example, maybe environmental
exposure is more important than we expect, or maybe there are these specific
settings that are driving transmission, that would be incredibly powerful,
because remember that each infectious person on average is infecting a couple
of others. So if there's a lot of variation between individuals, yeah, that suggests
a lot of people that aren't doing much infection and a handful are doing a lot. So
if we could predict that in some way, that could really help us chip away at
transmission, but it's not clear at the moment that there are these kind of
simple metrics that would enable us to identify those situations.
Azeem Azhar: In a sense, we don't have enough data at this point to build a model to predict
who is likely to be a super spreader if they did exist.
Adam Kucharski: No, and one of the challenges is a lot of the data we have at the moment, I
mean the clinical data, everything is looking back. We know these events have
occurred and we have to try and construct some narrative around them, but
ideally what you want is some perspective study.
Adam Kucharski: Contact tracing is to some extent like this, where you know someone's
infectious and then you can look at the people subsequently and what they do
and what happens, and that gives you, if you think about it, a kind of fairer test
going forward, because you're not trying to retrospectively untangle what's
happened. You're actually saying we're going to monitor these people and see
how transmission happens. And that, as we get more of that data in, I think that
sort of stuff's going to be really important for working out how this is actually
spreading.
Azeem Azhar: That sounds powerful, but it sounds a bit theoretical for where we currently are
today with a sort of doubling time of three to four days. I could imagine it would
be a very tough conversation for you to have with someone, to say, "Listen, do
you mind if we just leave it to spread in this time?"
Adam Kucharski: Yeah. I think places are doing intensive testing will get a lot of this indirectly.
Because testing contacts and testing people in detail is useful for control, but it's
also useful for data. So there are these kinds of synergies between what's good
for control and good for understanding.
Azeem Azhar: When we think about testing, it's obviously been people have been looking at
Korea and they've been yelling about the Korea testing numbers per day. You
look at an economy or a country like the U.K. or the U.S. which I guess is broadly
applicable. What's an appropriate level of testing per day that would have you
sitting there, an epidemiologist, thinking, "We're going to get a handle of this,"
and does it have to be unevenly distributed? So, high levels of testing and more
dense population areas?

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Adam Kucharski: Yes, I think it's not so much just over the numbers of testing, it's the proportions
which you're managing to capture. In the early stages in the U.K. for example,
every test that came in … we were testing a lot of people … but every test that
came back was for a case that we knew where they got infected.
Adam Kucharski: And then those numbers started to separate, that we increasingly got cases
where they were showing up, we didn't know where they're going infected,
which meant that there's probably another person out there that we should
have been testing that we weren't. So if you think of the testing effort, if you're
testing a thousand people and you're getting a handful of cases, and you know
what their story is, that suggests that a thousand is doing its job.
Adam Kucharski: But, if you're testing a thousand people and you're getting 10 cases, and five of
them, you've got no idea where they've come from that suggests that you've
got more infection out there, and you need a broader testing strategy.
Adam Kucharski: So I think that's where we are now that we've got so much infection, and we
were not in a position where we've got the capacity we need to be giving an
accurate picture. Fundamentally, it comes down to you want to see as much of
your outbreak as possible, as early as possible, and I think you just need the
testing that's going to enable you to do that.
Azeem Azhar: Now I'm cognizant that you have lots of, I guess, modeling to go off and do this
afternoon. So I want to just ask a couple more questions if I may.
Azeem Azhar: Let's come back to many citizens around Europe, and the U.S., and in the U.K.
will be waiting for the numbers every day. They get announced and we talk
about them on Twitter and Facebook. You mentioned earlier that that's a lot
like looking into the rear view mirror, and it's a historical view, rather than
where the road is going. How do you quantify how murky that picture we're
getting is, because I look at it, and the problem that I have with the numbers
that come out is, I don't know when the tests were actually conducted. Some of
them might've been conducted yesterday, some three days ago, some six days
ago. They're all reported on the day they're reported, and the same is true for
fatalities.
Azeem Azhar: I don't know when those patients were first diagnosed, when they were first
admitted to the hospital. I have no idea whether I'm looking at the fatality rate
of patients from two weeks ago, or from a week ago, or for three weeks ago.
But you do know that data. How should we be looking at it and making sense of
it?
Adam Kucharski: I think first of all, don't read too much into the case data from U.K. or anywhere.
We've seen in the U.S. a big up-tick in cases and that's just because they got
their act together with testing.

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Adam Kucharski: I think death's a bit more of a clear metric to be looking at, and it's been good
that a few visual journalists have been focusing more on that because I think
generally they are reported it pretty promptly, and near they have been tested.
But I think also, thinking of what's available in more detail to governments,
things like ICU data is another one, that those obviously are about a week
before any fatalities, but that gives you a handle on cases you're, pretty
confident about, and where you are in the unobserved outbreak.
Adam Kucharski: Of course both of those occur, and ICU admission occurs about three weeks
after infection, so if you want to work out where you are now, what you have to
do is take those ICU cases, rewind three weeks where you've obviously going to
accrue some uncertainty because you don't know exactly when they were. You
have to try and infer when they might've been infected, and then you have to,
based on early growth data, project forward again to work out where you are.
So you're gathering uncertainty going backwards, and then a whole bunch of
uncertainty going forwards, which really creates this challenge in knowing
where you are in the outbreak.
Azeem Azhar: Murky data everywhere. If you were to give us a rough guess about what the
level of infection is in the U.K. today, it's March the 19th, what would your
rough guess be, and what kind of trajectory are we on for say a month's time?
Adam Kucharski: In terms of absolute numbers, it's very hard to pin down exactly what we're on,
but I think we're certainly seeing an incredible amount of infection in London. I
don't think it really requires a model based on the level of infection we're
seeing. You can do a back-of-the-envelope at the moment. Pretty much all of us
have a couple of friends who are showing symptoms if not more. So I think from
that you could even just work out if you have 50 friends and you've already got
two or three of them showing symptoms, what's the likely prevalence going to
be in London?
Adam Kucharski: But I think in terms of going forward, it really depends on how seriously people
take these going in. I think there's been a lot of early calls for top level, quite
simple, government things to come in. I mean everything's about close schools,
cancel events, which all the modeling and kind of what we know about
transmission would have some effects. It's maybe delaying transmission a bit,
flattening the peak very, very slightly, but I think that there also, it doesn't need
to be an element of individual responsibility on this. If you're getting incredibly
clear messaging from every major news source telling you that you shouldn't be
going out. You should be reducing your social interactions and looking out for
people at risk, and you're not doing that, I don't think it's enough to say, "The
government didn't make me do it, so it's not my fault."
Adam Kucharski: I think that we do need to actually have some kind of responsibility over our
actions and think, "Well, if I'm going to go out and spread infection, and that a
few steps down the chain, puts someone in ICU, that's a behavior I didn't have
to do, and I played a role in that." I think we really to have a bit more individual

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perspective, as well as calling for the kind of top level stuff which is important as
well, but certainly not the only thing that's going to solve this.
Azeem Azhar: It is a time of paradoxes and contradictions. The coronavirus leaps out of from
some animal into some reservoir host, across into humans, through some
markets, gets spread by our most dynamic bits of modernity. That's a paradox.
It's tiny, 120 nanometers. It brings down the world economy, which is one of the
biggest things that we know. That's a paradox. And then, there's this paradox of
that we need to act collectively, but we also need to take individual personal
responsibility. There's another paradox. So it really is very much a strange time
of paradoxes that we're trying to navigate.
Adam Kucharski: It is, and I think it's very new territory. I think this is why maybe some of the
discussions of the scenarios and where we'd going has been quite difficult and
challenging because I think it is. It is a really tough debate, and I think anyone
proposing a simple, two week solution to this does just need to look and think
carefully about where we are, and what they're actually proposing because
there really aren't any easy choices in this. I think we're going to have to work
really hard and it's going to need a lot of innovation.
Adam Kucharski: I think to really think about how we go out of this, there's a U.S. researcher
made a nice Twitter thread yesterday, and made the point that this is our Apollo
program. That we need some serious innovation and large scale collaboration to
get around this, and I think there's a lot of truth in that.
Azeem Azhar: Adam, it's been fantastic to talk to you. I have been following you on Twitter. I
would love other people to follow what you're doing as well as get your book.
What's the best way for them to keep in touch with you?
Adam Kucharski: I'm trying to keep an eye on Twitter. It's obviously a bit of a deluge at the
moment, but yeah, that's probably the easiest way of seeing what's going on.
Azeem Azhar: And your handle is?
Adam Kucharski: Adam J. Kucharski.
Azeem Azhar: Adam J. Kucharski, thank you very much. Listen, thanks so much for taking the
time this afternoon. I didn't want to cut this conversation off. You have so much
insight to bring, and I have so many questions, but I appreciate there's also
more important work to do, so thank you very much.
Adam Kucharski: Thank you.

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Modeling the pandemic: a treasure trove for data geeks