Hello. Welcome to the How Epidemics End project, which is based at the University of Oxford.
My name is Erica Charters and in these videos I discuss with experts how they research disease in a variety of ways,
as well as how they research how epidemics end. Today, I'm here with Crystal Donnelly,
who's professor of applied statistics at the University of Oxford and also professor of statistical epidemiology at Imperial College London.
Bristol, thanks for being here. Thanks for having me.
I know that you studied a range of diseases you've studied, I think SA's tuberculosis and BSE amongst livestock and you've also studied Ebola.
Can you talk a little bit about how you study these diseases and what you look at?
Sure, so as you said, I'm a statistical epidemiologist,
and many more people are familiar with the term epidemiologist now, so I study patterns of disease.
But infectious disease is in particular, and that's important because with infectious diseases,
the risk to you is not just dependent on your own behaviour, but the behaviour of people around you.
And so that's true of animals as well, so we can see transmission between different animal species.
So I've worked across the range of statistical and mathematical approaches to epidemiology,
so measuring risk and also looking at patterns to see if we can quantify the dynamics of the disease.
So anticipating when it might go from an increasing trend to a decreasing trend,
and that can happen because of changes in behaviour, as we've seen with COVID.
If we contact many fewer people, there's less transmission.
Or it could be because a disease is actually becoming so common that there aren't as many susceptible
individuals around anymore that we obviously don't want to be the case with a very serious disease,
whether it's of animals or of humans. But by quantifying the patterns of who's getting infected, how it's transmitted from one group to another,
we can understand more about how it's transmitting, but also what the opportunities are for control.
So can you give us a few examples of how this works in practise, because I think probably a lot of people just know, oh,
there's something called data, there's some models and some numbers, and then some kind of projection comes out the other end.
So say for BSE or for something one of those, I think epidemic's that was very much in the news and in Britain.
What exactly do you do and what kind of data do you rely on?
OK, so BSE, which is bovine spongiform encephalopathy, which was also casually known as Mad Cow Disease that was first identified in the late
80s and it was a disease that caused neurological deterioration and death in cattle.
And so initially, there were studies to understand how it was transmitted,
and it was transmitted actually from cattle being fed protein from infected cattle.
And obviously, the farmers didn't know that when they were giving them the feed, but it was included cattle protein.
And so that set up a mechanism for transmission within the species. So that had all happened before we got involved at Oxford.
And when we became involved was when there was an announcement in the House of Commons, which was in 1996.
That disease had been identified in humans, which was then called new variant of Yakub disease that was associated with BSE in cattle.
So we then got access to surveillance data.
So surveillance is how we watch for diseases as a collective population.
And so then they recorded for individual cattle, which yeah, when they had the disease, what age the animal was,
when it was onset, which form it was in and to some extent, what forms it had been on before that was known.
And so we were able to look at the patterns of how many cases there were in each year and
when those animals were born and see how there had been an increasing risk initially.
But then measures that were brought in in 1988,
which banned the feeding of ruminant protein to ruminants, to ruminants or things like cattle and sheep.
And so they said no more feeding protein from those animals back to themselves, and that stopped the cycle of transmission.
Predominantly, there may have been a little bit of cow to calf transmission.
But it wasn't immediate, and that was what was difficult was the measure was brought in in the late 80s.
But we didn't see the peak until years later and the reason for that is the incubation period.
And that's the time from when an animal was infected to when it actually showed the clinical signs of disease, and that was five years on average.
So that was quite a long time between when there had been a dramatic reduction in infections and when we saw the subsequent reduction in cases.
But we were able to analyse the patterns and also additional data where they had done
deliberate infections and saw how long those take to show clinical signs of disease.
So it's bringing all those different pieces of data together, both to get estimates and particularly as a statistician.
It's important to me to quantify how uncertain those estimates are because uncertainty can be key if you're deciding on a particular control measure.
Well, there's a good estimate of how what the impact will be, how impactful a particular measure will be, but how uncertain are we?
Are we very sure? Or do we think that that's our best estimate?
But there's quite a lot of uncertainty and that can make a real difference, particularly if it's expensive to do.
On what decision is made, I think that it's really fascinating to think about trying to to kind of pin down uncertainty in situations which
obviously must be very fluid and in which obviously I'm sure politicians and the public really want certainty instead.
And you're actually under pressure to kind of explain the limits of what you can
provide detail on and what certain knowledge and what might be somewhat uncertain.
And I was thinking, I know you've also worked on Ebola, which of course, also had, I think,
a long development thinking about what it might mean to have projections and also
thinking about what the end of Ebola would look like and how we might determine that.
So can you explain a little bit about your research on that? Sure.
So I initially started working on Ebola with colleagues at Imperial College,
and when we got involved with helping the W.H.O. respond to the outbreak in West Africa.
And so that was the first outbreak of Ebola that had happened in West Africa,
although there had been previous outbreaks before much smaller and ended up costing thousands of lives in West Africa.
It's a highly infectious disease. It kills the majority of people, even with medical support who get infected,
so it's just devastating to communities and to the individuals who are affected.
But it can respond well to non-pharmaceutical interventions, which we now hear more about.
So those are the isolating cases, isolating people who may have been exposed,
monitoring them very quickly so that if they have have become infected and potentially becoming infectious, that they don't go on to infect others.
And so that's a lot of on the ground work with people who are in a situation where there is this deadly disease spreading,
going and finding individuals finding out from themselves or they're the people who live with them,
who they came into contact with and tracing all those individuals.
Some people would have public health officials show up and take their temperature every day while they're being monitored for a couple of weeks.
It is a disease where there are infections out in an animal reservoir.
And so that is believed to mainly be bats so that there can be potential for transmission through other species,
for example, through great apes can get it as well. And so people could get it that way.
And in that case, there will always be this opportunity for another transmission into the human population,
and then it can very easily spread from person to person.
So what we look for at the end of an epidemic, if that's the end of that particular Ebola epidemic rather than all of them in particular.
And so usually there is a sort of time frame at the end where there are no additional cases identified.
But then you don't just say, Well, OK, we haven't seen one today. We're off.
It's a matter of how long you wait until you're confident that there won't be any more cases.
And we have to allow for the fact that not just how long it takes that incubation period,
the time from becoming infected to showing the signs of disease,
but also the possibility that the surveillance system may miss, occasionally some cases.
And so it's important to make sure that there was time for not just that you saw no cases,
but in case you missed one or two that time, did they infect any others?
And it can be made more difficult because it has been shown in a couple of cases
that people have become infectious later after having recovered from the disease.
And so that can lead to flare ups even at the end and particularly in West Africa,
where there were multiple countries involved and so therefore a big geographic area.
There were times when a country declared. There a particular component of the epidemic over,
and then they had additional cases now sometimes that can happen for reintroduction from a nearby country,
or it could be this case where you have a flare up and somebody suddenly apparently they did have virus in them and it became infectious again.
So both of those things mean that you have to be very cautious at the end of an epidemic.
But right now, we don't have any Ebola epidemics ongoing, but we're very certain that there will be additional ones.
And it's just, you know, as a as a community, we know how to deal with them.
But there still can be devastating in that time between when they're identified
and when they finally are brought under control and reduced to no cases.
I think one point that's very striking is is how, as you described with Ebola, there you're discussing outbreaks,
so repeated outbreaks, and you can talk about a specific outbreak ending, but then also thinking that another thing might continue.
But also the, I guess, the political and the economic significance of being able to state that something that outbreak is over because of course,
that has policy implications for people travelling for people returning to work.
You've also mentioned, of course, how disease continues on.
And so I wondered if you could talk about other types of diseases where it's not so much that the outbreak ends,
but that perhaps cases go down to a particular level or they become more predictable?
And I know you've worked on influenza, and that might help us also to think about what we mean when we talk about the end of, say, COVID.
Yeah, certainly. So before any of us were born, there was influenza and there will be influenza long after us.
And what makes it tricky is it's a viral disease that has multiple animal hosts, so there can be swine flus, there can be avian flus.
And of course, we have transmission from person to person.
So the fact that we have animal reservoirs that can maintain influenza means that we can't really think of ever eradicating it.
But on the other hand, you know, we do function as a society quite well, despite there being influenza around every year.
Now, influenza does kill people every year,
but what we have to reduce those impacts are vaccination and also within particular settings, for example, care home.
There can be measures that are brought in if there appears to be influenza within a care home
setting to try and reduce the risk of transmission between residents so that can come in.
We had a large pandemic of what was a new.
That's what causes the pandemic is there's a new strain of influenza that shows up.
This was in 2009. Probably the estimate is about a third of us got infected over the course of that epidemic,
which is very large, although of course we're seeing very large spread of another disease right now.
On the other hand, we didn't see that then disappear. So it goes down to lower levels.
It becomes more predictable in the sense that we see now seasonal flus in the way we'd expected before.
We have seen less flus over these last couple of winters because of social distancing, because if we stayed further away from other people,
it reduces the risk of spreading COVID between each other, but also spreading other respiratory diseases, including influenza.
So there have been the last two years have been unusual for the transmission of influenza,
but it's still the case that we have multiple strains of influenza out there and grouped into big groups of influenza or influenza B.
But it's certainly the case that people still get this sort of influenza that caused the pandemic.
But it's just because there's so much immunity that's built up partly from natural infections,
partly from vaccination, that it's not causing the huge problems that it did before.
But these are still out there, and I'm very confident in predicting there will be another influenza pandemic,
but the severity of it will very much determine its overall impact on populations.
Lots of people will get infected, but because we can't immediately produce vaccines straight away for worldwide production.
But as you've seen, vaccines that are developed more quickly and can be delivered more quickly than ever before,
and that helps mitigate the impacts of these epidemics. But when does it end?
There's not really one point at which you can say, OK, it was a pandemic.
Now the you know, there is a declaration of the pandemic being over, but that's sort of a a doubly W.H.O.
That's World Health Organisation decision based on various characteristics when it when the epidemic itself is actually over.
There's no one point at which you say, OK, well, it's now changed from an epidemic to being endemic disease,
which means that it's it's sort of always with us at a sort of more predictable level that can still be higher in the winter than in the summer.
But that's sort of a gradual process of it sort of settling down. So there isn't one.
You know, it's not like a shutting of a door, but it's it's sort of gradually moves from being more like, you know, a large outbreak at.
So. From being less like a large outbreak and more into the subtle pattern.
So would I be right in suggesting that partly what we're thinking about is how you study patterns in data and especially quantitative data.
So thinking about the patterns of numbers and trying to make sense of them and in some ways never suggesting that we can be certain say
about predictions because my sense is a lot of people think that you might be able to predict what's going to happen in terms of numbers.
And you probably say that you can create models and projections to think about this,
but especially when it comes to people asking about COVID that we're probably not thinking about, say, when there will be no more COVID.
But what kinds of patterns that we might be able to establish by looking at numbers of target cases, for example?
Indeed. So there are different scales of predict prediction or projection.
You know, if you're saying, well, you know, how many cases might we see over the next week?
That's a much easier thing to try to do accurately than to predict over the, you know,
over a month or over three months, particularly because we have the possibility of these new variants showing up.
But we try to analyse data from several different studies.
So I'm involved with the study called React, which is Real-Time Assessment of transmission in the community of COVID 19.
And so what happens is essentially no swab kids get sent out to people about 100000 people each round,
reply randomly chosen, send theirs back and then it gets sent for PCR,
which is a very sensitive diagnostic test that allows us to get an idea of how much SARS-CoV-2 infection there is in the community.
And that's really important because then it's separated from what the tendency is to seek a diagnosis.
So we're trying to get a sense of that.
And so if a lot of those infections are asymptomatic, which may be more the case now that we have more vaccination and also depends on the variant,
some might have more asymptomatic infection than others. Looking at those patterns is really crucial.
We combine that with data on numbers of individuals who have severe disease,
so they go to hospital numbers of people who sadly die infected with SARS-CoV-2,
and those pieces all come together to form as complete as we can a picture of the situation now.
But predicting what's going to happen in future is fraught, and particularly we can say that new variants will arise.
I'm very confident saying that this isn't the last important variant,
but what it will look like where it would show up first, and nobody can say with any certainty.
So there are some inherent uncertainties in the system, but we can be prepared and know what data to analyse when they do arise.
That's a great summary of what I know is a very complex practise and methodology and all sorts of different kinds of data analysis ongoing.
So thank you very much, Crystal, for sharing your research and your expertise with us.
And thank you also for watching your videos. I hope that you will watch some of the other videos in the project series and please also do fill out the
feedback forms so that we can help to further inform and shape research at the University of Oxford.
Thank you very much.