An epidemiologist’s dream

There’s no escaping the fact that this is a fantastic time to be an epidemiologist. Often regarded as one of the geeky backwaters of medicine with more hours in the library and behind a computer terminal than actually seeing patients, suddenly it finds itself pushed to the front. Throughout the world, epidemiologists are looking for their shirts and ties, as the media circus draws them in.

And who should be surprised. So much of an epidemiologists career is spent writing papers for dusty journals, looking at small outbreaks of far-flung diseases that people can’t pronounce let alone express any interest. Some 30 years of bookish research, then a chair and the respect of your peers. And as for the general public, you have almost complete anonymity.

Epidemiologists dream of a pandemic. A real monster, spreading round the world in real time. The chance to witness an old-fashioned biblical plague playing out in front of their very eyes. They certainly know about pandemics – they read it in the books. The books written by other epidemiologists. But to witness one first-hand is beyond their wildest imaginings. It’s like all their birthdays and Christmases at once.

Epidemiologists appearing on television! Who’d have thought it. From the token geek on medical discussion panels, to prime-time television with journalists and the general public hanging on every word they say. Graphs and projections. Who could imagine something so big that Joe Bloggs in the pub (well actually in his own home now I hope) would be using epidemiological language and talking of ‘flattening the curve’ and the role of different social behaviours influencing mortality. This is epidemiology.

It’s rather like a physicist being present at the big bang. It’s the stuff of dreams. Yes, there has never been a better time to be an epidemiologist.

Crunching the numbers

The one problem with being a scientist for many years is that you can take the scientist out of science but you can’t take science out of the scientist. It’s in their nature. When I left research science, I didn’t cease to be a scientist.

I still have a scientists instincts, motivations, thoughts and ideas. I cannot leave a piece of data alone without thinking of alternative analyses or different ways of looking at the same. Like all scientists, when I look at data I think “what if…”.

Like so many others I have watched this unfolding catastrophe with a kind of morbid fascination. The same fascination that prevents you from averting your gaze from a car crash. And the numbers emerging from the WHO and elsewhere (Johns Hopkins is a very good page) are fascinating in their insights into each national response to the pandemic. The data are, to use President Trump’s comically inappropriate adjective “beautiful”.

But you have to know how to think about the data. The raw numbers themselves are a code and it’s up to the scientists to decode the information. Let me give you an example.

Take the UK for instance (not that anybody would bother). 8000 cases of coronavirus more or less. 400 dead more or less. On the face of it that amounts to a 5% death rate for the condition. The number of dead divided by the number of cases. 5% is an awfully high figure even for this virus so can it be accurate? Other countries publish much lower kill rates. How can this be?

It comes down to testing. Covid-19 is not the only illness to present with a dry cough. Nor is it the only illness characterised by fever, aches and pains. And it wouldn’t be the first lung infection to mature into pneumonia. So a patient presenting with any or many of these symptoms could well have Covid-19. It’s a fair bet.

But it’s exactly that – a bet and not a certainty. The only way to be sure that it is Covid-19 is to test. Without the certainty of a test result, it is no more than a backed hunch. So it’s clear that you have to test in order to have a firm diagnosis, the correct treatment plan and the appropriate recording of outcome whether good or bad. Individuals need to be traced and tracked through the entire sequence of diagnosis to treatment to outcome.

In the UK, although things are changing rapidly, patients are mostly tested when they present in hospital. Many of these patients will be transferred to intensive care where their outcome will be documented. Not surprisingly, these are very sick people. Many die. So our testing programme in the UK is, until recently, focused on those who present as hospital and are therefore already very sick and thus more likely to die.

In order to get a true picture of the mortality of Covid 19, we need to know how many people in the country either have had the illness or currently have it in a very mild form. We have been told throughout that, say, 4 out of every 5 people who contract the illness will not find themselves hospitalised. We don’t have those numbers because, until recently, it was not part of the testing programme.

Yet these data are critical to our understanding of how the disease spreads and how ultimately it may be defeated. Without this information we are applying controls and measures of uncertain value. Why? Because we can’t assess their efficacy without a knowledge of the whole population. As it stands, all we have is this 5% mortality in the UK. If it turns out that four out of every five recover in their own homes, then we are in reality looking at a 1% rate. Much more plausible.

Don’t get me wrong – an illness which kills 1% of the country’s population, particularly the old and wise, is cataclysmic by any standard. A 5% kill rate on the other hand is apocalyptic.

We also need to be careful of comparing data over different time frames. The number of people diagnosed is straightforward and up-to-date. But the number of the dead does not have the same temporal consonance. Going back to the figures for a moment – 8000 diagnosed, 400 dead – we are looking at different time points. To put it bluntly, most people do not die immediately after being clerked. Patients may be treated for a week, two weeks perhaps, before they die. In this case, the 400 dead should not perhaps be compared with the current 8000 diagnosed but with the figure a week ago. Looking at the date of this way paints a more bleak picture. On this basis, the kill rate is much higher.

Let’s also factor in health service resources. The death rate obviously bears a relationship to the provision of ventilators and staff to operate them correctly. If the number of intensive care beds needed falls below those available, then patients are in with a good chance. If on the other hand the number of beds needed exceeds those available, the outcomes are inevitably going to be worse. In Italy, demand outstrips availability manyfold and doctors are having to triage the arrivals. Triage, most often applied in the battlefield context but then this is a battlefield, means dividing patients broadly speaking into three categories – those that will most likely survive without ventilating, those that will most likely occupy a ventilator and then die and finally those where there is a realistic chance of improving the outcome by treatment. Only the last category will have access to a ventilator. And the doctors in Italy have found themselves having to make those choices.

When the health service resources are inadequate, the death rate rises dramatically. And this is why when there’s been so much talk about “flattening the curve”. I have seen illustrations with graphs, memes and buckets of water. They all illustrate the principle well, that of keeping demand below supply by flattening out the number of cases per week. And on the face of it it looks reasonably optimistic until you factor in the harsh reality of numbers and acknowledged that even the best estimates put demand way above supply. Our health service already cannot cope and we haven’t even begun this battle yet.

Much is said where we are on the curve relative to Italy. Italy has become the tragic illustration of what happens when you don’t get it right. Complacency for perhaps two weeks when the condition wasn’t taken seriously as led to the present viral holocaust. In turn, other governments have learned or ignored the lessons of Italy. Only time will tell.

That is why social distancing, self isolation or whatever we want to call it needs to be taken seriously. Because this is where everyone of us can make a difference. If we become ill, we may need an intensive care bed. And if we are occupying it, somebody else isn’t. By becoming ill, we are in essence depriving another person of life-saving treatment. So when the Prime Minister talks about everybody doing their bit, this is what he means. We have to stay well so that others can stay alive. It’s that simple. For every one of us that stays out of hospital, somebody who needs to will be able to. This is what flattening the curve is all about. Because if we don’t, the alternative is unthinkable.

Mutation is not all bad

People are already beginning to talk a lot about virus mutation, in essence taking the view that the virus is one step ahead of us in our development of a vaccine. The inference is that with every successive mutation it becomes a more dangerous little chap, and we’re left chasing shadows.

I’m no virologist, let me make that clear. But I don’t think that’s the case. My recollection, from the limited amount of microbiology I have been exposed to, is that with time viruses become less pathogenic. Not always but in general.

Look at it from the virus’s point of view, if it had a point of view. The virus has only one purpose in life – to replicate and therefore spread. Any mutation that improves the chances of doing so is likely to be successful. And vice versa. So in terms of propagation of one’s genome, killing the host is not just rather ill mannered but also counter-productive.

You have to remember that being harmful to the host does not improve your chances, as a virus, of proliferating your genome. Actually a pretty bad idea. If a virus is particularly virulent and kills its host in too short a space of time, it actually reduces its reproductive capacity.

It’s a fine balance. The virus needs the host in order to replicate its genome. It’s best chances of doing that are by reducing its pathogenicity or by increasing its infectiousness during the presymptomatic stages in the host. The latter is perhaps more difficult to achieve so in general viruses become less virulent with time. Presumably mutations which cause less damage to the host allow more opportunities for virus transmission.

There are of course exceptions. The 1918 influenza pandemic was biphasic. The first phase, in the early months of 1918 killed many but the second phase in autumn killed twice as many.

There are no guarantees in virus mutation. Each is in essence a throw of the dice. But to mutate to a less pathogenic form makes the virus more successful in its own terms of replication. From a Darwinian perspective it makes sense for the virus to be less harmful. With time, less pathogenic forms will win out. So my message to the coronavirus is to try and be a little less antisocial. Play nice.

*If a proper virologist has time to skim through these ramblings, I would be very grateful. I don’t want to spread misinformation. Or sound like Donald Trump.