Prior infection

In an earlier post I referred to an Austrian study in which a random sample of the inhabitants of a region had been tested for the Coronavirus. In that article I stressed the importance of random sampling in order to be able to extrapolate the conclusions from the sample to a wider population. But a limitation of that study was the fact that tests were ‘standard’ COVID-19 tests, providing information only on whether an individual currently carried the disease or not. So, although the infection rate was only 0.33% in the sample, it was impossible to say what proportion of individuals in that sample had ever had the disease, perhaps having only slight or no symptoms.

This week a similar study has been reported based on a study in the German municipality of Gangelt, close to the Netherlands border. But in this case, the 500 individuals in the sample were tested for Coronavirus antibodies, a test which provides information on whether an individual has a prior COVID-19 infection, regardless of whether they currently have the disease.

Now, there has been something of a mis-handling of such tests in the UK, with millions of bought tests of this variety which were not properly evaluated prior to purchase and shown subsequently to be worthless in terms of their results. But as far as I can tell, this isn’t an issue for the tests used in the German study.

The results of the study are interesting, and shed both a positive and negative light on the effects of the epidemic.

First, some context. On February 15th, a couple of weeks after Germany reported it’s first case of Coronavirus, Gangelt held its annual carnival. Subsequently, several attendants at the carnival tested positive for Coronavirus, and shortly after the town became a hotspot for the infection within Germany. It’s widely assumed that the carnival was focal in causing the local hotspot and for attendants then assisting the spread of the disease countrywide. So, it’s thought that the infection rate in Gangelt is likely to be relatively high, which is one of the reasons it was chosen for this study.

However, the random sampling antibody study found that ‘just’ 14% of the sample are likely to have a prior infection. Still, 14% is considerably higher than the proportion that have tested positive in the region (around 3.5%), implying that the true death rate due to COVID-19 there is around 0.37% as opposed to the 2% or so that’s stated for Germany as a whole based on standard data. That’s obviously a welcome piece of news.

On the negative side, as discussed in earlier posts – here for example – one solution to the pandemic will occur naturally when a sufficient proportion of the population have a prior infection and are hopefully immune – though this is not guaranteed – from subsequent infection. But this is thought to be as much as 80% for COVID-19. So, if in a community that’s thought to have been a bit of a hotspot the previously infected rate is only 14%, it’s likely to be a long way short of the required threshold in the country as a whole. In other words, on the basis of this study, Germany – and presumably other countries too – are likely to be very far off from being able to relax social restrictions and rely on herd immunity to get them out of the pandemic.

For the UK, the Harvard epidemiologist William Hanage shows – using some back-of-the-envelope calculations – that to acquire herd immunity in the UK, a minimum of 600,000 COVID-19 deaths would occur. Though the UK government’s initial strategy seemed to be aimed at achieving herd immunity without a vaccine, their realisation of the scale of this number of fatalities and the impact it would have on both communities and the health service is almost certainly what led to a change of heart. But until a vaccine is found, the alternative is maintaining social restrictions to a sufficient extent to keep the transmission rate of the disease sufficiently low. As Hanage says:

This crisis is not close to over, quite the reverse. The pandemic is only just getting started.


A couple of comments:

1. Hanage’s calculation goes like this… At the time of writing there were around 100,000 COVID-19 confirmed cases in the UK. But the British Medical Journal suggests that only around 20% of infected people are actually tested positive. (Based on the Gangelt study the estimate would be 3.5/14 =25%). This implies that the actual number of people in the UK who have been infected is around 500,000. But if the UK is currently at the peak of its epidemic, and a similar number of people will be infected as the epidemic declines, that implies one million infected people at the end of the epidemic. But that means around 65 million people will remain uninfected and without immunity. Similarly, there have been 10,000 confirmed deaths due to COVID-19 in the UK. Assuming fatalities have also now peaked, there will be a total of 20,000 deaths once the epidemic has faded. Now, a very conservative estimate of the proportion of people in the population who need to be infected for herd immunity to kick in is 50%, which implies around 30 million people. But so far, as we’ve seen, it’s likely that only one million have been infected. So, we need 30 times as many people to be infected, and this will imply 30 times as many deaths; i.e. around 600,000.

2. Hanage’s comments are not meant to be fatalistic. His point is simply that there are many positive signs that show social distancing is working in terms of controlling the spread of the epidemic in most countries.  But, these measures are doing only that: controlling the spread of the epidemic. Though numbers of infections are growing in the community, they are still a long way from the sorts of numbers that will inhibit further spread of the epidemic via herd immunity. So until vaccines and therapies are available, it’s inevitable that some form of ongoing social controls will be required to stop the epidemic growing exponentially once more.

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