Tag Archives: testing

Great news from the coronavirus app

NOTE: The first version of this article was based on the Daily Mail article:



Looking at the video by the researchers, for which I’m grateful to Kate Brewer) to the link:


it says that 25-30% of respondents reported ‘some COVID-like symptoms associated with COVID’. It also usefully clarifies that most of the early respondents were likely younger people. This is very different from the 1.9M reported in the DM article and which I re-used in my blog. Humble apologies, I didn’t check the source. Now that I have, I am still unable to find the other figures the DM quoted, so perhaps they used a different source.

So, using the revised figures ….

The coronavirus symptom tracking app results suggest, according to Tim Spector, that 25-30% of respondents reported ‘some symptoms associated with COVID’. Without proper testing, it’s as good an estimate as we’re likely to get. Extending to the whole UK population, there could be 16-20M people who have already had the disease. (As an aside, and I don’t trust Chinese figures, some reports suggest that 20% of people who are infected develop symptoms. For 25-30% to report symptoms, that would mean almost everyone in the UK would need to have been infected).

The app is a sort of self-selected, self-reported test, but presumably proper tests on a proper sample of the population would reveal more. If you’re trying to solve a problem, knowing its dimensions can make a huge difference to the solution you will pick.

If their figure is true, then only 0.2% – 0.25% of people who have had the disease developed into official cases. But we have no idea how many have been exposed to the virus and not even had enough symptoms to become part of their 25+%. It could be anywhere between 25% and 50% (as other studies have cited).

If true, we might already be a quarter or even half of the way through. We might only see another 40,000 – 80,000 cases, even if lockdown is lifted

So far, 3605 deaths have been announced in the UK from 38,168 cases, but the ONS says the death toll could be 20% higher, at 4325. That gives an 11.3% death rate in the UK but that doesn’t include documented cases that will die later (the numbers that have been listed as ‘recovered’ are only a tenth of the deaths, so that is an important caveat). So the UK figure is likely to be much higher than the 11.3%. On the other hand, as Peter Hitchens has often pointed out, that figure is for all deaths that occurred of people who had the disease, not those who died mainly because of it, very different. Large numbers of elderly people die every year. Every day, around 1650 people die in the UK. Any of those who died from the usual causes but also had COVID would appear in the COVID deaths figures, along with any who did die because of it but would have died in a few months of something else anyway.

Without proper testing of a large and representative sample of the population, we really have no idea how many people have actually already had the disease or are resistant, and without proper recording of deaths, how many known cases are still going to die. Only when we have proper large scale test results will we be able to estimate how many future infections, cases and deaths there might be as the result of lifting lockdown before the disease has been eliminated.

However, a simple calculation using the above suggests that if lockdown were lifted, there might only be 10,000 – 13,000 more deaths that might list COVID on the death certificate, and the number of deaths primarily due to COVID would be far less. Perhaps only a very few thousand more people will die because of COVID if lockdown is lifted.

If it really is only 3000 – 5000, there are far better ways to save that number, such as cleaning hospitals better.


Reducing infection rates – common sense

We could greatly reduce suffering, deaths, economic damage and duration of lockdown if the authorities were to apply some basic principles.

Restrict travel between high and low infection areas

Some areas are much more highly infected than others. Travel from highly infected areas to much less infected areas should be severely restricted. The gain from doing so is far higher than by restricting other travel.

Restricting travel within high infection areas will also achieve greater gains than doing so in low infection areas.

Red and green trains

Instead of all trains being made available to everyone, red trains would carry groups more likely to be infected and would be used by people who either live or work in a high-infection area. Green trains would be used by those who both live and work in low infection areas. There doesn’t need to be a very high difference before statistical gains are achieved. Any station would receive a few red trains, then a few green ones.

A further derivative would be to have red and green supermarket hours to separate those who work exposed to high risk from those who aren’t.

Both of the above rely on separating groups that have very different infection rates and both are quite robust against moderate cross-infection.

Travel profiles indicate most effective use of limited testing

We already target health workers and carers, but what about the rest of the population?

The faster we can identify infected people and isolate them, the more we can reduce the rate of spread, the number of total infections, overall suffering, and deaths. Given very limited testing capacity, we must optimise our approach. Some simple reasoning applies.

First, there is little point in testing those in lockdown. It would be nice in an ideal situation but we aren’t in one. The few who become infected will still emerge if they become ill enough.

The rest fall in two categories. One group travels mostly alone in private vehicles. A few will come into contact with large numbers of people through their work. If we can identify those high-contact groups, they can be allocated a higher priority.

Those travelling most on public transport are much more likely to become infected, coming into more frequent contact with infected strangers and once they become infected, are likely to infect many more. Concentrating testing on them will achieve the greatest efficiency at finding (and removing) infected people from the mix. The more infected people that can be found and removed from public transport, the faster the virus will be controlled. We know who uses public transport most via their payment cards. We  also know that those using red trains will have higher incidence than those on green trains.

Simple logic therefore shows that limited testing should therefore be applied in the following priority:

  1. Front line carers
  2. Most frequent travellers on red-train public transport
  3. Less frequent travellers on red-train public transport
  4. Most frequent travellers on green-train public transport
  5. Less frequent travellers on green-train public transport
  6. Those living in red areas who travel mostly using private transport
  7. Those living in  green areas who travel mostly using private transport
  8. Those in lockdown who must still venture out sometimes
  9. Those in total isolation

This isn’t 100% optimised, but it is close enough.