What do COVID-19 and CLIMATE CHANGE have in common?

Over the last few weeks I have noticed with curiosity, some similarities between the properties of COVID-19 and Climate Change.

Really? COVID-19 and Climate Change? 

Here is non-exhaustive list: See what you think…

1. The science is well-understood

The science behind climate change and pandemics has been broadly understood for decades.

There are poorly-understood details, but the basics facts are rock-solid.

However, many of these small details are important for making predictions, and in both cases, predictions are hard.

2. Humans cannot directly sense the key element

Both viruses and carbon dioxide are invisible.

We have no immediate sense that our homes and cars are emitting tons of anything!

We have no immediate sense of being infected or infecting others.

Thus we have to rely on relatively abstract intellectual understanding of our part in the way carbon dioxide is emitted and that viruses are being transmitted.

3. There is a delay between actions and consequences

Emitting carbon dioxide does not cause any immediate harm. In fact it brings truly phenomenal and very immediate benefits.

Appreciating the appalling magnitude of the long term harm which will eventually come from the climate change which results from those emissions, is difficult.

Hugging our friends and being spontaneous feels great. And mixing freely without bloody masks brings immediate benefits – happiness – and improved economic well-being.

Appreciating the exponential nature of viral spread and the delayed consequences of carelessness is not intuitive.

4. Inaction is cheap and easy: Action is expensive and hard 

Ignoring the basic facts of climate change allows governments and individuals to devote resources to other things: short-term economic growth or, say, new cars and holidays.

Ignoring the basic facts of the pandemic allows governments and individuals to focus on other things – like economic growth or foreign holidays.

The consequences of inaction on Climate Change will not be felt for many years. For the pandemic, failure to act is felt just weeks and months later.

But in both cases, politicians have strategies for evading responsibility, blaming for example, uncertainties of scientific predictions.

5. Confusion is a tactic 

Action against COVID-19 or Climate Change requires concerted behaviour-changes from the population.

People who do not want restrictions on carbon dioxide emissions deliberately sow confusion that makes it hard for the general public to fully appreciate the magnitude and inevitability of the damage to which we have already committed ourselves.

People who do not want economic restrictions deliberately sow confusion that makes it hard for the general public to fully appreciate the basics of viral transmission and inevitable death toll from inaction.

In both cases, they seek to undermine expert opinion and scientific consensus, creating the illusion of real uncertainty in which inaction can be justified.

6. And finally…

People who minimise the importance of climate change tend to be people who also minimise the significance of the COVID-19 pandemic.

Curious.

COVID-19: Day 268: Here we go again…

My puzzlement at what is happening pandemically is now over..

• Things are very obviously getting worse. 
• But thankfully about five times slower than in the Spring

Let’s look at the data.

Data#1. Prevalence

Since late April the ONS prevalence survey has been randomly testing people each week to look for the virus. They then collate their data into fortnightly periods to increase the sensitivity of their tests. Details of their full results are described methodically in this ‘bulletin‘.

Click for a larger version

The number of people tested and the number of positive tests are given in their table above. They estimate that at the end of the measurement period on 19th September 2020 roughly 2 in 1000 of the UK population were actively infected.

Their data – graphed below – suggest that the prevalence had been below the 1 in 1000 level for several months but that it is now definitely above that level and doubling roughly every 15 days.

Click for a larger version.

The raw count of positive tests was:

• 163 from 79,901 people tested in the two weeks to 19th September,
• 56 from 61,023 people tested in the preceding two weeks, and
• 26 from 46,107 people tested in the two weeks preceding that.

Because the data are changing so rapidly I have fitted an exponential curve to the last three points and this is shown as a black dotted line (– – –) on the graph above. This is not a prediction: it is a guide to say that “if things continue on this trend, this is what will happen.”

The last data point on the graph refers to the fortnight centered around 12th September. In other words, it is almost two weeks out of date. I expect that Chris Witty and Patrick Vallance had early sight of this data when they announced that things were going pear-shaped at the start of the week.

If the trend continues the prevalence will double every 15 days and increase by a factor 10 in roughly 50 days.

Because we expect this rise to be exponential, we can also look at this on a logarithmic plot. This covers a much wider range and we can see that – if the trend continues – the prevalence will exceed 1% of the population sometime in mid-October.

Click for a larger version

Data#2. Tests and Deaths

The graph below shows:

• the number of deaths per day.
• the number of people newly admitted to hospital each day
• the number of positive tests per day on the same logarithmic scale. 

The data were downloaded from the government’s ‘dashboard’ site.

• The deaths refer to deaths within 28 days of a test.
• Positive tests refer to Pillar 1 (hospital) and Pillar 2 (community) tests combined.
• Hospital admissions for the UK nations combined are no longer being reported because the Scottish data are no longer being reported since 17th September. The curve shown is the sum of data for each country excluding Scotland after 17th September. 
• All curves are 7-day retrospective rolling averages.

Click for larger version.

The graph above shows the data across the period of the pandemic. From May to July, all three quantities have decreased with (very roughly) the same time constant – the same slope on this logarithmic graph.

The graph below shows the data since July.

Click for a larger version.

Looking at this more closely, we that the data tell a clunkingly obvious story:

• Tests begin to rise, then
• Admissions begin to rise, then
• People start to die.

Click for a larger version.

To highlight this I have drawn rough trend-lines against each curve with the same slope. Physically, if phenomena have the same decay rate, it is likely that they are linked, either causally or by an underlying common cause.

We see that the first sign of the viral spread was in early July when tests began to deviate from their previous exponential decline.

About 3 weeks later, Hospital Admissions began to deviate from their previous exponential decline.

The data for deaths are noisy – because the number of people dying was so small, but it looks like there was a 5 week delay before deaths started to rise. This long delay could be plausibly understood if infections only reached vulnerable people indirectly through an intermediate population of non-vulnerable people.

Summary

The data are – in my opinion – decisively clear.

The prevalence data are unequivocal: the virus is spreading with a doubling time of 15 days.

• This is about 5 times slower than the initial rise in March/April.
• This gives us time to respond but the response needs to be drastic and immediate.
• In my opinion the current response does not meet this challenge.

The testing, death and hospitalisation data are unequivocal:

• This story is more complicated than the prevalence data because the way community infections feed through to Hospital Admissions and eventually deaths is complex.
• There are factors that depend upon community behaviour and age that make predictions difficult.
• The current doubling time for Hospital Admissions appears to be about 18 days.

Together, the data all point to the fact that the viral prevalence is growing and if unconstrained will lead to more deaths and another inevitable total shutdown.

What Next?

It is important to understand that the current rate of deaths – about 30 per day – arises from Hospital Admissions that occurred a few weeks ago.

Click for a larger version.

The current rate of Daily Hospital Admissions has risen by a factor three since early September. So we are probably already committed to death rates of roughly 90 to 100 people per day in mid-October.

Last week I said:

The key question is:

• With the viral prevalence we now have, and the mode of conducting our lives that we have now adopted – to what rate of hospitalisation and death have we committed ourselves?

I don’t think anyone knows the answer to that question. But we will all find out fairly soon.

We are finding out now. We have probably already committed ourselves to a period of several hundred COVID-related admissions per day, and around a hundred of COVID-related deaths per day.

Given that around 1700 people die every day in the normal course of events, we may consider this acceptable.

But COVID-related death rates of thousands per day will follow if the Government do not act.

Personally, I would advocate:

• A series of planned lockdowns from now until the spring of 2021. This will allow workplaces, schools and universities to plan and avoid the current chaotic uncertainty.
• The required ON/OFF periods could be optimised but I would guess that they would be roughly 3 weeks ON/ 3 Weeks OFF.
• The intervention should start now.

Come December, we will all desperately need a break!  But without immediate action on this scale, I fear that Christmas will be cancelled.

COVID 19: Day 264: Worrying about the effect of ‘false-positive’ tests

Click for a larger version. The upper graph shows the number of Pillar 1 (Hospital) and Pillar 2 (Community) tests per day versus the day of year. The middle graph shows the number of those tests yielding positive results for COVID-19. The lower graph shows the ratio of the upper two graphs to show the fraction of tests which are positive. The minimum value of that ratio is shown as a dotted line and represents a reasonable best guess at the MAXIMUM rate of ‘false positive’ tests. All data are 7-day retrospective averages.

I wrote yesterday about the difficulty of interpreting the recent rise in positive COVID-19 tests.

One point that I did not cover was the issue of false positive results.

Now that we are testing 200,000 people each day, even a small rate of false of positives (say 1%) would give rise to 2000 positive results each day. This can be compared with roughly 4000 positive tests per day at the moment.

Prof. Carl Heneghan argues that the tests may include many false positives caused by detection of ‘dead’ viral fragments.

But unfortunately nobody knows what the rate of false positive results actually is!

However we can estimate an upper limit.

Estimating the upper limit of false-positive tests#1

To estimate the upper limit of false positive tests we assume that

• The false positive rate has not changed significantly during the course of the crisis.

Then we look for the minimum registered fraction of positive results – and assume that they were all false positives. This is likely to be an overestimate of the false positive rate

I have downloaded data from the Governments Coronavirus ‘Dashboard’ to evaluate this. The data – all shown as retrospective 7-day averages – are shown in the figure at the start of the article and plotted versus the day of the year

• The upper graph shows the number of Pillar 1 (Hospital) and Pillar 2 (Community) tests per day.
• The middle graph shows the number of those tests yielding positive for COVID-19.
• The lower graph shows the ratio of the upper two graphs to show the fraction of test which are positive.

The minimum value of that ratio (0.0052 or 0.52%) is shown as a dotted line and represents a reasonable best guess at the MAXIMUM rate of ‘false positive’ tests. The true rate is probably lower than this.

Estimating the upper limit of false-positive tests#2

We can also perform a similar analysis for the Pillar 4 tests – those used for the ONS Survey – that I mentioned in a recent post.

Click for a larger image.

Looking at the data from July 3rd to 16th, the positivity rate was just 0.05% – 10 times lower than the maximum false positive rate from the Pillar 1 & 2 data. The maximum false positive rate cannot be higher than this.

Conclusion

There is no evidence that the false positive rate is materially distorting our view of corona virus spread.

Using either estimate does not materially change any of the conclusions I drew yesterday.

The demonstrably low rate of false-positives also speaks against the concerns of Professor Henegan that the government may be ‘chasing shadows’.

Living with the virus…

The BBC ask today Is it time we learned to live with the virus?

This article draws on the arguments of Prof. Heneghan who argues – reasonably – that we should focus on the harm caused by the virus (disease and death) rather than just the mere presence of the virus or of an infected person.

He and others argue that while COVID-19 may be worse than Flu, it is a disease in the same category of respiratory viruses.

And in the same way that we accept deaths from flu of between 20,000 and 50,000 per winter, and we should accept a similar rate of loss to COVID-19.

This is a good point. But the difficulty is that if the wrong choices are made, the death toll could be up to 10 times higher.

Edit at 16:30 on 21 September

Altered text to add a second way of estimating false positives.

COVID-19: Day 262: Update

Summary

My puzzlement at what is happening continues.

• On the one hand the viral prevalence appears to be increasing and a prudent approach prioritising public-health over the economy would indicate strong action – lock-downs and similar – is required urgently.
• On the other hand, an increasingly vocal group is arguing that the government are chasing shadows, and that an epidemic explosion similar to that we experienced in the spring is not about to occur.

Worryingly the views about what is happening have a political dimension. So it is easy to find oneself inclined to one camp or the other based on feelings of general sympathy rather than particular facts. Many people are totally fed up with the restrictions and the COVID-related pallava, and would happily just ignore the whole thing. But would that be wise? And would it cause thousands of deaths each day as it did in the spring?

So what is actually happening?

Back in March the virus had spread uncontrolled through the population for a couple of months and was on an exponential rise. It is my understanding that no reasonable person would dispute that, if left uncontrolled,  it could have killed on the order of half a million people. At the peak of the deaths a few weeks after lock-down, about 1000 people a day were dying after an agonising illness.

The consensus is that this was stopped by the ‘lock-down’ and that subsequent measures have contained the virus. The current rate of COVID-related death rates (about 10 a day) is probably acceptable indefinitely until a vaccine arrives.

Since July 4th there has been a slow rise in COVID-19 positive tests per day, and two weeks ago there was a sudden sharp rise. But the interpretation of the rise is open to question:

• On the one hand the Government take the rise as an indication that the viral prevalence is increasing. They warn that the virus may explode just as it did in March.
  • See Neil Ferguson on the subject
• And on the other hand, critics point out that hospitalisations are not rising and that the protocols for testing have changed – concentrating on areas where the virus is known – creating an ‘echo chamber’ of alarm.
  • See Carl Hennegan on the subject

Both these views are probably true, but the cost of locking-down is immense, as is the potential cost of not locking-down soon enough if a lock-down is required.

Before trying to figure out what we should do, we should look at the data…

Data#1. Prevalence

Since late April the ONS prevalence survey has been randomly testing people each week to look for the virus. They then collate their data into fortnightly periods to increase the sensitivity of their tests. Details of their full results are described methodically in this ‘bulletin‘.

Click for a larger image.

The number of people tested and the number of positive tests are given in their table (reproduced above) along with their estimate that on the 5th September 2020 roughly 1 in 770 of the population were actively ill.

Their data – graphed below – suggest that the prevalence has been below the 1 in 1000 level for several months but is now almost certainly above that level: the raw count of positive tests was 87 from 66,717 in the two weeks to 10th September, up from 36 in 51,992 in the preceding two weeks and 22 from 39,998 in the two weeks preceding that.

Click for a larger image.

My conclusion is that viral prevalence in the general population around the 10th September was two to three times what it had been at it its minimum in July and August.

It has probably risen further in the 17 days since the last data point

Data#2. Tests and Deaths

The graph below shows:

• the number of deaths per day.
• the number of positive tests per day on the same logarithmic scale. 

The data were downloaded from the government’s ‘dashboard’ site. The deaths refer to deaths within 28 days of a test and the positive tests refer to Pillar 1 (hospital) and Pillar 2 (community) tests combined. All curves are 7-day retrospective rolling averages.

Click for a larger image

The rapid rise in the number of positive tests is probably the result of a genuine increase in prevalence, coupled with a change in the testing protocol i.e. more testing in suspected ‘hot spots’.

In the last couple of weeks the generally downward trend in deaths per day has shown fluctuations and appears to be starting to increase. It is certainly not falling.

Are we on the verge of a viral resurgence? Or Not.

The ONS survey and the testing data indicates an increase in viral prevalence,

But if we plot the number of people hospitalised alongside the test and death data in the graph above, we see that increase in tests has not resulted in a concomitant increase in hospitalisations or use of ventilators.

Click for larger version

The data show that up until the start of July, the data for tests, hospitalisations, ventilations and eventually deaths all followed the same pattern.

But since then the roughly 10 fold increase in positive tests per day has not been matched by similar increases in hospitalisations.

In order to understand the above graph, one needs to understand that although the significance of three of the data streams has remained unchanged across the graph – the significance of a positive test has changed significantly.

To see this it is best to split the above graph down the middle and consider the left and right hand sides separately.

Click for larger version

Let’s consider the left-hand side of the graph first: 

Click for larger version

Here the positive tests arose as the COVID-status of a seriously ill person entering hospital (Pillar 1 testing) was confirmed.

• Typically patients were already in a a vulnerable group.
• Typically they had already been ill for 2 to 3 weeks before entering hospital, and around 20% of these people would die within about 3 weeks (link).
• Thus the link between a positive tests, hospitalisation, ventilator use and death were striking and easy to see.

Now consider the right-hand side of the graph: 

Now most tests are carried out in the community (Pillar 2) and only around 1% are positive.

• The vast majority of positive tests are amongst people who are not in a vulnerable group and who will never need to go to hospital.
• Even for people who will eventually become hospitalised, the test will come much earlier in the course of the disease.

What next?

The data are not – in my opinion – decisively clear. This is how I read the graphs.

• The rising number of Positive tests are consistent with the ONS data on rising prevalence. The significance of the kink at the start of September is not yet clear but is probably a result of a real increase and increased testing in hot-spots.
• The fall in the number of COVID hospitalisations flattened out at the start of September.
  • This statistic is the difference between admissions and discharges, and so there must have been a rise in the rate of daily admissions beginning around late August/start of September..
• Curiously, the previously falling number of COVID-patients on ventilators flattened out before the hospitalisation curve. I can’t think why that might be.

What has probably happened is this:

• The virus has been spreading and increasing in prevalence since July.
• Summer holidays, and increased activities of all kinds have allowed the virus to spread, mainly amongst younger people.
  • Younger people are less seriously affected and thus have not caused an immediate increase in hospitalizations.
• With increased activity and the re-starting of schools the spread is now reaching vulnerable groups leading to increased deaths and hospitalisations.

The key question is:

• With the viral prevalence we now have, and the mode of conducting our lives that we have now adopted – to what rate of hospitalisation and death have we committed ourselves?

I don’t think anyone knows the answer to that question. But we will all find out fairly soon.

COVID-19: Day 261: Do you remember Day 75? 16th March 2020

Previously I said:  

“I am having difficulty grasping ‘the big picture’ about what is going on with the pandemic.”

And I am still struggling. I will re-visit this week’s data in a future article, but I here I just wanted to remind you – and myself – of what has been previously predicted about this pandemic – back in March 2020 – a week before lockdown.

Remember March 2020?

One of the news stories then was the change in Government policy as a result of a briefing by Neil Ferguson’s group at Imperial College which predicted that:

• if no action was taken the corona virus would cause around half a million deaths in the UK over the course of a few months.

The Government deemed this unacceptable and as a result of actions taken:

• Based on recent antibody tests, only around 6% of the UK population have been exposed to the virus.
• Deaths have been restricted to less than a tenth of the ‘no action’ alternative.

Despite many failures for which the government deserves to be criticised, the saving of around 450,000 lives is a real achievement.

I remember reading Ferguson’s report dated 16th March 2020 which The Guardian published in full as a special supplement.

• The report is available here

As I read the report I was shocked by the prediction that even if we ‘locked down’ and prevented our health services being overwhelmed, the virus would still be present and would simply rise again.

The report predicted that – in the absence of a vaccine – we would need to have repeated periods of opening and closing of the economy which would be determined by the extent to which the critical care infrastructure was being overwhelmed.

Click for Larger Version

Figure 4 from that report is reproduced above.

Given all the uncertainties involved – I think our current situation is pretty well described by this graph.

Why do I mention this?

I can’t think of any reason why predictions of viral transmission though  a population should depend on one’s political view point.

But the right-wing press (paid link, paid link) frequently seek to portray the predictions of Neil Ferguson’s group at Imperial College as flawed – without ever being specific.

They state that the group predicted half a million deaths and that this did not happen. This is true.

What the right-wing press do not state is that the reason we have not had half a million deaths is that the government acted on Neil Ferguson’s predictions of what would happen if they didn’t do anything. 

In fact – given the uncertainties involved in the prediction from way back in March – uncertainties of policy and in knowledge of the viral properties – I would say that this foretelling of our future – now our present – looks to have been spectacularly prescient.

I mention this because – as I see it now – a “Winter of Discontent” is looming.  And in these difficult times we need to be careful about who’s views we trust.

Personally I think the people who predicted our current situation with such prescience, deserve more credit than they are currently being given.

COVID-19: Day 256 Update: I am feeling uneasy

Michael: How are you feeling this week?

Thank you for asking. I am well.

Last week I said:  “I am having difficulty grasping ‘the big picture’ about what is going on with the pandemic.”

This week I still feel the same. But things are becoming clearer. And I think I am beginning to understand the fundamental reason for my unease. It is the unreliability of every single measure of the prevalence of the virus.

Considering this as a measurement problem I realised that…

• …all the measures we have of the prevalence of the virus are imperfect.

We’ll look at the latest data below, but here I will just summarise how we get to know anything at all about the viral prevalence.

How we measure the viral prevalence:

• The ONS prevalence survey takes samples from people randomly-selected from around the UK.
  • It tests people whether or not they have symptoms.
  • It samples the adult population reasonably fairly with regard to age, ethnicity, location and social class.
  • But even sampling 25,000 people each week it is not very sensitive.
  • Also, if the geographical distribution of the viral infections is not random (which it isn’t) then the survey can easily miss (i.e. under-sample) ‘hot-spots’.
  • The lag between measurements and analysis is several days to weeks.

• The death count.
  • Probably the most reliable indicator of the spread of the virus, but even counting bodies is not straightforward.
  • The death count is blind to the amount of infection amongst younger people – in general they don’t die..
  • And it lags the time of active infection by around three to four weeks. So by the time the older infected people start dying – the virus may have already passed through three to four generations of infection and be widespread in the younger community.

• Hospitalisation.
  • Like deaths, this statistic lags the infection, but by less time than the death count.
  • Like deaths, this statistic is blind to the amount of infection amongst younger people – in general they don’t need to be hospitalised.

• PCR Tests
  • The PCR swab tests from the tonsils (or nasal passages) test for COVID-19 genetic material. But the tests are themselves imperfect indicators of the amount of virus present and whether it is alive or dead. The tests sometimes detect remnant dead virus fragments, and sometimes fail to detect live virus.

• Pillar 2 (Community testing)
  • The meaning of the pillar 2 tests changes depending on the testing strategy and protocol.
  • This makes it hard to associate any increases or decreases in the Pillar 2 tests with a sign of increased or decreased prevalence.
  • Mass (Pillar 2) testing in suspected ‘hotspots’ is certainly good for making rapid assessments of areas of known high infection – but the exact significance of the measured prevalence from one hotspot to another is not clear because of differences in community behaviour and testing protocols.
  • The UK testing infrastructure appears to have bottlenecks and its actual performance may be obfuscated for political reasons (examplar). One curious example is that the government still do not clearly distinguish between tests processed, and the number of people tested.

• Symptom surveys
  • These surveys ask people to fill in an app-based questionnaire daily, reporting any symptoms.
  • Through mass participation, this can detect the onset of infection amongst participating social groups with only a few days delay.
  • But these surveys cannot detect the virus pre-symptomatically,and are only weakly sensitive to the 80% (yes, 80%) of people who are infected a-symptomatically or with only mild symptoms. (Yes, 80% – link).

• ONS Antibody tests
  • The ONS antibody tests provide an insight into how many people have been infected in the past – the answer is about 6% of the UK population.
  • But there are still unanswered questions about whether all infected people produce an antibody response

Why I feel uneasy.

So in order to get a picture of what is happening look we need to look at all these measures – and each one needs to be interpreted with nuance. And we need to seek a coherent picture that is consistent with all the data.

Additionally the government endlessly changes data formats and presentations to make coherent and consistent analysis difficult. This is possibly deliberate but could also be a result of chaotic incompetence.

Summarising, my unease arises from being unable to establish a coherent narrative about “what is going on”. This is not a narrative that seeks to blame any particular group, but one which just states the facts as well as we know them without any spin.

Indeed. I am writing this to try to clarify my own thoughts.

This week’s nuanced analysis.

Based on the data below I note that:

• The number of positive cases has risen sharply. The sharpness of the rise is almost certainly an anomaly caused by a change in testing strategy or protocol – it just doesn’t look right! – but there is a consensus that this probably reflects a real rise.
• There is an increase in the daily rate of deaths – but there has not been any obvious pre-cursor of this in the positive tests.

The government’s ‘policy roulette’ has come up with a “rule of six” and a ‘Moon Shot’ testing programme.

• The “rule of six” (RO6) represents a continued arbitrary breathtaking assault on our freedom.
  • It is not clear that it will be widely adhered to – especially if it is expected to be adhered to nearer to Christmas (103 days away).
  • The government’s endless U-turns and their disregard for their own commitments gives them little moral authority.
  • But it is clear that the ‘new normal’ we have been experiencing in the last couple of months is not working well enough to suppress the virus.
  • So the RO6 is probably the sort of extreme measure that might significantly affect virus transmission: some disagree.
• The Moonshot testing programme is nonsense from top to bottom.
  • The existing testing programme is being chaotically mismanaged (link). Making it bigger will likely make a bigger mess.
  • It is likely that a vaccine from one source or another will become available in the early part of 2021 – and it would be much cheaper and more effective than a testing programme.

Data#1. Prevalence

Since late April the ONS prevalence survey has been randomly testing people each week to look for the virus. They then collate their data into fortnightly periods to increase the sensitivity of their tests. Details of their full results are described methodically in this ‘bulletin‘.

Click for a larger image.

The number of people tested and the number of positive tests are given in their table (reproduced above) along with their estimate that on the 5th September 2020 roughly 1 in 1300 of the population were actively ill.

Their data – graphed below – suggest that the prevalence has been below the 1 in 1000 level for several months and has increased recently: the raw count of positive tests was 55 from 59,222 in the two weeks to 5th September, up from 26 in 45,959 in the preceding two weeks. But this survey lacks the sensitivity to track rapid local increases in prevalance.

Click for larger image.

 

Data #2. Other ONS conclusions

ONS also analyse antibody data and conclude on the basis of just over 7000 tests that – as in previous weeks – roughly 6.02% ± 1.1% of the UK population have already been exposed to the virus.

Data#3. Tests and Deaths

The graph below shows:

• the number of deaths per day.
• the number of positive tests on the same logarithmic scale. 

The data were downloaded from the government’s ‘dashboard’ site. The deaths refer to deaths within 28 days of a test and the positive tests refer to Pillar 1 (hospital) and Pillar 2 (community) tests combined. All curves are 7-day retrospective rolling averages.

Click for a larger image

The rapid rise in the number of positive tests looks unfeasibly sharp and is probably the result of a genuine increase in prevalence, coupled with a change in the testing protocol.

In the last couple of weeks the generally downward trend in deaths per day has shown fluctuations and increases whose significance is still not yet clear.

I am puzzled because the rise did not seem to be associated with any corresponding change in positive tests in the preceding weeks.

What to make of all of this?

I don’t know the true story that links all these facts. Worryingly, I don’t think anybody knows what is going on.

But is difficult not to expect several more weeks of increased cases and eventually deaths.

 

COVID-19: Day 252 Update: Autumn

As I said last week, I am having difficulty grasping ‘the big picture’ about what is going on with the pandemic.

Even with the benefit of a couple of hundred days of thinking about it, I still find myself confused by the basic facts of this virus:

• That it is mostly harmless for most people.
• That it has the potential to kill hundreds of thousands within a few months if left uncontrolled.

But my view of what is happening in the UK is becoming clearer and I will outline that below. First let’s look at the latest data.

Data#1. Prevalence

Since late April the ONS prevalence survey has been randomly testing people each week to look for the virus. They then collate their data into fortnightly periods to increase the sensitivity of their tests.

The number of people tested and the number of positive tests are given in their table (reproduced below) along with their estimate that roughly 1 in 1700 of the population were actively ill in the two weeks around 19th August 2020.

Click for Larger Image

Their data – graphed below – suggest that the prevalence has been below the 1 in 1000 level for several months, but that there is no systematic trend towards lower prevalence.

Click for larger image.

Data #2. Other ONS conclusions

ONS also analyse antibody data and conclude on the basis of just over 7000 tests that – as in previous weeks – roughly 6.02% ± 1.1% of the UK population have already been exposed to the virus.

On the basis of a statistical model, they also conclude that there were roughly 2800 infections each day during the week including August 25th, with a daily incidence increasing at roughly 100 infections (3.5%) per day.

Since there were roughly 1200 positive tests each day during that week, we can estimate that less than half the infections are being found as they occur.

Data#3. Tests and Deaths

The graph below shows three curves:

• the number of deaths per day.
• the number of positive tests on the same logarithmic scale. 
• the fraction of tests conducted which are positive shown on a separate logarithmic axis on the right hand scale.

The data were downloaded from the government’s ‘dashboard’ site. The deaths refer to deaths within 28 days of a test and the positive tests refer to Pillar 1 (hospital) and Pillar 2 (community) tests combined. All curves are 7-day retrospective rolling averages.

Click for larger image.

The data suggest a rising incidence which started just after the official ‘re-opening’ of the economy on July 4th.

I draw this conclusion not just from the rising number of positive tests, but also the small rise in the positivity rate of the tests.

• The ‘number of positive tests‘ statistic is difficult to interpret by itself because its value depends on the number of tests and the testing protocols.
• The number of tests has increased dramatically over the period of the graph: there are now over 175,000 tests each day. Alongside this increase in tests per day, the positivity rate for tests has declined from 50% around the start of April, to less than 1% since the start of July. It is now rising slowly, suggesting that the virus is ‘easier to find’.

In the last couple of weeks the generally downward trend in deaths per day has shown fluctuations whose significance is not yet clear.

What to make of all of this?

The prevalence of people ill with the virus is low enough (below 1 in a 1000) that most people can get on with many parts of their life while maintaining social distance.

But the prevalence is increasing systematically. This growth means – by definition –  that R is bigger than 1.

The cases are mainly amongst younger people who are at little risk themselves (hence the low death rate), but their continued infection serves to spread the infection around the country.

As we enter autumn, there are many uncertainties, but it seems that several factors will likely increase R further. I say this because I can’t see any way these factors could act to reduce R.

• The return to school will result in more interactions between otherwise separate bubbles, not just within schools, but also at peripheral activities.
• The return to universities will likewise result in more interactions between otherwise separate bubbles.
• The colder weather will move gatherings of all kinds indoors where viral spread is harder to prevent. And colder weather will probably allow easier infection.

With all these steps, it seems inevitable that there will be continued outbreaks around the country this autumn and winter. The local ‘lockdown’s in Leicester and Manchester are likely to be repeated elsewhere.

If the infection spreads amongst the young, then there should be very little associated mortality, and one might argue that this would be just fine if this allowed the economy to fully re-start.

But with high infection rates it would seem likely that eventually many vulnerable people would be affected. Especially if we factor another winter event:

• Christmas: Just 110 days away, Christmas will form the perfect, trans-generational super-spreader event.

Recall that at the end of January 2020, Chinese New Year was all but cancelled in the People’s Republic of China (PRC). I recall this being reported as the equivalent of “cancelling Christmas” in the West.

But PRC has a dictatorial government and the virus threat was still new in January. I think that the UK government would not stand much chance of stopping Christmas gatherings especially after the year we have had.

What does this tell us?

Last week I asked

• Does the data tell us that we have a low-enough incidence of COVID-19 such that it can be managed by ad hoc local closures until a vaccine arrives?
• Or does the data tell us that the virus is continuing to infiltrate its way throughout our society, ready to spread rapidly as soon as an opportunity arises.

I think that both these statements are true. The situation we are in is manageable – as it is now.

But with schools and universities opening, colder weather, and Christmas on the horizon, it is hard to see how we will manage to keep the death rate this low over the coming months.

A safe and effective vaccine cannot arrive soon enough.

====================

Edited on 6/9/2020 at 21:30 to remove incorrect information about common colds.

NPL: Last words

Follow up on Previous Comments

Previously, I wrote about Serco’s role in facilitating NPL’s decline to its current state, which – as I experienced it – featured a poisonous working environment, abysmal staff morale, and a management detached from reality.

Having been conditioned for several years that ‘the truth’ can never be spoken, it felt frightening to simply say out loud what had happened at NPL. And indeed what is still happening there.

Several people contacted me ‘off-blog’ about that article and I would like to thank everyone who did.

Only one person offered a significantly differing narrative, arguing that in fact NPL’s problems were aggravated by – paraphrasing – a preponderance of old white men – alpha males – amongst the scientific staff. It was their maleness rather than their whiteness which the correspondent saw as being of primary significance. I didn’t really think that this was a key issue – but then I am an old white man. While there are many issues around gender and race to be addressed in engineering and science organisations, I had thought NPL seemed to deal with them reasonably well. I posted their response anyway, but they later requested I delete it.

Another correspondent  who had been closer to management during the 2000’s reminded me of many of the difficulties Serco were having during this time. Particular events involved competing with Qinteq for the NPL ‘franchise’ and being forced to lower their margins by the government. As I reflected on this, I thought that these were areas of strength and competence for the Serco managers. And their focus on these issues probably allowed them to feel they were ‘doing something’, and distracted them even further from the simple fact that they did not have a clue how to run a scientific organisation.

Time to wrap up

Other correspondents have asked me privately.

“Michael, but what do really think about NPL’s management?”

Four months on from my departure, I am happy to report that I think about them very little.

Initially I had meant to write more about my time at NPL – describing the hilarious antics of the nincompoops now in charge.

But in honesty – I just don’t want to. It’s time to move on.

Flashbacks

I had been very devoted to the work I did, and many people asked me if – despite my relief at leaving – I would miss work. And I wondered that too. But so far, I have not missed it one iota.

Thinking back – so much of my time there has simply faded into nothingness and my memories of the place feel dreamy.

What I do remember clearly are the camaraderie and kindness of colleagues and friends. These memories are golden.

But I do still have occasional panicky flashbacks where I remember the poisonous bullying and re-experience the sense of helplessness it was designed to induce.

I am confident these flashbacks will diminish as I replace them with positive memories – such as having my house insulated.

Indeed I wonder sometimes if any of my memories really happened?

• Did NPL Managers really try to sack me three times? Each time for a matter related to my ‘improper’ response to management incompetence?
• Did NPL Managers really throw away the gas with which we measured the Boltzmann constant? The gas whose bottle-specific isotopic composition was critical for NPL’s only contribution to the 2019 redefinition of the SI units?
  • Did they find the bottle of precious gas which was marked as “NPL Critical” and had my personal phone number on and just bin it without asking me?
  • And did they try to sack me for “raising my voice” attempting to stop them?
  • And did my alleged bad behaviour include “crying aggressively” when I found out they had already thrown it out?
  • And did the people responsible really never apologise?
• Was I really told they would try to sack me for a fourth time unless I apologised to a senior manager, but not told why I was apologising? Or what for?
• Was I really told by the Head of Department not to tell them “any bad news”?
  • And did this person later try to sack me because I pointed out their Knowledge Transfer team had “no Knowledge”
  • Did that attempt fail after I was awarded an MBE?
  • Did I really meet the Queen?
• Did a senior advisor to NPL’s board (Cyril Hilsum CBE FRS FREng HonFInstP ) really write to me personally to tell me why anthropogenic global warming wasn’t real?
  • And when I told him I was shocked at his ignorance did he complain about me immediately to management?
  • And did a director stomp into my office and tell me to “shut up”.?
  • Did no one from management support me in challenging his poisonous delusions?

• Did NPL managers try to sack me for suggesting positive ways to waste less of scientists time?
• Did NPL managers really decide to ‘transform’ NPL and then immediately start sacking people before deciding what they were transforming it to?
• Did NPL managers spend hundreds of thousands of taxpayer pounds subsidising multiple contracts to foreign companies and governments?
• Did NPL managers really get rid of the UK’s leading facility for measuring ultra-low heat transfer in structures, literally cutting it up and putting it in a skip?
• Did NPL managers really have a top-level digital strategy that advocated we “Create new units that underpin the cyber-physical world.”?

Thinking back it is hard to distinguish what was really real, from my personal nightmare

But if even a small fraction of my hazy memories are correct, then NPL was (and still is) a showcase for management chaos and incompetence.

Farewell

As I mentioned above, I had initially meant to write more about the surreal and ridiculous specifics of NPL’s ‘transformation’. I researched some details but…

..but it’s just too late. I no longer want to devote any of my time or energy to thinking about NPL.

So farewell to my old colleagues and good luck.

And as Forrest Gump might have said, “…that’s all I have to say about that!”