Can we ease the lockdowns?

Many countries are starting to move towards relaxing their lockdowns.  Across Europe, some restrictions have been lifted or dates to do so announced for the next few weeks.  In the US, despite starting lockdowns much later, there has been some relaxation already, including Florida beaches and shops in Texas.

This is as I expected, countries reached peak daily death rate around 3 weeks after the imposition of strict lockdowns whilst the clamour to end them builds.  We now see a lot of optimistic headlines and articles about the worst being over now, and how people are impatient to restart their lives.

Unfortunately, my read of the recent data is not so optimistic.

A key metric to look at right now is how rapidly the virus spreads during the lockdown.  We have initial data from the UK whereas Spain, Germany and Italy are 2 weeks further along.  In the last post, I estimated R0 of 0.8 during lockdown but this could be optimistic.  For example, in Italy the ICU cases have dropped from 4,000 to 2,800 in the last 2 weeks which has been hailed as a “dramatic drop” – actually a drop of 30% in 2 weeks implies a lockdown R0 of 0.85.  In the UK we are past the peak in daily deaths, but actual numbers remain close to it.  This may be caused by data issues, for example we know registering covid deaths can have a delay of a few days.  In Germany Merkel stated her estimate of R0 is 0.7 under lockdown, but every country will be different.

I will stick to my estimate of R0 under lockdown as 0.8 and now I want to make some projections based on relaxing lockdown (R0 rises) at different dates.  If we consider the R0 of the period when we were first trying social distancing (hand washing etc) to be 2 and given that it is currently 0.8, then it seems plausible that, upon some return to more normal behaviours, we can easily reach R0 of 1.3.  I will use this figure as post-lockdown R0.

Projection –  if UK ends the current lockdown on May 8th and R0 rises to 1.3 thereafter
Picture 1

Given the lag between infections and deaths, the initial 3 weeks after 8th May will look like things are under control – we ended lockdown and daily deaths keep falling!
A low of 164 daily deaths is reached on 25th May – but with the R0 at 1.3 (as discussed it could easily be higher) the deaths quickly rise again and breach the 1,000 level by 29th June.

What if we attempt another lockdown when death rate starts to rise?

Projection –  UK ends the current lockdown on May 3rd and then restarts in early June
Picture 2

Let’s say we relax lockdown for the month of May and restart it on 11th June, with R0 returning to 0.8.  We peak again at around 1,000 deaths per day, similar to early April.  This is a theoretical cycle that is sometimes mentioned in the press, we alternate 1 month of lockdown and 1 month in a slightly less severe lockdown. We would never get close to returning to the “hand washing” level of freedom and of course, we should not assume the NHS can keep functioning at this level of crisis indefinitely, given that most routine operations and scans have been put on hold.

What if we delay the end of lockdown until the start of June?

Projection – UK ends the current lockdown in early June
Picture 3

The low point of deaths and infections is pushed out and is much lower, with a low of 54 per day on June 19th.  However the power of compounding is relentless and we soar back into high infection rates by mid-summer.

What can we do?

This analysis is not very encouraging. I was hoping that R0 under lockdown would be far lower than we have seen, which would mean both that the rate of infections and deaths would fall more quickly, giving more headroom to relax the restrictions while still keeping the R0 close to 1.

This suggests that there is no easy way forward and that we are not on a path to progressively relaxing lockdown restrictions.  If a minor relaxation in lockdown leads to R0 of 1.3, then we merely have a short period until the pressure on health services increases and we return to more severe lockdown.

Are there reasons to be optimistic?

Yes.  This is not hopeless, but the key is to focus on R0.
One reason for optimism is my lack of access to accurate and detailed data.  Smart people, mainly in Germany, are doing high quality scientific work to understand the transmission of COVID-19.  If we better understand the transmission, then we will not need blunt tools such as lockdowns to reduce R0.  The rate of transmission, R0, is an average statistic; some areas will still have very high levels and so can be targeted accordingly.  I am sure we could find many current, restricted activities are less actually risky in terms of spreading and can be allowed to grow. Finding out what is driving the R0 to persist so high during lockdown is critical.

In my first post, I mentioned how sensitive the model is to small changes in R0, this is also reason for optimism.  If I run the model with R0 under lockdown at 0.5, rather than 0.8, and the post-lockdown R0 as 1.2 rather than 1.3 then the outcome is fantastically better.
Picture 4


What will actually happen?

From what we have seen so far, we are likely to see wildly different outcomes in different countries.  Singapore and China brought down R0 using testing and contact tracing that many in the West would oppose as an infringement of civil liberties.  Without almost total compliance, the lockdown methods are far less effective, and the UK and US are certainly making only minimal progress in that direction.  In Germany, led by a well-liked, credible, smart leader from a science background, we have seen early imposition of lockdown and investment in scientific research that gives me confidence they will find a balance between personal liberties, economic activity and public health.  The US appears to have the worst possible leadership and political structure which will make containing the R0 extremely difficult.  The UK government has had a very bad start relying on herd immunity, ignoring building testing capacity, refusing to work with the EU and failing to prepare the NHS for the oncoming storm.  But the failures of the government have been offset by the public spirit and compliance to adapt to this awful situation and by the extraordinary response of the NHS workers.  There is always the opportunity for the government to learn from others and adopt better policies and find a path though this crisis.


Modelling COVID

The most common method of modelling the spread of COVID is to consider estimates for how rapidly it is spreading (R0) and how deadly it is (mortality rate).

R0 is the number of new people infected by a given individual.  If R0 is 1 then the number of cases and deaths per day is stable, i.e. total deaths continue to rise but at a constant rate.  An R0 above 1 and the disease is accelerating.  I have found it hard to find a transparent source for this and have therefore built my own simple version which I found to work extremely well and be very instructive.


R0 I estimated R0 initially at 2.7 considering the UK case.
This was observed during the period before we applied measures to change the population’s behaviour.  The speed of the spread changes as we become more aware and change our behaviour.  We would expect the speed of spread of an airborne disease to reduce during various forms of lockdown.

For the pedants amongst you, I am aware that in the scientific literature they tend to say Re (effective) has fallen but this is just semantics and I will just say that R0 changes.

If you recall we went through a stage of around 2 weeks of hand-washing and cancelling football matches and some form of social distancing.   I estimate an R0 of 2.0 from the 3rd of March

On March 23rd we started official lockdown.  Here I use an R0 of 0.8, a big reduction resulting from a dramatic change to economic and social behaviours.

Mortality rate I used 1%

I assume that people are infectious for 5 days and death occurs 18 days after infection.  This produces the following results for what we have seen and gives a projection for the next few weeks.

Cumulative and daily deaths in UK – actual v modelcovid1covid2

We can also consider countries which are further advanced to see if the model works going forward.  Here we see Spain and Italy fit the daily deaths model very well, including the recent period when death rates are declining.

Daily deaths in UK, Spain, Italy (adjusted start date)- actual v model

I then applied the same model to look at the US.  The state level data is messy and dominated by NY and NJ.  To see what is happening outside of those, I totalled all the states excluding New York and New Jersey and compared to the same model as previous.

Daily deaths in USA (excluding New York/New Jersey) – actual v model
Until recently, the model works extremely well but worryingly the actual US daily death rate has continued to rise rather than fall.


How sensitive is the model to the assumptions?


The model is very sensitive to changes in R0.
For example, if R0 actually remained at 2.7 in the 2 weeks pre-lockdown rather than 2.0, then the peak death rate would be dramatically higher.


This helps appreciate how hard it is to give accurate forecasts as minor changes in assumptions can give hugely different results.  It further shows how scary this virus it as it could easily be far worse than we are currently experiencing. Given the problems in estimation, it helps explain why the NHS were so concerned that we would exceed capacity and how important it was that the measures we took at least slowed the acceleration of the spread.  The sensitivity of the model to R0 makes forecasting very difficult but conversely makes our confidence much higher on what it has been historically.  We know that R0 cannot have been much higher or lower than the model as otherwise the outcomes would be wildly different.

Mortality rate

The other key assumption is of course mortality rate. I was encouraged that the model is robust to changes in mortality rate, given that this parameter is most controversial.  For lower mortality rates, it would require a higher R0 for the initial period  to match the actual data we are seeing i.e. we have more cases but a lower mortality rate to reach the same number of deaths.   This means we can look separately at the evidence on mortality rate, but it does not materially change the projections for the next few months.covid6

What does this model suggest about the virus and our policy response?

The model is robust across the US, UK, Italy and Spain over different time periods without having to change parameters, apart from considering different start dates, which makes me quite confident in the approach.

Before we had any response to COVID, the R0 was around 2.7 which means that cases and deaths roughly double every 3 days.  The initial period of attempting to slow the spread by washing our hands, stop touching our faces, social distancing etc only seems to have reduced R0 to 2.0 which is still a rapid spread.  But this made enough difference that we did not run out of hospital beds, even if we did run out of PPE.

The number that I think is most important is the R0 ,post lockdown.  This can be seen by how quickly the death rates decline after the peak.  It is clear from the UK, Italy and Spain that the R0 is below 1 as the death rates are declining, and the peak deaths were around 3 weeks after the lockdown.  But I am very concerned that the death rates are coming down so slowly which suggests that the R0 may be 0.8 or perhaps even higher.  The US data is even worse as it implies that the average R0 in 49 of the states may still be over 1.

The reason this is so important is that this is the number that tells us how soon we can end lockdown and how far we can relax.  I will expand on this in my next post.


What have we learned about the parties from the election?

I think worth laying out a few ideas on what I have learned after this election.

Things that seem clear – but we probably knew already

  • May is a terrible leader
  • May is a terrible campaigner
  • The Conservative Party is deeply divided
    and may break out into open civil war at any moment

Things that seem new

  • The country is dominated by division on age lines not class lines
    I found this chart to be shockingly good as a model for voting behaviour.
    I am at exactly the crossover age, which perhaps explains why I dislike both so much!

  • Your income says nothing about who you will vote for
    The next chart is also stunning

Things that seem incorrect

· For Labour, Corbyn is an electoral asset whose positive campaign resonated with the electorate and he is poised to win next time.

Corbyn set expectations so low this feels like a landslide victory for him. But it was not.

1979 Callaghan 269 seats, he admitted defeat & resigned.

1992 Kinnock 271 seats, he admitted defeat & resigned.

2017 Corbyn 262 seats, claims victory and orders the winner to resign.

This was yet another vote AGAINST the elites.
Labour’s campaign was much better than the Tories but the idea of a Progressive Alliance doesn’t really exist. The Progressive Alliance is defined by its OPPOSITION to the Tories.
In that way, it reminds me of the Lib Dem party. A group of disparate protest votes united through negative cohesion as they all hate the same things. But once in power, they fall to pieces losing large portions of their supporters feel betrayed by almost any action they take.

However if the seemingly inevitable Tory civil war is bitter enough, maybe even Corbyn can win.

  • Hard Brexit is unpopular

A mandate for a Hard Brexit was the central theme of May’s campaign. But the Brexiteers have perhaps, done far too good a job of convincing the electorate that Brexit has already happened. The continual media suggestions that the economy is doing much better than predicted post the Brexit vote, helps to support the myth that Brexit has already happened.  If Brexit has already happened then it is hard to argue that it is still the most important issue.

If this is the case then it was not just the negative campaign against Corbyn that backfired, it was that the central “positive” element just didn’t make any sense and Labour’s focus on more traditional election issues such as Health and Education resonated far more strongly.

Hard Brexit is very unpopular with me and I think is the dominant issue we face. But I do not think my views are commonly shared by the electorate.

  • Opinion polls are worthless

I think the problem is that people do not understand what a poll is, and especially what uncertainties are inherent in them. This means that they get over-interpreted, and then the source is later blamed for not producing for which was impossible in the first place.

What I learned from the opinion polls during the election period was, not that the Tories had a big lead, but that the polls were massively volatile. Minor changes in methodology between polling companies seemed to be part of the outsized swings day to day – this is extremely useful information. The conclusion being that the uncertainty band around the poll should be seen to be extremely high, and this result fell easily within my uncertainty band.

Fiscal transfers within a country

The ONS has calculated an estimate of the fiscal transfers within the UK for the first time. It shows that London and the South East generate more tax revenue than they receive in government spending, whilst other regions are significant net beneficiaries.

What I find important is not the numbers themselves which are entirely unsurprising, it is the fact they were calculated and published at all. Fiscal transfers within a nation state are a key feature of a stable society. If people begin to associate themselves with a region, rather than the overall country, then the cohesion of the country is called into question.

The move towards devolution of power within the UK has been growing, with mayors, local powers and of course the devolved administrations such as the Scottish parliament. The evidence so far is that granting more powers to the regions of the UK will not satisfy the desire for autonomy, In fact, it reinforces the sense of a separate identity. Publication of figures like those above works to further emphasise our differences.

Comparison to the US and the EU

To get a sense for how important fiscal transfers are to the cohesion of a state, this comparison between the US and the EU is striking. Each blue dot is a EU country, each red dot is a US state. I have used the same scale in both charts to make the differences in the dispersion clear.

Y axis Relative income per head of the population (100 is the average for each group)
Shows much greater inequality between EU countries than between US states.

X axis Net fiscal transfer between states (% of GDP)
Shows fiscal transfers between EU states are tiny compared with those between US states.

The US data is characteristic of a country i.e. large fiscal transfers are necessary within a single currency zone, given any lack of flexibility in monetary policy to deal with cyclical or structural differences. The low level of EU transfers helps show why the Eurozone is having such difficulties. One way forward for the Eurozone is to become more integrated, developing into a proper federal state, including large permanent fiscal transfers.

Perhaps the most remarkable difference between the EU and the US, is the level of attention these figures receive. In the US, this issue gets virtually no attention. If I ask Americans, they have generally no idea what the numbers might be and have never given it any thought. They pay federal taxes but do worry about the geographical split of Federal spending. On the other hand, in the EU it is a massive political issue, despite the numbers being an order of magnitude smaller. The EU budget is only 2% of GDP but generates vastly more hostility than national budgets of 20 times the size. This is a major political hurdle for the Eurozone to deal with, perhaps having the most hostile country leave the EU will help them.

My concern is that the UK is moving away from a “US level” of large fiscal transfers without political awareness or opposition. If the United Kingdom cannot regain its sense of being “United”, then the road leads to full separation.