Was the Second Lockdown Justified?
By: Ben Pitter-Fagan
Tags: Lockdown, UK, Data Science, Pandemic, Covid 19

With the UK officially coming out of the second national lockdown on 2nd December and vaccination announcements on top of that, it seemed as if pressures may have been slightly relieved as the nation awaits the next steps for moving forward with life. We’ve exited the lockdown, but have entered a nationwide ‘tiered system’ (with London being under the strictest tier), which is confusing as ever and a separate set of Christmas rules that adds to the public’s confusion.

A month-long lockdown was announced on 31st October 2020, to be from 4th November to 2nd December. Similarly, to the first lockdown with all non-essential businesses/public buildings being closed (apart from schools, colleges and universities), the original intended purpose of this was to flatten the curve of cases, in order to reduce the strain on the NHS. In this report, I will be:

1. assessing how effective the second lockdown has been in achieving this

2. challenging the data chosen to make these policies and

3. analysing the wider socio-economic impacts of further lockdowns.

Throughout this, I’ll be referencing various sources of information as well as utilising my own graphs that I have created (on Power Bi and Python). The following sub-headings will address the reasons why this second lockdown may have not been warranted.

Disclaimer: I am not a medical professional giving any advice on how to respond to the pandemic. I am only analysing the current situation from statistical and social aspects.

1. Over-projection of data models

Research has shown that the model projections used by the government to estimate death figures were four times too high and also outdated. Scientists from Cambridge University (MRC Biostatistics Unit), presented these graphs to government on the day of the announcing the second lockdown, predicting a daily death count of 1000 per day up to that point, when it was in fact 162 for England. The model used was made three weeks prior on October 9th, despite the fact that the Cambridge statistics unit had more updated projections with much lower figures and a more optimistic projection. The updated model made 3 days before the Downing Street conference on 28/10/20, actually forecasted 240 daily deaths by the following week with 500 for later in November. It made no prediction for December as that would be too inaccurate.

FIGURE 1: Death scenario data used by the government was out of date| Source: The Telegraph

According to former Brexit secretary David Davis, “The first responsibility of the scientific advisers to the Government is to give the truth to the public and not to cherry-pick the data. This is a fairly major error on their part if they’ve used old data which effectively misleads the public.”

However, this is not the first instance of over-projected data figures being used by the government. In March, Professor Neil Ferguson of Imperial College London predicted casualty figures to be 10 times greater than what they actually were.

FIGURE 2: SAGE Models used by various institutions, base their estimates on extracted underlying data from software called ‘Grab It’, rather than raw data. Estimates by Warwick University and LSHTM were closer to the real figures| Source: BBC

2. No Opposition in Parliament

The decision to approve a second national lockdown was won via a Commons vote of 516 to 38. This shows that opposition to lockdown strategy is minimal. Despite this decision, there was a backbench rebellion on the day against the PM, led by Theresa May (now MP for Maidenhead) against the current COVID strategy. She argued that the SAGE prediction model of 4000 deaths per day used by the Government to justify the second lockdown was wrong (in reference to the outdated model by Cambridge University, mentioned previously). Within this speech (that Boris Johnson walked out of), she also mentioned the lack of data on wider socio-economic costs such as domestic abuse, mental health deterioration and business costs amongst other effects.

Amongst the recent Tory opposition to extensive lockdown measures is Sir Graham Brady, chairman of the 1922 Committee backbench of Tory MPs. They formed the ‘COVID Recovery Group’ to challenge scientific advice, break the cycle continuous restrictions and help the government to find a sustainable way to live with the virus until an effective cure is found.

Nigel Farage rebranded his Brexit Party as “Reform UK” for a similar purpose as the COVID Recovery Group. This could definitely be viewed as an opportunity for him to exploit another issue as Brexit draws to a close, but despite any personal agendas or political affiliations, more MPs from different parties need to step to the forefront, speak up and actually work together on their ideas that overlap when it comes to alternative approaches to lockdowns.

3. The North-South Divide

A rise in cases is the key component that dictates policy regarding how to handle Coronavirus; not just in the UK but globally. Going by this metric, I downloaded and analysed case data from GOV.UK, for all 9 regions across England. Using that data, I produced a line graph of cases per 100,000 people, omitting the middle regions West Midlands, East Midlands and East England in the graph below, in order to show the clear disparity in the number of cases between the south and up north.

FIGURE 3: COVID Cases per 100,000 people across England | produced by me on PowerBi | Data source: GOV.UK

I’ve highlighted the figures on 31st October 2020, the day of the second lockdown announcement, just to add more perspective to this. If this isn’t enough, then I took this data further by creating a saturation map out of it, but this time I’ve included all of the regions in England. The varying intensity of colour correlates with the number of cases per 100,000 people.

The visual representation in FIGURE 4, is an even more thorough display of the disparity between the regions, with the highest number of cases being up north and the lowest in East England and all of the southern regions. The East and West Midlands also falls in the middle in terms of numbers as well as geographical position, along with the North East. Could a tiered system have been implemented in the first place, to avoid the economic devastation of another lockdown? With the government still insisting on a national lockdown, having regional lockdowns instead of would be a good call. I couldn’t tell you what the cause of this major difference in numbers is, but just by analysing public data and presenting it in a palatable way, it is clear to see that some regions need more help than others, so why could the government not see this?

FIGURE 4: Saturation map of COVID Cases per 100,000 people across England | produced by me on PowerBi | Data Sources: GOV.UK, ONS

4. Cost-Benefit Analysis

A major argument against continuous lockdowns has been the wider socio-economic damage that has been caused, as a massive side-effect. The first lockdown was understandable, given the ambiguity regarding the virus and how to even deal with it, but it seems that now we are only presented with two options; to either protect the vulnerable or to rebuild the economy (but never both), not that we have the choice anyway.

Top UK Economist David Miles, who is a former member of the Bank of England’s MPC (deciding on the country’s interest rates), has been a consistent voice of expertise on quantifying the costs of the lockdowns. He has implemented a cost-benefit analysis method used by the NHS called QALY (Quality Adjusted Life Years) to the current pandemic. QALY is a system used by the NHS to assess if a treatment is cost-effective enough. If the treatment costs more than £30,000 and doesn’t increase the patient’s life by over 1 year, then it’s dismissed. In other words, it compares the costs of lives saved during restrictions against the costs of locking down. So, for the life of one person saved, the cost would be £30,000 x 5=£150,000 (assuming they gained an extra five years of life). On top of that, a cost of £20,000 is also factored in as a consideration for hospital resources saved. This methodology seems very rigid and unethical to quantify lives financially, but this is how decisions are made on treatments in the healthcare system.

FIGURE 5: Cost-Benefit Analysis table for the first lockdown, assuming 5 QALY (Quality Adjusted Life Years), converted to an index of billions (£). 5 QALY is also assumed lost for each COVID-19 death. Along the x-axis are different GDP loss scenarios, taken as a proportion of the 2019 UK GDP of £2.2 trillion. Along the y-axis are different possibilities for lives saved, due to imposing restrictions| Source: Stay at Home, Protect the National Health Service, Save Lives: Cost Benefit analysis of the lockdown in the United Kingdom by David Miles et al.

The estimations for number of lives saved is made by subtracting excess deaths from projected deaths (assuming no change in behaviour), based on an Imperial University study. The maximum figure of 440,000 net lives saved, is definitely an overestimation, just based on the factors used to calculate the number of lives saved. The arbitrary figure of 5 extra years being gained for every life saved is an assumption; plus, the study can’t account for other deaths incurred as a result of the pandemic. So, these are related deaths, due to the strain on the economic system, such as increased suicides, missed A & E appointments or cancer deaths due to receiving no treatment. For each life not lost, it’s assumed that they were all already at risk and had underlying health issues. Not all of them may have necessarily needed to be in ICU, hence been a strain on hospital resources.

I’ve touched on some of the negative impacts already, with the GDP drop in the economy being the most obvious one (with banks forecasting a double dip recession). Job prospects have been depleted for the most part, job losses, rising debts and independent business owners being on the knife edge (if they haven’t gone under already). Young people’s education has been disrupted and there has been other undiagnosed illnesses, such as cancer. These costs in particular are hard to quantify now, but when this period is over, more will come to light. Even years from now, more of the effects will become apparent.

The drastic reduction in care for cancer patients is a noticeable impact on the healthcare system. There is a huge backlog of patients, plus people have stayed away from hospitals in general due to the “Stay at Home” message; so many more deaths may be going unaccounted for as many are dying at home due to misdiagnosis and even loneliness. This alone could be creating another pandemic, which is going unreported.

FIGURE 6: Waiting times for NHS cancer treatment has worsened| Source: FT — Nov 6th

5. Cases do not equal deaths

The figures used to stir up a response to COVID-19 are the number of cases; whether this is in the media or in Parliament where these figures are used to justify policies surrounding it. Under the first heading ‘Over-projection of data models’, I’ve already spoken of the false case figures that were used to warrant a second lockdown, but what I’m doing now is looking at the actual numbers for cases and deaths throughout the year.

FIGURE 7: Covid cases compared to deaths in the UK, from the start of the year up to the 2nd December (end of 2nd lockdown) | produced by me on Python | Data Source: ECDC

Just by looking at FIGURE 7, it can be seen that the rise, peak and fall in the number of cases from mid-March to June, correlates with the number of deaths. From September, there was another rise in cases, but even then, the death toll remained relatively low and didn’t follow suit, but why is this? From mid-June to mid-October the death toll has been relatively low (skewing above the zero line); with some fluctuations in the line throughout November. The highest death toll to date was on 22nd April at 1,224 in one day. This was in the period of the first lockdown when there was a lot more uncertainty, but since then the toll has reached nowhere near that figure. What has been noticeably more newsworthy since then is the rise in the number of rapid cases, but could this just be due to more testing being available to the public? Before I delve into that, I’ll look at the moving averages for the tolls of cases and deaths.

FIGURE 8: Covid cases and deaths in the UK — with a moving average (up to the 2nd December) | produced by me on Python | Data Source: ECDC

In FIGURE 8, I have added a 7-day moving average line to the cases and deaths to highlight the trends. Any point on a 7-day average line takes an average of the sum of cases/deaths from the last 7 days. To me, the death toll is more indicative of how severe the virus actually is. Not to belittle the significance of cases, but it’s not possible to make a direct comparison between the number of cases between March and July, compared to now as more testing is available. On top of that, Autumn (September to November) is the season when many people pick up the flu; plus, many symptomatic people are being tested so their results would be more likely to return as positive. A point worth noting about the death toll is that people categorised as passing from COVID-19 are “people who had had a positive test result for COVID-19 and died within 28 days of the first positive test”, according to GOV.UK. With that being said, there is a possibility for people who tested positive; if they passed away within those 28 days from another cause such as cancer or even a road accident, then their death cause could still be classed COVID-19. This may not be the case, but a more stringent definition of a COVID-19 death would need to be set, in order to prevent or mitigate this possibility.

FIGURE 9: Area Chart of UK Covid cases in relation to the introductions of different testing systems| produced by me on PowerBi | Data Source: GOV.UK

FIGURE 9 shows the correlation between the rise in the number of COVID-19 tests and a rise in the number of cases. Virus tests are the swab and saliva sample tests that have been rolled out on mass to the public. Another name for this form of testing is PCR (Polymerase Chain Reaction) testing, which detects the presence of the virus’s genetic material in the swab sample, called RNA (Ribonucleic acid); with a result being returned from the lab within 12 to 24 hours. The data for the virus and PCR tests are the same in quantity, so I’ve only included one of them on my graph. Antibody tests take blood samples to see if you had an infection of the virus before. Those tests are only available to NHS and care staff.

Between 18th August and 5th October, virus tests rose by 71.2% and cases rose by over 3000%. Between the 5th October and 22nd October, virus tests rose by another 24% and cases rose again by 16.2%. For the third spike in cases up to 13th November, virus tests rose by 22.4%, with cases rising by 25.4%. As Antibody tests are only available to medical staff, they don’t have as much effect on the overall rise in testing. Cumulatively from 18th August to 13th November, the number of virus tests and cases increased by 117.6% and 3042% respectively. Surely, there would be other factors that contributed to the massive spike in cases, but the continuous increase in virus testing being one of them is not far-fetched.

FIGURE 10: UK Covid cases compared to deaths, but including markers on the dates were there were significant increases in cases. Also marked are the number of virus tests and deaths on those days. The death numbers fluctuated, so there aren’t any notable spikes to point out | produced by me on PowerBi |Data Source: GOV.UK

This trend is shown more clearly in FIGURE 10, where I have marked the same checkpoints on a different graph, to give an even clearer picture of the connection between the virus tests and cases. This is why it is difficult to compare the cases in Autumn to those between March and May; as the same level of testing was not available. The death numbers fluctuated, so there aren’t any notable spikes to point out.

6. Hospitalisations, mortalities and excess mortalities

As stated continuously in this report, is that throughout the pandemic, case numbers are the main statistics that have been used to decide on national policy in response to the virus. Apart from figures on the cumulative deaths due to COVID-19 or daily additions to that toll, cases are the primary data being sold to us by the Government and the media. Another way of assessing the severity of the virus and formulating a response, is through a method called “Focused Protection”, suggested by Dr. Martin Kulldorff, Professor at Harvard Medical School. This method involves focusing on three factors: hospitalisations, mortalities and excess mortalities. I have had a go at reproducing these statistics myself, starting off by comparing the hospitalisations to deaths; due to Covid.

FIGURE 11: UK Covid deaths compared to Hospitalisations| produced by me on PowerBi | Data Source: GOV.UK

Hospitalisations and deaths provide a clearer picture on the actual severity of the virus as opposed to just focusing on cases; plus, there is the added complication of PCR tests producing false positives. Regarding the deaths, there is no clear trend, but it can be noted that when the death line fluctuates (end of March-June and late October to early December), is also when the hospital cases are at their highest. This is very notable in April, when the death toll was the highest, coinciding with record hospital cases. The trend in hospital cases does resemble that of a rollercoaster, reaching a summit, dropping then going up again.

FIGURE 12: UK Covid deaths compared to Hospitalisations during the 2nd lockdown | produced by me on PowerBi | Data Source: GOV.UK

In FIGURE 12, I’ve zoomed into the period of the second lockdown; noting when the hospital cases and deaths reached a peak for that time period. Deaths did fluctuate during this period and as noted before, that is when hospitalisations generally are at their highest. However, there was no specific correlation with hospital cases and deaths because as noted on 23rd November, when hospital cases reached a peak, death numbers were at their lowest. There was no clear sign of improvement in deaths as the numbers kept going up and down, but the hospitalisation numbers started a downward trend after reaching its summit.



It would also be interesting to know what proportion of deaths take place in the hospital and the percentage of hospital patients with existing underlying health issues. This would help in formulating a more targeted approach to protection.

Now, moving onto excess mortalities, this is a term used in epidemiology to represents the number of deaths from all causes above/below the amount anticipated under ‘normal circumstances. In this case, the assessment would be of the total deaths from all causes this year in comparison to the average number of deaths in the same period over the past few years. This is a way of attempting to analyse the wider collateral damage, caused as a result of the pandemic. I’ve touched on many of the effects in previous sections, such as; increased deaths from: suicides, drug abuse, loneliness, misdiagnosed treatments and so on. As those specific numbers won’t be completely collated until a lot later down the line, we’re still unable to pinpoint these direct side effects to the death toll at this point; but we can look at the comparison in total numbers.

A method to calculate excess deaths and display it graphically, is by working out a statistic called the ‘P-Score’. The P-Score represents excess mortalities as the percentage difference between the weekly deaths in 2020 and the average number of weekly deaths in the same week for the past five years. A P-Score of 100% would mean that the number of deaths this year are 100% more (or double) the average deaths for that period over the past five years.

Equation 1: P-Score Formula

I did this calculation for the whole UK, by collecting data on the weekly deaths for: England and Wales, Scotland and Northern Ireland from the ONS (Office of National Statistics), NRS (National Records of Scotland) and NISRA (Northern Ireland Statistics and Research Agency) respectively. I sieved through the historical data to obtain the weekly deaths between 2015 and 2019 to work out an average.

FIGURE 13: UK’s P-Score | produced by me on PowerBi | Data Sources: ONS, NRS, NISRA

In the middle 2 weeks of April is when the P-Score is at its highest, coinciding with the high COVID-19 death toll during that period. Since the first lockdown, the P-score tapered off, but rose again in October, with the score remaining around 20% throughout November. What is evident is that excess deaths are nowhere near what they were in Spring, but with the level that it reached in November, it would be interesting to find out what the other causes of death were during that period. I believe that the P-Score provides a baseline to examine different periods and what the causes of death were back then.

With that being said, the P-Score calculation still does have its flaws. The five-year average does not account for changes in mortality trends, mortality rates amongst different age groups and population size. What I have done now is combined the data for hospitalisations, mortalities due to Covid and excess mortalities into one graph and here is the result:

FIGURE 14: UK’s Hospitalisations, Mortalities & Excess Mortalities. Data for the daily hospital cases and Covid deaths were accumulated into weekly figures, to match the weeks of the P-Score | produced by me on PowerBi | Data Sources: ONS, NRS, NISRA, GOV.UK

By focusing on the factors that give an alternative view of the severity of the virus; this is what it looks like. I’ve marked the key points on FIGURE 14, with the High P-Score around April correlating with the highest weekly hospital cases and deaths. From October to November when there was another spike in hospitalisations, the P-Score is not as high as it was during the first peak. During the first peak, the high P-Score correlated with the high hospitalisations, so the main cause of the excess deaths was clear to see. However, with the P-Score for November wavering around 20%; this shows that excess deaths are still approximately 20% above normal levels; so, this identifies a period where related deaths as a result of the overall conditions due to the pandemic would need to be looked into. By assessing the deaths weekly as opposed to daily, it can be seen that there is a steady increase in weekly Covid deaths from the first week of October to the end of November. This does give more clarity on the severity of the disease, rather than just looking at cases solely.

Conclusion

Regarding data models that the Government use in order to decide on lockdown measures (or restrictions in general), it was noted that outdated SAGE models were used that amplified the number of cases and predicted deaths. There is a health crisis and nobody is denying that, but it does create mistrust when false numbers are knowingly being used to justify the implementation of draconian measures. When announcements are made, the majority of the public would be non the wiser whether actual figures or flawed predictions are being used to bring in restrictions.

There needs to be more scrutiny of decisions, which leads onto the second section regarding the lack of opposition in Parliament. More voices need to be heard, offering an alternate approach to complete shutdowns (which should only be the last resort).

This week it was announced that London and other regions in the South would go into Tier 3 due to a spike in infections. However, leading up to the second lockdown, I noted and displayed visually that cases up North far surpassed that of the South. A more targeted approach should have been employed back then.

Regarding cost-benefit analysis; the costs to the economy are evident but that is a reason why there should be more considerations before resorting to such measures. The hospitality sector in particular is being continuously crushed (even under the tier restrictions) and small to medium businesses suffer the most due to this. With the distancing and sanitizing precautions, could there be a way for those businesses to still operate? A more effective method would be to find out the percentage of cases that arise as a result of those organisations being open. Using gyms as an example, according to a study by Sheffield Hallam University, after analysing 62 million gym facilities across Europe between September and October; they found that the average infection rate per 100,000 visits was 0.78. Gyms are vital to physical and mental wellbeing, especially at a time like this, so more studies such as that one need to be used to inform decisions.

Cases have been used as the main reporting tool during the pandemic, which does stir up a lot of panic and fear. However, with the range of people being tested, increased testing and the flaws within it (i.e. false positives or symptomatic people showing as positive); it does not provide a complete picture. Death counts give a better reflection of the severity of the virus.

To build on the death counts, is hospital cases and excess mortalities. Hospital cases give an indication of the seriousness of the Covid cases and excess mortalities provide a barometer to compare the number of deaths this year to the average deaths over the past five years. The purpose of analysing the excess mortalities is to gain an idea of the extra deaths due to Covid, as well as other causes as a result of the current social conditions. It is still difficult to pinpoint the specific causes of other deaths related to the pandemic, but the widespread collateral damage will become more apparent over time.

This report used data covering the whole UK (apart from section The North-South Divide), so case and death figures would be higher overall. Decisions of the four nations that make up the UK are made independently, so a separate study of just England would give an even more accurate picture. Even then, with what has been produced by using public data; has provided a different view on statistics regarding coronavirus that you wouldn’t necessarily see elsewhere. Cases have been made for why more factors need to be considered before implementing more lockdowns and the need for more transparency with the statistics that the media reports. Lockdowns have become the norm now, so when announcements are made, a lot less people question it. The wider social damage is still unquantifiable as of yet, but if extreme measures are to be implemented that affects the lives of normal people, then more questions asked of the government as to why that is the only option and at least debating the alternatives before complying.

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