Sweden’s total deaths per million in population as of July 14 is 549. That’s considerably lower than the deaths per million rate in the UK, which is 662, and in Spain, which is 608. In Belgium, the death rate is 884.
Sweden deaths per million is many times better than the rates found in New Jersey and New York: 1,763 and 1,669.
Articles condemning Sweden’s “failure” rarely if ever mention these comparisons.
Nonlockdown Sweden has a death rate similar to harsh-lockdown France can only be explained by claiming France didn’t lock down harshly enough or long enough.
Two weeks after the WHO’s prediction that Sweden will have a resurgence in COVID-19, both cases and deaths in Sweden continue to trend downward.
Thanks to Sweden we know what both lockdown and nonlockdown countries look like: they look remarkably similar in some cases.
After all, after failing to implement a lockdown for months, Sweden is still nowhere near matching the death rates reported in New York.
In reality many of the people who died from Covid-19 were likely to die this year anyway, so in one respect this estimate is likely to be too high. In another respect it’s likely to be too low, as it will not include ‘lockdown deaths’, that is, the deaths from delayed cancer and heart treatments, and so on, but as I was interested in the effect of Covid-19 I didn’t want those in my graph anyway. (Another complication is that not everyone who is classed as a Covid-19 death actually died from it, but I decided to ignore this.)
The five year average for 2015-19 is 531,355 deaths per year. As of writing this there were 42,462 Covid-19 deaths in the UK. There are likely to be a few more deaths in the next few weeks, but not many more, as the disease is (barring an unlikely second wave in winter), on its way out. Besides, the number we are adding on here is for the whole of the UK, not just England and Wales, so if anything this number is inflated. That gives us 573,817 deaths for 2020. Then I got hold of the historical population figures for England and Wales, and calculated the death rates per 1000 from it, so that population increases are taken account of. Here is the result:
Novelist Hector Drummond decided to look at the annual death figures for England and Wales from the Office for National Statistics. This is what he found after graphing the numbers all the way back to the turn of the twentieth century.
The 2020 death figures on the right cannot even be considered a spike over the course of the century.
@gummibear737, a Twitter use who has been analysing COVID-19 data, has published a chart that confirms a hypothesis by Dr Michael Levitt:
Stockholm is the best population to test Covid theory whereby it was hit hard early and did not have lockdowns. Nobel Prize winner Dr Michael Levitt postulated that the virus burns out when it has infected 15-20% of the population. According to this, he’s right.
So what does this mean? Lockdowns were a waste of time and resources. Minimizing deaths just delays the inevitable. Those countries which were not hit are most likely to see continued spikes and outbreaks. Maybe less during the summer but a second wave later this year.
Stockholm is the best population to test Covid theory whereby it was hit hard early and did not have lockdowns.
Nobel Prize winner Dr Michael Levitt postulated that the virus burns out when it has infected 15-20% of the population.
The difference between what the government was telling us and what their information was telling us was so extreme and outrageous.
Exponential means a “constant rate of growth.” The government data in March was clearly showing that the COVID-19 was declining, not growing exponentially. This was the same in all countries you could see the data. [See chart 1]
A constantly declining growth rate will make a bell curve. The government were standing in front of bell curve graphs during their briefings yet they were telling us we were in the middle of the epidemic.
It was very clear that we were heading to a peak sometime around early to mid-April.
You don’t have to be complicated mathematics to see that COVID-19 was running out of steam almost from day one.
The conclusion from the Centre for Evidence-Based Medicine seems to be that it’s impossible to predict if there will be a second wave.
Sweden’s epidemic looks identical to the UK’s but they did not lockdown. Their datapoint indicates there won’t be a second wave. There has been no spike in Denmark either. [See chart 2]
Unknowns: has summer affected COVID-19 and will there be a mutation?
Will illnesses during the autumn and winter be mis-attributed to COVID-19? Poor media coverage means that we can’t be sure.
Symptoms of COVID-19 are very similar to the flu. Something could look like a second wave but will we really know?
The lockdown is costing a Brexit bill a week.
The government response seems to have been skewed by Neil Ferguson’s modelling data. The make-up of government advisors seems to be a recipe for groupthink, which is very dangerous.
Epidemiology (the way a disease spreads through the population) is not complicated science. The government could have had lots of people who were very good at this but they didn’t.
We should have cocooned the vulnerable, make sure the NHS has capacity and “let it rip” through the population.
We should never have had an open-ended lockdown.
The ‘R number’ is just the difference of in the number of people infected after each generation of a disease. Britain crossed the ‘magical R of 1’ line a few days before lockdown and the same day as Sweden. Whatever interventions have been done doesn’t seem to have had any effect. [See chart 3]
COVID-19 is mostly a care home and hospital disease. This was obvious very early on. Old people should not have been moved from hospitals into care homes. It seems as if we knowingly seeded the most vulnerable environment with the disease.
37% of our deaths are care home residents but they are only 0.5% of our population. Of them are dementia sufferers.
Over 20% of the infections were picked up in the hospitals. COVID-19 seems more like MRSA than influenza in that it’s an infection control problem.
COVID-19 is much more comparable to flu for the rest of the population.
1968 flu killed 80,000 people in the UK.
This last winter was a low flu winter. It’s quite possible that the people who died of COVID-19 are those who didn’t die.
If you overlay COVID-19 deaths with the 2000 flu season, they look very similar. [See chart 4]
95% of deaths have had another serious disease. Most people have almost no chance of dying from COVID-19.
If you are under 40, you have more chance of being struck by lightning that dying of COVID-19.
If you are under 60, you have more chance of drowning.
At any age, you have more chance of dying on the roads than dying of COVID-19.
Lead indicators of 111 and 999 calls with COVID-19 symptoms show there was no spike after VE Day celebrations or BLM protests. In fact, it was even coming down at lockdown. That lockdown was big change for COVID-19 is invisible in the data. [See chart 5]
Charts
Chart 1: COVID-19 was declining in Europe as of march. It was not growing exponentially
Chart 2: Sweden’s epidemic looks similar to the UK’s but they did not lock down.
Sweden has had fewer covid deaths per capita than Belgium, Spain, Italy or the UK, and its children <16 have missed no school. @AlistairHaimes, 17 June 2020
Chart 3: Britain crossed the ‘magical R of 1’ line a few days before lockdown
UK Rt (“R number”), late Feb to early April. Lockdown did the square root of nothing. Hand-washing advice early March does look to have caused a massive drop in R, as you’d expect. @AlistairHaimes, 5 May 2020
Chart 4: COVID-19 deaths overlayed with the 2000 flu season
If covid deaths had happened in winter rather than spring. Shown against two recent moderately bad influenza years, for comparison. @AlistairHaimes, 23 June 2020
If social distancing made things better, we would expect a positive correlation on both of these graphs – in other words, earlier social distancing would lead to both earlier flattening of the curve and lower total deaths, meaning these points would all sit close to a diagonal line sloping up from left to right. Instead what we see is very little correlation at all, and what there is is negative. So early social distancing is either doing nothing or making things worse. This is likely because the virus spreads mainly in hospitals, care homes and private homes rather than in the community, so social distancing of the wider population beyond a basic minimum (washing hands, self-isolating when ill, not getting too close, and so on) has little impact.
There was no exponential growth in Covid-19 infections the UK. From the first days of the outbreak growth rates were in decline.
The following chart produced by financial strategist Alistair Haimes should put the above question to rest (compare it with the above chart).
The left hand side starts in March 2020 when the UK had had its first 300 infections and then stops at 10 April when Europe as a whole had reached a growth rate of zero or less. The chart is analogous to the above chart of interest rates. If you cannot distinguish the different colours and European countries don’t worry too much (UK is dark blue) as they all show the same overall pattern. The trends are all downwards, from start to finish.
[I]f you believe herd immunity is only reached at 60 per cent, you should be terrified at any loosening of lockdown. If you don’t, then you must reconcile antibody testing that says 80 per cent are still susceptible with the difficulty the virus seems to encounter in marching very far past 20 percent of the population.
That is the reconciliation my hypothesis achieves. I propose that there may exist forms of human resistance to this virus that don’t show up in Covid-19 antibody tests.
[I]n focusing on that Covid antibody test alone as indicating a pass-or-fail immunity, we could be overlooking important ways in which humans may be endowed with, or acquire, other kinds of resistance.
This is my first podcast and they will improve. This is Part 1 and it describes how COVID may grow exponentially and how we flatten the growth curve.
Part 2: Curve Fitting for Understanding
This is Part 2. Fitting viral growth data with simple mathematical functions can give important insights into how epidemics will grow. Here we illustrate two commonly used growth curves, the Sigmoid Function and the Gompertz Function. While superficially similar, they are really very different.
Part 3: COVID19 Never Grows Exponentially
Part 3. The total case numbers in South Korea and New Zealand have exponential growth rates that decrease linearly on a log-scale. This is not ever exponential growth.
The number of people claiming unemployment benefit in the UK soared to 2.1 million in April, the first full month of the coronavirus lockdown.
But the labour market is set to worsen, according to politicians and analysts, with Therese Coffey, Secretary of State for Work and Pensions, telling the BBC on Tuesday that the unemployment rate was likely “to increase significantly”.
About 8,000 more people have died in their own homes since the start of the coronavirus pandemic than in normal times, a Guardian analysis has found, as concerns grow over the number avoiding going to hospital.
Of that total, 80% died of conditions unrelated to Covid-19, according to their death certificates. Doctors’ leaders have warned that fears and deprioritisation of non-coronavirus patients are taking a deadly toll.
“There are really only two particularly unusual things about the Covid-19 epidemic: the timing of its arrival and the lockdown some countries declared.”
Deaths per day, as is well-reported, peaked around Easter; and because deaths lag infections by something around three weeks, this implies that infections peaked sometime in mid-March. If you add up all the bars in the chart and fill in the blank area of deaths still to come, we are looking at a killer that, in scale, is bad-but-nothing-special compared to killers of previous years. Panning out: as a killer worldwide, it looks as though Covid is going to take a toll perhaps 1% of 1918’s Spanish Flu.
…the dark blue line is 2019-20, with Covid-19; the turquoise and red lines are the bad flu years of 1998-99 and 1999-2000.
…Covid-19 is narrowly in third place as a killer to remember, behind the 1998-99 and 1999-2000 influenzas (2017-18’s ‘Beast from the East’, the green line, doesn’t place), a point also made by American statistician William Briggs.
The spread of COVID-19 is not going to follow an exponential curve – and grave errors will follow if analysts believe it will. The number of new cases rises rapidly, peaks, and then declines. It’s called the epidemiological curve. It’s not a theory or hypothesis; it plays out that way every flu season. It is how it has played out in China and Korea for COVID-19. Flattening the peak to avoid overloading the healthcare system is the main medical goal of the seemingly extreme containment policies we have seen to date.
Tony Heller compares COVID-19 with other pandemics and explains why the lockdown may create an even more devastating second wave.
Medical professionals say there never was a surge, hospital activity is at a low and we’re in danger of losing our capacity to deal with the second wave because we panicked.
How many tests performed for #SARSCoV2 are positive? Visualization of the data of the US, Switzerland, Germany and France. (Update, 13.4.2020)#COVID19 Data sources: BAG (Switzerland), RKI (GER), COVID Tracking Project (US), Santé publique France (France) pic.twitter.com/cCs0nnjktG
Andrew Mather, a mathematician and financier based in the UK, explains how the official data clearly showed that the COVID-19 crisis was over in the UK before the lock-down.
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