A very interesting article was recently published in Lancet that sought to understand which factors correlate, on a country level, with covid related outcomes. The study was observational, so it can only show correlation, not causation, but it can still give pretty strong hints as to which factors protect people from covid, and which factors increase the risk of being harmed.
The most interesting thing about the study, from my perspective, was that it sought to understand what effect lockdowns, border closures, and widespread testing have in terms of decreasing the number of covid deaths. Although correlation does not automatically imply causation, if there is a lack of correlation, then that strongly suggests a lack of causation, or at least, that any causative relationship that does exist is extremely weak. And considering the amount of money, effort, and resources that have been poured in to lockdowns this year, and that continue to be poured in to them right now, it would be pretty disappointing if lockdowns had such a minimal effect that there was no noticeable impact on mortality whatsoever. Am I right?
But I get ahead of myself. The study chose to limit itself to looking at the 50 countries with the most recorded cases of covid-19 as of the 1st of April 2020. My interpretation is that they chose the top 50 most affected countries, rather than looking at all 195 countries, due to resource constraints. Data was gathered up to the 1st of May 2020. All information gathered was in the form of publicly available facts and figures. Data gathered included information about covid, income level, gross domestic product, income disparity, longevity, BMI (Body Mass Index), smoking, population density, and a bunch of other things that the researchers thought might be interesting to look at. The authors received no outside funding and reported no conflicts of interest.
There are a few problems here that become apparent straight away. First of all, as mentioned, all the data in this study is observational, so no conclusions can be drawn about cause and effect.
Second, May was relatively early in the pandemic, and it’s now November, so we’re missing about half a year’s worth of covid data. On the other hand, the pandemic had already peaked in much of the world by May 1st, and lockdown measures had at that point been in place for months in most countries, so it should be possible to get a pretty good idea about what effect lockdown has in terms of decreasing covid deaths, even using only the data available up to May 1st.
Third, the analysis builds on publicly available data, often provided by different governments themselves, with widely varying levels of trustworthiness, and with different ways of classifying things. As an example, data from Sweden is infinitely more reliable than data from China. And while certain countries have used quite inclusive criteria when deciding whether someone has died of covid or not, other countries have been much more strict. The countries with stricter definitions will tend to have lower covid death rates than the countries with more generous definitions. This lack of homogeneity in how things are defined can make it harder to see real patterns.
Fourth, the reseachers who put this study together gathered an enormous amount of data, pretty much everything they could think of under the sun that might in some way correlate with covid statistics. That means that this study amounts to “data trawling”, in other words, going through every relationship imaginable without any a priori hypothesis in order to see which relationships end up being statistically significant. When you do this, you’re supposed to set stricter limits than you normally would for what you consider to be statistically significant results. They didn’t do this. We’re going to discuss this problem in more detail later in the article.
Before we get in to the results, I’ll just mention one more thing. The results are presented as relative risks (not absolute risks), which tends to make results look more impressive than they really are, and the statistical significance level is presented in the form of confidence intervals, not p-values (not a problem in itself, just a different way of presenting data). If you haven’t already done so, I strongly recommend you read my guide to scientific method before reading further, in order to make sure you understand all the terms used and gain maximal value from the content. Anyway, let’s look at the results.
The factors that most strongly predicted the number of people who died of covid in a country were rate of obesity, average age, and level of income disparity. Each percentage point increase in the rate of obesity resulted in a 12% increase in covid deaths. Each additional average year of age in the population increased covid deaths by 10% . On the opposite end of the spectrum, each point in the direction of greater equality on the gini-coefficient (a scale used to determine how evenly resources are distributed across a population) resulted in a 12% decrease in covid deaths. All these results were statistically significant.
Another factor that had an effect that was significant, but more weakly so, was smoking. Each percentage point increase in the number of smokers in a population was correlated with a 3% decrease in covid deaths.
Ok, let’s get to the most important thing, which the authors seem to have tried to hide, because they make so little mention of it. Lockdown and covid deaths. The authors found no correlation whatsoever between severity of lockdown and number of covid deaths. And they didn’t find any correlation between border closures and covid deaths either. And there was no correlation between mass testing and covid deaths either, for that matter. Basically, nothing that various world governments have done to combat covid seems to have had any effect whatsoever on the number of deaths.
We’re going to come back to this incredible fact in a little bit, but first we’re going to go off on a little tangent. As mentioned, the researchers didn’t correct for the fact that they were looking at a ton of different relationships, rather than just one single relationship between two variables. As I have discussed previously in my article on scientific method, the more relationships you look at, the more strictly you have to set the cut-off for statistical significance, since you will otherwise just by chance get a lot of relationships that seem significant but aren’t.
If you set a p-value of 0,05 (5% probability that a significant relationship was seen in a study even though there isn’t one in the real world), then one in twenty relationships you look at will be statistically significant just by chance. The 5% cut-off is intended to be used when looking at a single relationship, not when looking at multiple relationships. Now, in this study, the authors used confidence intervals instead of p-values, but that doesn’t change anything. A 95% confidence interval is equivalent to a p-value of 0,05, and so the same rules apply.
When you look at multiple relationships at the same time, you are supposed to correct for it. One way to correct is by using a method called the Bonferoni correction formula. This formula is very simple to understand. Say you have a p-value of 0,05 when looking at one relationship (the standard p-value in medical science). If you instead look at two relationships, you divide your p-value by two, thus getting a new p-value for significance of 0,025. If you are looking at ten relationships, you divide by ten, thus getting a new p-value of 0,005.
The authors who performed this study used a 95% confidence interval, as though they were only looking at one relationship between two variables. But they were in fact looking at a ton of variables (they never even specify how many) and a huge number of relationships, so they should have set their confidence interval much more widely.
They did have some results that they claimed were statistically significant, which I haven’t bothered to mention yet, because they’re certainly not significant after statistical correction.
For example, the authors claim a significant correlation between the Gross Domestic Product and covid deaths (relative risk 1,03, 95% confidence interval 1,00 to 1,06), and a significant correlation between the number of nurses per million population and covid deaths (relative risk 0,99, 95% confidence interval 0,99 to 1,00). But if you adjust, as they should have done, for looking at a large number of variables, then there is no way these results would still have been statistically significant. Sorry nurses.
So, what can we conclude from all this?
First of all, lockdowns do not seem to reduce the number of covid deaths in a country. Oops. Based on this data, if you want to decrease the number of covid deaths, you should encourage more people to start smoking, and possibly also start a communist revolution, to equalize wealth as far as possible.
Just kidding. As I’ve mentioned, the data is observational, so we can’t say anything about causality. What we can say from this is that lockdowns don’t seem to work – if they have any effect at all, it is too weak to be noticeable at a population level.
The other important finding from this study, from my perspective, is the strong link between obesity and risk of dying from covid. We can’t say that obesity in itself increases risk of dying – people who are obese have so many different biological systems malfunctioning at the same time that it’s impossible to say whether obesity is the cause of increased risk of death or just a marker of poor health in general.
Regardless, obesity is the strongest covid risk factor that we can do something about. And even if it isn’t the obesity itself that kills people, when we fix the obesity, we also fix the many derangements in metabolism and immune function that go along with it. So it is reasonable to think that efforts to decrease the rate of obesity in the population would decrease the number of people dying of covid. That is where we should be putting our efforts as a society right now – making people healthier so that their bodies are able to fight off covid (and cancer, and heart disease, and dementia, and all the other things that preferentially kill people with sub-optimal health).
You might also be interested in my article about whether vitamin D can be used to treat covid, or my article about whether a low fat or low carb diet is more effective for weight loss.
36 thoughts on “Does lockdown prevent covid deaths?”
Is it significant that the NHS in England has sent home obese nurses because they are more at risk in the hospital environment? I suspect the link between obesity and increased mortality from covid19 is very widely known.
Sebastian an alternative hypothesis is that the difference is down to how good governments are at actually-efficiently running real programs ,is what makes the difference.
In short lockdowns are worth it if a government can use the time bought by the delay to get track test and trace really working at max speed.
For example in Australia the two most populous states ,despite being right next to each other, have followed two very different paths.
Victoria has a government that is inclined to ‘performance’ : hundreds of press conferences a government is both very centralised and yet chaotic . When Victoria’s first lockdown ended it’s track test and trace systems were not up to scratch and it quickly ended up back in lockdown for most of the year ( mercifully it now seems to be OK but it’s been an awful costly victory)..
In contrast the state of NSW used the initial period of lockdown to get its track test and trace working extremely well and therefore its community and economy has been able to function reasonably well since then (with restrictions are similar to those current in Sweden) without seeing a second blowout in infections.
BTW you might find this perspective of interest
The COVID-19 pandemic has revealed how valuable it is for governments to have operational expertise, plan for the long-term and socialize certain risks.
By Steven Weber and Nils Gilman
Thanks for this sensible, balanced discussion of this issue. If only the made-for-tv doctors/commentators we have have here in the USA could be so smart and honest. Your observation that obesity is a general marker (or even cause) of illness is also excellent. It is not just a problem of how you look or function, obesity is a cause of metabolic, inflammatory, immune and brain dysfunction.
“This failure has taken place in Italy, Iran, Spain, England, Sweden, the U.S. and elsewhere; authoritarian, conservative and social democratic governments alike have been overwhelmed.”
How was Sweden overwhelmed? Swedish approach does not fit any of the 3 patterns mentioned in the article, it should have been analysed apart,
Fasinating and an incredible conclusion. I am stunned, especially given the fact that the number of cases began to fall drastically after about 10 to 14 days of full lockdown. Until now, I had assumed that fewer cases would mean fewer deathes. Am I misunderstanding something here? Perhaps the reasearchers are saying that the % of cases that resulted in death was not reduced by lockdowns rather than the overal number of deathes was not reduced?
The smoking figure is interesting too. Again, totally counter-intuative to me, but the correlation is nonetheless there.
As is the obesity issue. I and several other people I know have started to lose weight as a result of you and others in the medical community sharing this data and breaking it down into lay terms. Thank you very much for taking the time to do this. It is very much appreciated.
Thanks Julia. No, thats’s not it – they’re talking about total covid deaths, not covid deaths as a share of covid cases.
I don’t necessarily agree with everything they say.
Rather its , if ( and it’s a big if) you do decide to go for mandated lockdowns then you must be able to get track test and trace working at max speed as quickly as possible.
Otherwise you have a tactic: delay , but no strategy: no purpose.
BTW the figures re smoking are quite well established by now and realy are a mystery. There was some speculation that nicotine ( not smoke) in itself might in some way block the virus’s ability to enter cells . It’s also well established that smokers are less likely to develop Parkinson’s.
What would be interesting to know is if people who use smokeless products such as Snus are also underrepresented in Covid19 cases needing treatment.
Yes, that would be interesting. I’m not aware of any studies looking at that. I seem to remember hearing of some study in France where they were going to try treating covid patients with nicotine patches. Not sure what happened with that.
Great article, as always!
I have one minor comment regarding your claim that the lack of a correlation between lockdown stringency and COVID deaths would suggest a lack of causation, or at most a weak causation. The lack of correlation in the lancet study involved a ‘between countries’ analysis, where the lockdown stringency at May 1st was not associated with the COVID death rate at May 1st. Such analyses ignore that a causal effect of lockdown stringency on death rate is operating at a within countries level, across time.
An effect found in a between countries analysis can be completely different from the effect found in a within countries analysis that takes into account change over time. For instance, it is possible that an increased lockdown stringency within countries over time results in a lower death rate over time, while this effect disappears (or is even reversed) when analysing the country averaged data. This phenomenon is known as Simpson’s paradox. I made a figure based on some hypothetical data to illustrate this issue: http://www.paultwin.com/wp-content/uploads/COVID_Simpsons_paradox-page-001-1.jpg
I therefore think this question can better be analysed by taking into account change over time within countries, testing whether changes in lockdown stringency over time result in changes in death rate a few weeks later (after adjusting for possible confounders such as seasonality).
Thanks for your thoughtful answer, and the very helpful graph. I agree that it is valuable to compare countries with themselves over time when trying to figure out what effect lockdowns have. But I also think between country comparisons are useful. Although the study was comparing lockdown stringency on May 1st with deaths on May 1st, most of those measures were put in place in early March, which should give enough time for the measures to have a noticeable effect on deaths.
And as you suggest, comparing countries with themselves over time is connected with problems of its own that can also confound the results, for example due to the fact that the virus has an epidemiological life cycle of its own, and due to the effect of seasonality.
Many people are claiming that Sweden’s higher number of deaths when compared with its neighbors was due to its lack of lockdown, and this study suggests that that isn’t the case.
If lockdown doesn’t prevent covid deaths, how do you then explain the difference in covid deaths between Sweden (no lockdown in spring) and its neighbors (Norway, Finland, Denmark)?
I think the explanation is connected with the unusually low number of deaths in Sweden in 2019 (6% less than average), which meant that at the beginning of 2020 there was an unusually large number of very frail people in Sweden. Obviously we can’t go back in time and see how many people would have died if Sweden had instituted strict lockdown, but this study suggests that the result would largely have been the same, i.e. Sweden would have had a lot more deaths than its neighbors even with strict lockdown.
there are numerous factors at hand here.. for instance you can look at rates outside of stockholm and u find that sweden actually compares to neighbouring countries without seeming very different at all. note: sweden has the largest city in scandanavia (stockholm) – stockholm has a population of 1 million (1.6million if u include nearby area)
Copenhagen ranks second with a population of 794,128 (altho it rises if u include nearby area to close to stockholm but not quite as big)
Norway’s capital city Oslo has a pop of 697,549.
sweden also has more elderly people from various backgrounds than neighbouring countries- such as italy and iran- which were also early on impacted by covid. (as was sweden)
stockholm is a far more international city, around 10% of the city’s population regularly travels overseas. and many had been to the italian alps that winter as it was a shitty winter domestically for snowing. norweigans go cross country skiing domestically far more. for example..
and also sweden meticulously counts the data- counting all deaths within 30 days of a covid infection- regardless of actual cause(s) of death. I have been unable to find out how norway or finland or denmark actually count their “deaths with covid”
also sweden has a constitutional protection against lockdowns- which had to be subject to a special process to even allow some of the possibilities for lockdown like measures later on. and its more decentralised in terms of its health policy re: infectious disease. so thats why there were differences and perhaps more transparency in some ways
Low hanging fruit…. Obesity is, more often than not, is closely associated with ‘metabolic syndrome’ / T2 Diabetes which is another known risk factor. So, both easy and understandable to send the cuddly Nurses off the ‘firing line’. A very wise precaution !
At the same time, this would be wonderful chance to address the culprit, High Carb – high PUFA oils eating patterns of modern ‘Official’ dietary advice.
It would be a better idea to test them to see if they were metabolically healthy ie normal blood sugar ( and post prandials as well as fasting) , normal blood pressure . It is possible to have a BMI that puts you in the obese category and still have no comorbidities just as you can have a normal or low BMI and still be metabolically unhealthy.
Lets start with this 16 possible factors of the difference:
Dr Rushworth, do you have the numbers for Finland, Norway & Denmark concerning 2019 deaths compared to average?
Here are some other factors that matter in the Swedish deaths discussion.
When you take a look at Swedish deaths, you will see that the majority of them happened in the Nursing homes, which are highly regulated. A great many good Swedish laws and regulations, aimed at preventing the mistreatment of the elderly and people with dementia in nursing homes, meant that you could not immediately isolate the people who were sick. You aren’t allowed to lock them in a room, or tie them in a bed, or drug them so they wouldn’t leave their beds, or even move them from one floor to another. And, of course, the people with severe dementia — severe enough that they are in a Nursing home in the first place — are precisely the sort of people who cannot be trusted to keep their distance, not touch others, and not cough openly. So when trying to prevent deaths in Nursing homes, the thing you need to do is to keep covid out of them altogether.
One of the things that _was_ done in Sweden was to lock down the people in the Nursing homes, and prevent them from having visitors. This clearly had an effect. Some places locked down their members on their own initiative as much as a week earlier than the Heath Agency so ordered, and they had much less incidence of covid in their places. But, once you have got these people locked down you no longer need to lock down the whole country in order to protect them. We know where they got their covid, and it wasn’t from hanging out in bars and cafes. Their caregivers brought it to the Nursing homes. This problem was exacerbated by the fact that there wasn’t enough protective equipment for the caregivers, and not all of them were adequately trained in how to use them, and in the middle of a pandemic you cannot just get more delivered overnight. All of these problems got worse because the care homes, suddenly needed more staff, whose job was to circulate around and remind people not to touch each other, etc. They called up their temporary workers, who were not tied to any particular Home, but could work in any of them. Normally this flexibility is a good thing but in this case it just meant that an worker who got asymptomatically ill could then go on any spread the disease in another Home that had been covid-free up to this point. Recall that in early March, most authorities believed that only people who had symptoms could spread the disease to any great extent. By the time we learned otherwise it was too late for many people.
So. Lots of things that need improving in the Nursing homes in the light of what we know about pandemics. But a general lockdown won’t have any effect on these problems because these caregivers who spread covid are precisely the people who would be told that their work was essential and would not be subject to lockdown It would only have an effect if you thought that the caregivers were all catching covid in the cafes and the bars, instead of at work which is where we think they caught it, and is very much more likely. And, indeed we know that one of the places, aside from work where they caught it was ‘on vacation out of the country’.
So when did Sweden, Norway and Denmark have vacations? Well, Sweden, as usual had them staggered throughout Februrary. Which is convenient if you want to estimate ‘how likely were you to get covid on vacation’.
Winter break Copenhagen and Göteborg: 10.2.2020 – 14.2.2020
Winter break Oslo and Malmö : 17.2.2202 – 21.2.2020
Winter break Stockholm: 24.2.2020 – 28.2.2020
(source:https://publicholidays.dk https://publicholidays.no and https://publicholidays.se)
Since the people of Göteborg, Copenhagen, Oslo and Malmö did not immediately head for the hospitals when they came back from vacation, we know that they didn’t find much sickness while away. This wasn’t true for the Stockholmers. They came home sick. Danes and Norwegians who happened to be travelling that week came home sick as well. 2 weeks after the Stockholmers return travel is shut down, a pandemic is declared, and all the countries get to deal with all their own sick citizens. But there are many, many, many more sick Swedes proportionally than Danes or Norwegians, because the Danes and Norwegians estimate that less than 1% of their citizens were out of the country at the time. But more than 10% of the Swedes were. (I only have a source for the Swedish figure https://www.thelocal.se/20200611/public-health-agency-head-coronavirus-came-to-sweden-from-countries-that-were-under-our-radar) . If you start with 10 times as many people per capita who were away and might have become sick, it is not surprising if you end up with per capita, 10 times as many sick people.
I think you are dead wrong here with your conclusion. The paper explicitly states “policy of full lockdowns (vs. partial or curfews only) was strongly associated with recovery rates”. Which makes sense because lockdowns happen when infection rate is high and it prevents the infections from resulting to additional deaths.
Correlating number of deaths with lockdowns is nonsense because lockdowns happen exactly when death rates are on the rise and getting high when mandates come to late. To correlate dates to the first case of a country (like in the paper) makes less sense in this regard. So the correlation/causation goes: high death rates -> lockdown – trapped in a fallacy. You wouldn’t argue when traffic has been stopped because of an accident the stopping would be useless.
Lockdown works on the dynamics of the pandemic, changing the trend of it. As far as I can see the publication does only look at static numbers.
Look at the results, not the conclusions. It is actually quite common for conclusions to vary from results. The results are what matter, not the conclusions of the authors. And in this case, the results are not politically correct, so the conclusion does not align with the results. You can be sure that if there was a significant negative correlation between lockdown and deaths, then they would have trumpeted that result heavily, maybe even made it part of the title of the paper. Instead they only mention it in passing in the results.
If it’s nonsense to correlate deaths with lockdowns, then why did they do it?
I think they looked at the correlation between lockdown and covid deaths because they were hoping for a negative correlation, but they didn’t find it. As I stated, the data were gathered up to May 1st, while lockdowns came in to force in March. That should be enough time to see an effect of lockdown on covid mortality.
The average time from infection to death is about three weeks, so if lockdown was implemented in a country in mid-March, then that should start to have an effect during the first half of April. There should reasonably have been three weeks in April where countries that implemented strict lockdown had noticeably fewer deaths than countries with more relaxed attitudes. And late April was when covid deaths were at their highest, far higher than February and March, so the fact that there were already some deaths baked in due to infections happening before lockdown was implemented, should not be enough to erase the difference if it is significant.
Having said that, I agree that it would have been good to also have the results for later months. I hope this study is followed up with data for the full year.
Sebastian haven’t seen any further re that French study.
However because from memory the effect seemed to be dose dependant I.e. it was only regular smokers not light occasional smokers that were underrepresented. It could be that the low dose given by patches is insufficient to have an effect. Unfortunately because of the traditional association of nicotine with smoking there is little interest in and strong pressure against doing research into any possible benefits of nicotine in itself.
OK, this might be a stupid question but…has anyone tried blowing cigarette smoke on Covid viruses to see if it affects them? Seems like it would be a very simple experiment. Maybe cigarette smoke kills the virus. The virus in most cases infects tissues in the airways and lungs. If those areas are frequently fumigated with smoke that is toxic to the virus, then maybe infection is less likely.
If Peru that has perhaps the strictest of Lockdowns and Spain that has perhaps the strictest Mask mandate both have similar infection rates to other countries it indicates that these measures do not work very well to stop infection – There is a proposal that colossal quantities of virus are carried in the atmosphere and whipped up into the pattern of global air currents and that pattern matches the pattern of global infection – In this way the virus is delivered to each Locked Down location and the airborne virus pass through masks like mosquito’s through a chain link fence and that would explain why the authoritarian measures of Spain and Peru are useless – If this is the case it will not be welcomed by authoritarian governments who would likely suppress such understanding as was done for any treatments that rivalled the vaccine solution.
I am wondering about when to take which vaccines… Is it better to wait and see which one is best? Could it be possible to take a few of them (not at the same time)?
In other words, why take one right away if that makes it risky to take another one when something more effective comes along? Could taking more than one vaccine over-stimulate the immune system causing the infamous cytokine storm?
I doubt taking multiple vaccines in itself would be a problem, if anything it might increase the probability of developing effective immunity. Although unless you are part of a group that is at high risk for severe disease, I would wait to take a vaccine until there is good safety data.
Good points. I would add the game changer: early treatment with HCQ + Zn + AZ or Ivermectin + Zn + quercetin outside the hospital before day 4 of symptoms start. All protocols include an antibiotic, for ivermectin doxycycline is preferred and all receive vitamins D and C. Reduces hospital need 80 to 90%
Why include an antibiotic?
I am not 100% sure that masks have not helped in Spain. Looking at the figures for my health district, for the past few months, 70% of the outbreaks occured in social settings. Private gatherings, family get togethers, meals in restaurants, sharing coffee and breakfast with friends from other households in the morning.
In all of those settings, the wearing of masks is not required and people in my area, myself included, rarely wear them when carrying out those activities. You can´t while eating and drinking. There is also appears to be marked uptick in the numbers 10 days or so after special holidays when these activities are far more widespread.
For these figures, an outbreak is categorised as 3 or more cases from the same source or 1 case if in a care home setting.
I am not saying that masks have definately helped to reduce the spread here in Spain. That is most definately still up for debate. I am just adding a local perspective to the conversation.
Does lockdown prevent covid deaths?
If it did, you should have excess mortality rate in countries without lockdowns, like Sweden, which clearly isn’t the case. On the contrary, it’s less deaths than an average year. Clearly, the only objective statistics you can look at is the overall mortality rate, and overall patients at ICU, everything else is just biased opinions, tampered data, and scaremongering.
The major thing which distinguish covid from previous cold and flu virus, is it’s fast spread globally, basically to all countries within a couple of months. If you want herd immunity which is the only natural way of protecting the population, lockdown is a crazy idea, beacause it’s only going to delay or prevent herd immunity. Close the borders for a couple of weeks to flatten the curve, fine, but longer than that is totally counterproductive. According to the definition of herd immunity, (no exponentially increase or decrease), then applied to the death tolls, Sweden had herd immunity at the last week of May, T-cell studies in the summer confirmed that conclusion.
Result of the Lockdown: The cure is worse than the desease, by a substantial margin.
The Lockdown has now slowed down or totally stopped businesses all over the world for 8 months, which causes irreparable and devastating economic, social and personal results, countless bankruptcies, suicides, and homelessness to name a few. I wonder, when is enough, enough? after 10 months? after 1 year? after 10 years? and who honestly believe that the worlds population are going to survive without enough production? With a crashed economy?
The push for vaccin by big pharma is a mirage, it takes many years to develope a safe vaccin which also works, the absence of a safe and functioning vaccin for SARS1 (which came 2003, 17 years ago), is as clear evidence of that, as anything.
When the alarm bells has silenced, and the smoke has cleared, this global Lockdown may very well be the worst decision ever made by anyone, in peace time.
as a professional researcher and user of statistics (biomedical engineerings) but by no means a doctor / virologist / infectivologist and even no professional statistician, I have naively taken the task of trying and finding any potential relationship between the Oxford Stringency Index  and the simplest and most available measures of how bad the covid has been – cases and deaths.
so far – my analysis is still highly preliminary – it looks like there is no relationship at all between the OxSI and the acceleration of cases and/or deaths, even when we compare the SI with the other numbers *one or two weeks later*.
before I go ahead: are you aware of anyone already trying to do this in a more professional way?
Excellent, no at present I’m not aware of any other studies doing this. You should definitely try to get your results published when you’re finished.
Anyone who thinks it works should show me the deflection of the graph either in deaths or recorded infections. Infection and death curves all follow the same shape as expected. These curves of natural rise and fall are seen year after year for repeating viruses flu and norovirus and they go with seasonal fluctuation. It is not rocket science to apply a lockdown when the exponential phase has been raging for some time and then wait for the curve to fall to convince the public that government it is acting and to stop public panic. It worked the first time but not the second. If lockdown worked within two cycles of the virus ((3-4 weeks) there should be a dramatic fall in numbers – did we see one?
This is standard microbiology stuff that was taught to me in school back in the 1970s. We discussed how helpless we were with Hong Kong flu in 1968 and we do not seem to have advanced sine then? Slow moving SARS and EBOLA could be contained by immediate action but fast moving viruses like flu and the common cold can’t.
No one yet has mentioned that flu tears through nursing homes every winter and is the cause of seasonal deaths that the public ignore every winter. In the UK there are about 650,000 ‘normal’ deaths but it varies widely – one of the most recognised correlations is with the weather – please consult the ranges yourself for proof, there are plenty of sources.
Sadly, we haven’t yet reached the end of the Coronavirus deaths but I read long articles and much debate over what system is best etc. countries who fared well at the beginning are being hit by the autumn surge now. Death rates on Wikipedia for all countries show similar rates across the world.
Our real problem with Covid is one of belief. There must be something we can do? There has to be an answer? We steadfastly and repeatedly refuse to believe that we can not do something about it, it is hardwired into our brains – to find cause or blame and not accept the truth – that we have been spitting in the wind. Like any good confidence trickster governments are now using the panic to blame people who are out after 10pm – as though the virus knows what time it is – to deflect from telling the public the reality.
People reading this article may also like to watch Ivor Cummins on you tube. Not a fan of Youtube but in his monthly briefings on Covid-19 are about facts and recently the scathing lack of care for cancer and heart patients who are not being treated and will lead to preventable deaths because of Covid-19?
Thanks again for some real insight and dispelling the ‘beliefs’ even scientists have that we are masters of this situation.
I have been convinced for months that the lockdowns and other disruptive measures taken by many governments have not been effective in substantially reducing mortality, based on a simple observation, namely the fact that, between the countries having the highest numbers of deaths per million people, there is little difference in these numbers.
If I look today (written 2021-02-26) at the column “deaths/1 M pop” on Worldometers (https://www.worldometers.info/coronavirus/#countries), I see that between Belgium (with 1893 deaths/1M) and Moldova (with 961 deaths/1M), there are about 30 countries with mortality rates encompassed within a ratio of 2. This seems to me a surprisingly short range of variation, especially because, the virus transmission being an exponential phenomenon, small differences of success in reducing the transmission number can translate into very high differences in deaths.
Indeed, it is the case for some successful countries like Japan (61 deaths/1M), South Korea (31 deaths/1M), New Zealand (5 deaths/1M), Taiwan (0.4 deaths/1M) and others, which show death tolls up to 3 orders of magnitude lower. These countries are the evidence that social measures, in some well-organized societies, can indeed be effective. But for the countries with the highest mortality numbers, is there any evidence that without the measures applied, it would have been worse? Where is the case showing the catastrophic predictions made by the Imperial College team? Even the very small states of San Marino and Gibraltar, with many elderly people, do not reach 3000 deaths per million people. Even in subnational data, we do not find a territory that reached much higher death numbers, which suggests that nowhere has the infection reached 70-80% of the population.
It could be argued that this is because every country did apply some level of restriction. But given the high differences between the way these restrictions were applied by governments and followed by the population, as well as the exponential nature of epidemics, we would still expect a higher dispersion of deaths numbers on the high tail of the distribution.
The fact that, in extremely different countries, the highest death numbers are capped in a relatively narrow range, well below the maximum predicted, could be a coincidence. But it seems to me a singular one. Another explanation is that these numbers actually represent the highest “natural” limit at this time of the outbreak. In other words, if nearly nobody did worse than New Jersey, it is because it was not possible to do much worse.