Reposted from Dr. Judith Curry’s Climate Etc.
June 21, 2020 by niclewis |
By Nic Lewis
Key points about the recent Nature paper by Flaxman and other Imperial College modellers
1) The transition from rising to declining recorded COVID-19 deaths in the in 11 European countries that they studied imply that transmission of COVID-19 must have reduced substantially.
The study was bound to find that together the five government non-pharmaceutical interventions (NPI) they considered contributed essentially 100% of the reduction in COVID-19 transmission, since in their model there is nothing else that could cause it.
2) The prior distribution they used for the effects of NPIs on transmission in their subjective Bayesian statistical method hugely favours finding that almost all the reduction in transmission is due to one, or possibly two, NPIs with all the others having a negligible effect.
The probability density of the prior distribution at their median estimates of the effect on transmission of each type of NPI, which allocate essentially all the reduction in transmission to lockdowns, was many billion times greater than it would have been if the same total estimated reduction had been spread evenly across the types of NPI.
3) Which intervention(s) is/are found to be important depends critically on the assumptions regarding the delay from infection to death. When using their probabilistic assumptions regarding the delay from infection to death, a huge (and highly improbable given other assumptions they made) country-specific effect is required to explain the reduction in transmission in Sweden, where no lockdown occurred. If delays from infection to death are increased by just three days, their model no longer finds lockdowns to have the largest effect, and a more moderate country-specific effect is required to explain the reduction in transmission in Sweden.
4)The estimated relative strengths of different NPIs are also considerably affected by the use of an alternative prior distribution for their effects on transmission that does not strongly bias the estimation of most of them towards a negligible level. They are also considerably affected by phasing in over a few days the effects of the two NPIs that seem unlikely to have had their full effect on their date of implementation.
5) It follows from the above that that study provides no information whatsoever as to the actual contribution from all NPI combined to the reduction in transmission, and nor does it provide robust estimates of relative effects of different NPI.
Introduction
On 8 June 2020, Nature published a paper (Flaxman et al. 2020[1]) by modellers in the Imperial College OCIVD-19 response team. Its abstract ends with:
Our results show that major non-pharmaceutical interventions and lockdown in particular have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
Using a counterfactual model, the paper also estimated the impact of interventions on deaths from COVID-19 in the 11 European countries studied, saying:
We find that, across 11 countries, since the beginning of the epidemic, 3,100,000 [2,800,000 – 3,500,000] deaths have been averted due to interventions.
The mainstream media publicised the ‘3 million deaths saved’ claim, without critically appraising the paper or, generally, mentioning the relevant caveat in the paper:
The counterfactual model without interventions is illustrative only and reflects our model assumptions.
In Imperial College’s press release Dr Flaxman ignored his own caveat, saying
Using a model based on data from the number of deaths in 11 European countries, it is clear to us that non-pharmaceutical interventions– such as lockdown and school closures, have saved about 3.1 million lives in these countries
In this article I examine the main claim – that major non-pharmaceutical interventions (NPI) have had a large effect on reducing transmission of COVID-19, to which the inferred reduction in deaths is attributable, with almost all the reduction due to lockdowns. I show that this claim is strongly dependent on the assumptions made and is highly dubious.
The case of Sweden, where the authors find the reduction in transmission to have been only moderately weaker than in other countries despite no lockdown having occurred, is prima facie evidence against the paper’s main claim.
How the effects of lockdowns and other interventions were estimated
Flaxman et al. employ a ‘hierarchical Bayesian’ statistical model. It uses data on daily deaths (up to 5 May 2020, when two countries relaxed their lockdowns), the dates of imposition of five types of NPI (school or university closure, case-based self isolation, public events banned, lockdown ordered and social distancing encouraged), and estimates of the infection fatality rate, for each of 11 European countries.[2] Using these data, the model infers what time profiles of the effective reproduction number (Rt, the number of people whom an infected person in turn infects) – and hence of new infections – would produce the best match between projected and recorded deaths for each country. To do so it uses a simple model of epidemic growth and probabilistic estimates, common to all countries, of the time from infection to death and of the generation time (that from a person becoming infected to them infecting others). The assumed infection fatality rate (IFR) is common between countries for each age band, but reflects the age-structure of each country’s population. It averages slightly over 1%.
A separate initial value, R0 (the basic reproduction number), of the reproduction number Rt is inferred for each country. Rt then changes from R0 in stepwise fashion at the date of each NPI, which act multiplicatively with an equally strong inferred effect for all countries. Each country’s epidemic is seeded by a series of infections starting 30 days prior to a total of 10 recorded deaths.[3]
The model is described in more detail here, and is illustrated in Figure 1, taken from Flaxman et al.Fig. 1. Reproduction of Flaxman et al. Extended Data Fig. 3: Summary of model components
The treatment of interventions
The model uses no information on NPI’s except their type and their implementation date in each country. NPI of each type are treated as having the same (multiplicative) effect on Rt in each country. Each type of NPI is treated identically. As well as the five types of actual interventions, all first interventions (whatever type) are treated as an extra type of intervention, for each country occurring on the date of implementation of its first actual NPI (almost always either self isolation or public events ban, and never lockdown). Hence there are six NPIs with shared values for all countries.
In addition, a pseudo-NPI with a strength that is estimated separately for each country is treated as taking place on the same date as the last actual NPI. These country-specific pseudo-NPIs allow for variation between countries in the effectiveness of the implementation of their NPI. They are probabilistically constrained to be relatively small, making a country-specific effect large enough to cause a halving of Rt exceedingly improbable.
In all 11 countries the exponential growth in infections and deaths experienced early in the epidemics slowed and then turned negative, with infections and deaths decreasing. This implies that in all 11 countries Rt decreased very substantially, to below one, since the start of their epidemics.
In the Flaxman et al. model the only factor that can cause Rt to decrease significantly is the effect of each NPI. Therefore, the estimated overall effect of the NPIs in reducing Rt, and hence deaths resulting from COVID-19 disease, is bound to be very strong.
The only non-NPI factor that affects Rt in the Flaxman et al. model is the reduction arising from the proportion of the population susceptible to infection (set at 100% initially) gradually diminishing over time due to individuals already infected by COVID-19 becoming immune to it. This reduction is very small in their model, for two reasons:
- they make the very unrealistic assumption that all individuals in a country are equally susceptible to COVID-19 and, if infected, are equally likely to infect others.
- the relatively high infection fatality rates they assume result in only very small proportions of countries’ populations becoming infected in their model.
Therefore, their model has to attribute almost all the overall reduction in Rt to government interventions.
Factors not considered by Flaxman et al., all of which are highly likely to have caused some reduction in COVID-19 transmission, and which between them may well have caused substantial reductions in Rt in all 11 countries, include:
- population heterogeneity in social connectivity – which generates highly correlated heterogeneity in both susceptibility and infectivity – and in other factors determining susceptibility to COVID-19
- unforced changes in the behaviour of individuals as they adjust it to reflect COVID-19 risk
- seasonal factors: infections by common coronaviruses peak in the winter and diminish greatly as spring progresses.
As is well known by competent epidemiologists, the first of the above-mentioned factors causes Rt to diminish faster, potentially much faster, with the number of people who have been infected than if it were proportional to the number of people remaining uninfected, as assumed by Flaxman et al. The other factors directly reduce Rt.
If follows that Flaxman et al.’s counterfactual case, which predicts ~3,200,000 deaths in the absence of any NPIs (their ‘counterfactual model’), is completely unrealistic, as therefore is their estimate of 3,100,000 lives saved by interventions.
It also follows that Flaxman et al.’s claim:
Our estimates imply that the populations in Europe are not close to herd immunity (~70% if R0 is 3.8)
may be invalid. As shown here, due to population heterogeneity in susceptibility and infectivity the herd immunity threshold it is bound to be lower – quite possibly very substantially so – than if, as required for it to be ~70% at an R0 of 3.8, populations are homogeneous.
Flaxman et al.’s assertion that all the reduction in transmission (i.e., the reduction in Rt) was due to NPIs, other than very small reduction as more people have been infected and become immune, is unsound. Nevertheless, it seems quite likely that NPIs have had a significant, perhaps substantial, effect on Rt. However, given the confounding effects of the other factors mentioned it is impossible reliably to estimate the total effect of NPIs on Rt and hence on deaths.
Even when making the unrealistic assumption that almost all the reduction in Rt was due to interventions, any allocation of that reduction between the NPIs is very fragile. Flaxman et al. accept this in relation to NPIs other than lockdown, writing:
Most interventions were implemented in rapid succession in many countries, and as such it is difficult to disentangle individual effect sizes of each intervention. In our analysis we find that only the effect of lockdown is identifiable, …
On their median estimates, lockdown caused an 82% reduction in Rt, whereas no other NPI caused as much as a 1% reduction in Rt. While it would not be particularly surprising if such a drastic intervention as lockdown had had stronger effects than other NPIs, even if lockdown had a strong effect one would expect some other NPIs to have had a significant effect. So how did Flaxman et al. find that, remarkably, almost the entire effect of interventions was due to lockdown? The answer, which turns out to be two-fold, shows that their finding is not credible.
Why Flaxman et al. found almost all reduction in COVID-19 transmission to be attributable to a single intervention
Flaxman et al. use a subjective Bayesian statistical method. I have repeatedly criticised this type of Bayesian method in the climate science field, but – probably due to its ease of use – it remains standard practice there and in many other fields.
A subjective Bayesian method requires prior probability distributions to be assigned for each unknown parameter whose value is to be inferred. These prior distributions are then modified by the likelihood function, which reflects how well the modelled deaths fit the daily deaths data at varying values of the parameters, in order to arrive at a ‘posterior’ probability distribution for the parameter values. They use a common method of achieving this that results in a large number of quasi-random draws (‘posterior draws’) from the derived posterior probability distribution.
They represent the strength of interventions by a six dimensional parameter alpha (five actual NPIs plus the synthetic first intervention NPI), with the corresponding effect of intervention i (i being 1, 2,3, 4, 5 or 6)[4] on Rt being to multiply it by exp(-alpha[i]).
The combined effect of all interventions is then to multiply Rt by exp[-(alpha[1] + alpha[2] + alpha[3] + alpha[4] + alpha[5] + alpha[6])][5], which depends only on the sum of the individual alpha values. Their own posterior draws show a median value of the sum of the alphas of 1.75, which corresponds to an 83% reduction in transmission (1 – e−1.75 = 0.83).
The prior distribution assigned by the authors to the strength of the reduction in Rt caused by each intervention is of particular concern. Each of the six alpha values is assigned a gamma-distributed prior probability distribution; a small offset is applied, so that the gamma-distributed values inferred initially are marginally higher, but that is a cosmetic feature.[6] The authors write:
The intuition behind this prior is that it encodes our null belief that interventions could equally increase or decrease Rt, and the data should inform which.
That is not in fact true. As the left hand panel of Figure 2 shows, their prior allows each intervention to decrease Rt by up to 100%, but only to increase it by less than 1%. And the combined effect on transmission of all interventions (right hand panel) can only vary between –100% and + 5%. However, since the trajectory of the deaths data is, on their assumptions, bound to result in all interventions combined being found to strongly reduce transmission, the +5% limit is of no real consequence.
Fig. 2. Reproduction of the upper panels of Flaxman et al. Supplementary Fig. 3: Cumulative distribution function F(x) of the prior for one intervention’s multiplicative effect x (= e–α) on transmission (left) or for the effect of all interventions combined (= e–Σα) (right).
On the face of it, the combined effect of the six-dimensional joint alpha prior distribution looks fairly uniform over the range in which the estimated reduction in Rt could fall; it assigns a similar probability to a reduction in the range 40% to 50% and in the range 80% to 90%, for example. However, that only looks at one aspect of the six-dimensional prior distribution.
If I take the sum of the six alphas to be 1.75 (the median sum from their posterior draws) and set them to be all equal, at 1.75/6, their joint prior probability density is 0.0023. But if I set one of the alpha values to 1.70 and the remaining five to 0.01, giving the same overall reduction in transmission, the prior probability density is 64.3. That means their prior distribution assigns a 28,000 times higher prior probability assumption to this case, where one type of intervention has a completely dominating effect relative to all the others, than to a case where the same overall reduction in transmission is caused equally by all types of intervention. The reason is that the offset-gamma distribution used assigns a strongly increasing probability density as an alpha value decreases towards −0.008, its lowest permitted level, favouring cases where the effect of all but one or two NPIs is estimated to be almost zero.
So it is unsurprising that they found a single intervention to be totally dominant.
The median individual alpha values in their 2,000 archived posterior draws are −0.007, −0.007, −0.007, −0.007, 1.699 and −0.006. So all interventions except lockdown were estimated to have a completely negligible effect.
The median ratio, across their own posterior draws for alpha, of the actual prior probability to what it would have been if in each draw the total effect of the intervention had been spread evenly across them, was in fact 392 billion to one!
It is not clear that the authors realised that the prior distribution they used very strongly favoured finding that most interventions had a negligible effect, and I very much doubt that any of the peer reviewers appreciated that this was the case.
The Sweden problem
Using the code and data accompanying the Nature paper as is, except with the 8,000 draws split between 4 not 5 chains to better match my computer, I can accurately replicate Flaxman et al.’s findings, with lockdown accounting for almost the entire reduction in Rt (Figure 3).
Fig. 3. Effect of interventions on Rt in the base case, with all aspects of the model as per the original version (that archived for the Nature paper). The red First intervention estimate includes the effect of the synthetic first intervention NPI and so only applies for countries where the NPI concerned was the first to be implemented; it should be ignored in all other cases. Mean relative percentage reduction in Rt is shown for each NPI (filled circle) together with the 95% posterior credible intervals (line). If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19.
Sweden did not have a lockdown, but it still had a large reduction in Rt, albeit one not quite as large as the average for other countries. So how did the model account for that? This is where the country specific factors, which are treated as occurring on the date of the last actual intervention and in effect are an addition to its alpha, come in.
The country specific factors are given an apparently small influence, being zero-mean normally distributed with a standard deviation that is itself zero mean normal+ distributed[7] with a standard deviation of 0.2. But for Sweden a value of 1.27, in the far tail of the resulting distribution, was inferred. The probability of such a large country factor arising by chance appears to be about 1 in 2,000. That in itself implies that their model does not adequately represent reality.
Using a less informative prior
I investigated use of a prior distribution for the six alpha parameters that was essentially flat over the alpha parameter range relevant for NPI, both for each parameter separately and for the six-dimensional joint alpha parameter. For technical reasons, rather than using a uniform distribution I chose an independent zero mean normal distribution with a standard deviation of 10 as the prior distribution for each parameter. I hereafter refer to this as the ‘flat prior distribution’, even though it is not quite flat over the parameter range of interest (approximately 0 to 2).
I then ran the model using the same assumptions, but using the flat prior distribution rather than the original offset-gamma prior distribution. Doing so should eliminate the previous strong bias towards finding that most interventions had almost no effect.
The resulting estimates of the effect of each intervention were as shown in Figure 4. The estimated effects of NPI other than lockdown all increase markedly from their near zero values when using the original prior, but the contribution of lockdown remains dominant.
Fig. 4. Effect of interventions on Rt : as in Fig. 3, but with the flat prior distribution for alpha substituted for the offset-gamma prior distribution in the original model..
The country specific factor for Sweden was slightly less high than before, at 1.12. The probability of such a large country factor arising by chance appears to be about 1 in 900; still minute.
So, even when using the flat prior, the Flaxman et al. model does not adequately fit reality. The problem is that, as it still estimates lockdown to account for the vast bulk of the total reduction in Rt, it cannot adequately account for the reduction in Rt that occurred in Sweden, where there was no lockdown.
Why Flaxman et al. found lockdown was the intervention that dominated the reduction in COVID-19 transmission
I have explained why it to be expected, given Flaxman et al.’s choice of prior distribution for the effect of interventions on the transmission of COVID-19, that a single type of intervention (or at most two types) would account for the vast bulk of the reduction in Rt. But why lockdown?
The key here seems to be that lockdown was, other than in Sweden, on average imposed at a point in time that, allowing for the assumed probabilistic delay between infection and death, would result in deaths peaking at about the time that they actually peaked. Also, the timing of lockdown, relative to the peak in recorded deaths, differed slightly less between countries that locked-down than was the case for most other interventions.
Flaxman et al. took probabilistic estimates of the delay from infection to symptoms appearing and from symptoms appearing until death, with assumed mean values of 5.1 and 17.8 days respectively, and added them to obtain the infection to death delay values. The 5.1 day delay from infection to onset of symptoms seems reasonable. But the 17.8 days mean from onset of symptoms until death looks as if it may be on the short side for European countries. Ideally, a separate onset of symptoms to death delay distribution would have been estimated for each country. However, the authors may well have been unable to find suitable European data. They actually used a value estimated by Verity et al.[8] (also members of the Imperial College COVID-19 modelling team) from just 24 cases in mainland China.
One of the peer reviewers suggested that the value Flaxman et al. were using for the delay from onset of symptoms until death of (in the originally-submitted manuscript[9] being reviewed)18.8 days, not 17.8 days, was rather short, writing:
it is smaller than preliminary estimates available from hospitalization data in Europe (about 5-6 days from onset to hospitalization, at least 2 weeks in the hospital)
I therefore increased the average delay from onset of symptoms to death slightly.
I also took the opportunity to correct the dates used in the model inputs for school/university closure in Sweden and for self-isolation in Spain to those given in Flaxman et al. Extended Data Figure 4, which agree to those in their Supplementary Table 2.
I found that adding 3 days to the infection to death delay, bringing the average onset of symptoms to death delay to ~21 days (median 19.6 days) – which is fully consistent with the peer reviewer’s comment – was adequate to reduce the problem of Sweden needing a very large country-specific factor. That factor was then estimated at ~0.4, to match the reduction in transmission in Sweden – still over twice as large as for any other country, but no longer statistically-inconsistent with their assumptions.
The resulting estimated effectiveness of the various interventions, using the authors’ original prior distribution for alpha, is shown in Figure 5.
Fig. 5. Effect of interventions on Rt : as in Fig. 3 (original prior) but with the infection to death delay increased by 3 days, and one intervention date corrected for each of Spain and Sweden (see text).
School closure is now found to have a slightly stronger effect on transmission than lockdown. This may seem rather unlikely in reality, but the model has no information to go on regarding the likely relative strengths of each type of intervention – it just knows when they were implemented in each country. Other interventions are found to have almost zero mean effect, as is to be expected given the nature of the original prior distribution.
Using instead the flat prior gives slightly different estimates of the effectiveness of the various interventions (Figure 6), with school closure not having quite as strong an effect as when using the original prior. The effects of social distancing, and to a slightly lesser extent public events ban and self isolation (one of which is generally the first intervention, so the red line applies to it), all cease to be negligible.
Fig. 6. Effect of interventions on Rt : as in Fig. 5, with the infection to death delay increased by 3 days, but using the flat prior distribution instead of the original prior distribution.
If the infection to death delay is increased by 5 rather than 3 days from Flaxman et al.’s assumed probabilistic magnitude – arguably still as reasonable as Flaxman et al.’s assumption – and the original prior used, the changes in the relative effectiveness of different interventions become even more marked (Figure 7). Lockdown is now estimated to have far less effect than school closure, while social distancing now has a significant effect. The country-specific factor for Sweden becomes small.
Fig. 7. Effect of interventions on Rt : as in Fig. 5 (original prior) but with the infection to death delay increased by 5 days not 3 days.
When the flat prior is used instead, the estimated effect of school closure reduces while that of all other interventions increases (Figure 8).
Fig. 8. Effect of interventions on Rt : as in Fig. 6 (flat prior) but with the infection to death delay increased by 5 days not 3 days.
Finally, I investigated the effects of phasing in certain of the interventions. Flaxman et al.’s assumption that all interventions immediately have their full effect on their date of implementation is questionable. It may not be too unrealistic for closing schools, banning public events and decreeing a lockdown, all of which it is feasible to enforce. However, responses to self isolation advice and social distancing encouragement (which both generally preceded a lockdown) are more within the discretion of the individuals concerned, and very arguably would take a little time to reach their final strength.
I examined phasing in over four days the effects of just those two NPIs, with their strength increasing evenly from 25% on the date of implementation to 100% three days later. The result, using the original prior distribution for alpha and making a ~3 day increase in the delay from symptoms to death, is shown in Figure 9. The strength of the reduction in transmission attributed to lockdown reduces slightly compared with the no phase-in case, while than attributed to social distancing increases.
Fig. 9. Effect of interventions on Rt : as in Fig. 5 (original prior), but with the effects of self isolation and social distancing phased in over 4 days and the infection to death delay increased by 3.2 days.
Finally, I repeated this experiment using the flat prior (Figure 10). The strength of the reduction in transmission attributed to lockdown reduces noticeably compared with the no phase-in case, although it is still larger than that of school closure (the estimated effect of which reduces only marginally), while the estimated effects of banning public events and (particularly) social distancing increase markedly.
Fig.10. Effect of interventions on Rt : as in Fig. 6 (flat prior), but with the effects of self isolation and social distancing phased in over 4 days and the infection to death delay increased by 3.2 days.
Conclusions
First and foremost, the failure of Flaxman et al.’s model to consider other possible causes apart from NPI of the large reductions in COVID-19 transmission that have occurred makes it conclusions as to the overall effect of NPI unscientific and unsupportable. That is because the model is bound to find that NPI together account for the entire reduction in transmission that has evidently occurred.
Secondly, their finding that almost all the large reductions in transmission that the model infers occurred were due to lockdowns, with other interventions having almost no effect, has been shown to be unsupportable, for two reasons:
- the prior distribution that they used for the strength of NPI effects is hugely biased towards finding that most interventions had essentially zero effect on transmission, with almost the entire reduction being caused by just one or two NPI.
- the relative strength of different interventions inferred by the model is extremely sensitive to the assumptions made regarding the average delay from infection to death, and to a lesser extent to whether self isolation and social distancing are taken to exert their full strength immediately upon implementation or are phased in over a few days.
It seems likely that the inferred relative strengths of the various NPIs are also highly sensitive to other assumptions made by Flaxman et al., and to structural features of their model. For instance, their assumption that the effect of different interventions on transmission is multiplicative rather than additive will have affected the estimated relative strengths of different types of NPI, maybe substantially so. The basic problem is that simply knowing the dates of implementation of the various NPI in each country does not provide sufficient information to enable robust estimation of their relative effects on transmission, given the many sources of uncertainty and the differences in multiple regards between the various countries.
Nicholas Lewis
[1] Flaxman, S., Mishra, S., Gandy, A. et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature (2020). https://doi.org/10.1038/s41586-020-2405-7
[2] Denmark, Italy, Germany, Spain, United Kingdom, France, Norway, Belgium, Austria, Sweden and Switzerland.
[3] The seeding continues for 6 days, with the average number of seed infections per day being inferred by the model.
[4] The numbering of interventions used in their code is 1. school (and/or university) closure ordered; 2. case-based self isolation mandated; 3. public events banned; 4. first intervention; 5. lockdown ordered; and 6. social distancing encouraged.
[5] In mathematical notation, exp[-(alpha[1] + alpha[2] + alpha[3] + alpha[4] + alpha[5] + alpha[6])] is written e–Σα.
[6] The alpha distributions are defined by αi ~ Gamma( shape=1/6, scale=1) − loge(1.05)/6. Hence alpha can range between −loge(1.05)/6 (approximately −0.008) and plus infinity.
[7] “Normal+” means a normal distribution with the negative part of the distribution excluded.
[8] Verity R, Okell LC, Dorigatti I, et al. Estimates of the severity of COVID-19 disease. medRxiv 13 March 2020; https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.
[9] The original Flaxman et al. manuscript was submitted on 30 March 2020, the same date as Imperial College published “Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries.”, by the same (or almost the same) authors: https://spiral.imperial.ac.uk/bitstream/10044/1/77731/9/2020-03-30-COVID19-Report-13.pdf . From the referencing of comments in the Nature peer review file, it appears that the original Flaxman et al. manuscript was almost identical to Report 13.
Originally posted here, where a pdf copy is also available
We won’t know anything for sure about #19 until we clean the data …. such that it is. The world did a terrible job of data accumulation much due to political pressures.
You’re right. Just today, a new study from Penn State says that infection rates may be 80 times faster than initially believed. https://news.psu.edu/story/623797/2020/06/22/research/initial-covid-19-infection-rate-may-be-80-times-greater-originally#.XvFU1lJ9264.twitter
Sadly, this is not past tense.
The media clearly is conflating positive corona virus tests with hospitalizations.
“Largest spike in cases!!!!!!” Etc., Etc.
Balder and dash, poppy and cock, tom and foolery.
Add Malarkey and
Banana Oil (Brooklyn, NYC, USA)
The pandemic is still in progress
FINAL data will a need analysis for accuracy.
LOCKDOWNS are theoretical — what people actually did is most important.
The rules for people flying into the country are very important.
What was done concerning nursing homes is very important.
Deaths from ordinary flu are grossly overestimated according to doctors, due to use of models rather than a list of names of those who died.
People who died with pneumonia are often blamed on flu when there are over one doxen causes of pneumonia.
The only conclusions possible now:
There are no COVID experts yet.
COVID was worse than any other flu except the 1919 pandemic that killed my young grandmother when my mother was only two years old.
“COVID was worse than any other flu except the 1919 pandemic that killed my young grandmother when my mother was only two years old.” Wrong the 50 and 60 pandemics were worse far worse.
You are clueless Luhman — prior flu epidemic deaths were always grossly overstated. COVID MAY BE TOO. But they are history and COVID is still in progress. It is far too common for people who die with pneumonia to have their deaths blamed on influenza.
I would win money if I bet the interventions saved 0 lives. I would be far closer to reality than the 3 million.
In fact, I would argue the best these interventions could accomplish is to delay or transfer the deaths.
In New York City for example, there is no evidence what-so-ever that the shut down saved any lives or slowed the disease down by any measurable amount.
But there is ample evidence that the lockdowns cost many lives.
What New York State and governing of New York City did not save lives.
An more obvious example is that what the Chinese Govt did,
did not save lives.
What New York State did was quite similar to what Chinese Govt did. Sure, NY didn’t do weld the doors shut thing, didn’t exactly go around and beat people who weren’t wearing masks, and didn’t murder doctors, and many unknown things. But NY did a lot of their own stupid things- there are still in lockdown which somewhat equal to Chinese govt stupid and reckless things.
But I believe the Chinese government committed international war crimes, and the New York government does not seem to have committed international war crimes, so it seem what New York government did was less criminal and evil than CCP. But you should support the New York government. And there might be a number of lawsuits, as consequence.
But some of the measures taken did reduce deaths from the China virus. And
in terms of a number, it seems 3 million lives saved seems about right. Other actions related to it, and one can say the riots are obviously related to it, could cost some high number of deaths.
One say the china virus was going cause over reaction. It caused over reaction in China, and it caused over reaction every where in the world.
And media “over reacted” or the media did bad job, but they managed to make a lot money off it. They continued to be lazy and greedy, and generally acted as spoiled brats. This general behavior over the decades has killed uncountable amounts of people, but during this 1/2 year people it could be assigned a number more than 3 million deaths.
Or the totalitarian state and those that enable it, are causing a lot problems which very harmful to entire world. And it is bad idea to corporation own the media. Corporations are basically mindless and tend to focus on the wrong things and the Media reflects this.
No distancing at least leads to infection.
Last, I wrote about 600 new infections in a german industrial slaughter house. Now, there has over 1,600 tested positive. In that region they restart the lockdown.
A lockdown is a last resort.
A lockdown is like house burning. So sawing thru the roof or lots of things could be plan.
We don’t lockdown at this point in time, and unless there are
unforeseen problems, we need any lockdown anywhere in the world or anytime in the future in regards to China virus.
It’s over. But this virus should addressed as other virus like common flu, or common flu can change and it’s possible the china virus in the coming decades will be worst of all the common flu. Right now it’s bad because of it’s high chance of having an effect upon those over 50 and is worse in terms over 60 and older. It could change and effect younger population and it could cease to exist.
We does appear to be going to “saved” by a vaccine. It’s obviously a very fast spreader, and despite the speed we ramping a vaccine up, it could not be in time, it could not work, it might work to some extent. But china virus or common cold might be solved by vaccine.
So anyways US and Europe should lifted their lockdown more than month ago. Each day it continues, is only due to bad governance.
The lockdown is not choice, it will happen if want to do lockdown or not. The purpose of lockdown is stop chaos- and would get more chaos were one to not decide to lockdown soon enough. New York State failed to lockdown soon enough, they got all the chaos and more due to failure to act soon enough. Fortunately the effect of virus was not as bad as it could been, but NY late lockdown could made it worst. And how they did the lockdown did make it worst. And continue it months longer {after flattening the curve] has made the whole thing worst.
Slave labor conditions, exactly like the US meatpackers. German Labor law will be now modified, and other such “enterprises” with seasonal black-work will come under the hammer.
Of course these “enterprises” have a financial presence and will put politicians in the spotlight – the general welfare or private interest?
Lockdowns kill.
You can’t prove a negative. Or put another way the lockdowns killed 3 million through starvation.
Great work, Nic. I would have to re-tool my statistics to fully understand your argument, but I know the difference between mathematics and bullshit. And your conclusion is, rightly, damning.
By the way, where did you get your data on interventions? I looked at the data from the “Blavatnik School of Government” at Oxford, but their assessments seem to be entirely subjective. Can you direct us to raw data on what measures were enacted, when and where? So we can separate out the effects of one policy over another?
Question: Did someone find a study under wish it is written that the covid-19 virus kills ?
GIGO. Even muggins here knows you shouldn’t use Bayesian for this sort of problem.
“…I have repeatedly criticised this type of Bayesian method in the climate science field, but – probably due to its ease of use – it remains standard practice there and in many other fields.”
Not only is it easy to use, it gives the desired answers. The benefits go on and on.
Is the heard immunity a good thing?
“Scars of Covid-19 could last for life as doctors warn of long-term damage to health
Healthy people, who were in their 40s and 50s when the virus struck, are now facing anxiety, chronic fatigue and disability for years”
Maybe not.
https://www.telegraph.co.uk/news/2020/06/22/revealed-scars-covid-19-could-last-life-doctors-warn-long-term/
Sweden (left France well behind and catching fast with Italy) may be one to tell the final verdict
http://www.vukcevic.co.uk/EuropeCV.htm
No need to look beyond intubation and toxic experimental drugs as causes for that long term damage.
Vuk, the situation is more dire than you describe, as we are not going to be able to isolate for ever. Is that what you are suggesting? Vaccines are roughly 60% effective. The young people will not isolate forever.
The tragic irony almost all of covid deaths and permanent damage is preventable.
The reason why there is a significant portion of the population that is dying and getting very sick from covid….
…is because a significant portion of our population is ‘Vitamin’ D deficient.
It is interesting that ‘Vitamin’ D is a proto hormone that is produced by our body for an important biological reason. The chemical (blood serum 25(OH)D)) Vitamin D changes our body at a cellular level. Vitamin D turns on and off genes which adds biological apparatus to our cells.
Going from ‘Vitamin’ D deficient to ‘Vitamin’ D normal is exactly like getting a cellular upgrade where the Vitamin D upgrade is what evolution (and millions of years) came up with to fight cancer, fight viruses, energize the internal core body, prevent type 1 and 2 diabetes, prevent multiple sclerosis, protect our brains from dementia, and so on.
Regardless of sex or age ‘Vitamin’ D deficient people are 19 times more likely to die or have serious covid symptoms than ‘Vitamin’ D normal people.
This fact plus the fact that 82% of the US/UK black population is Vitamin D deficiency logically explains the why twice as many ‘blacks’ are dying of covid in the UK and the US than white people who are less Vitamin D deficient ….
Coronavirus: Black African deaths three times higher than white Britons – study
https://www.bbc.com/news/uk-52574931
82% of the US black population, 69% of the US Hispanic, and 42% of the US general population is Vitamin D deficient.
Prevalence and correlates of vitamin D deficiency in US adults.
https://tahomaclinic.com/Private/Articles4/WellMan/Forrest%202011%20-%20Prevalence%20and%20correlates%20of%20vitamin%20D%20deficiency%20in%20US%20adults.pdf
4000 UI/day of Vitamin D supplements is required to raise the serum 25(OH)D of the entire population above 30 ng/ml.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3585561
Patterns of COVID-19 Mortality and Vitamin D: An Indonesian Study
Vitamin D Insufficient Patients 12.55 times more likely to die
Vitamin D Deficient Patients 19.12 times more likely to die
For Vitamin D status, cases were classified based on their serum 25(OH)D levels:
(1) normal – serum 25(OH)D of > 30 ng/ml,
(2) insufficient – serum 25(OH)D of 21-29 ng/ml, and
(3) deficient – serum 25(OH)D of < 20 ng/ml.
There is one well known reason why someone with dark/black skin is more Vit D deficient than someone with white skin is because white skin absorbs sunlight easier than dark/black.
Gasp! Are you suggesting that once proto humans – or whoever they self identified as at the time – left Africa the groups who lived in the areas with the least sunlight started to evolve fairer skin in order to process enough Vit D?!
You know, claiming actual rational science to justify evolutionary difference in geographical groups?
Outrageous!
Good thing you are not on Twit, Patrick. Someone might hashtag you!
Can you explain what is untrue about my post given I know many Africans living in sunny Aus who are Vit D deficient?
Vuk
June 23, 2020 at 11:56 am
————-
Vuk, sorry but I think you fail to grasp the meaning of herd immunity.
The Sweden case shows that really there is no difference on how Sweden versus Italy or France approached this “problem”.
Lockdown or not means nothing here, as making any difference.
Also shows a significant problem with the herd immunity in the modern western developed countries.
One main part of herd immunity is the sharing and the prevalence of the best immune response in the population, during the epidemic, or pandemic as in this case.
Sweden got nothing, as it supposed to, in consideration of herd immunity, because already damaged goods as the rest of the club.
Guess which age group has the best immune systems, and produces the best immune responses!
In reality that group is the last group to face the full exposure to highly infection diseases during epidemics or pandemics.
This time around it failed badly (in developed countries), because it suffered considerable fatality prior to the novel disease getting it, as already that group was considerably already a damaged goods.
Besides that, still the overall herd immunity still very good in the case of this novel virus, even with no much support from the third age group, the most important age group for herd immunity.
Hospital fatality and the high severity there not due to any disease directly,
same as in AIDS, where AIDS stands for a syndrome, condition, not a disease.
The disease is HIV-AIDS.
This time around this fatal and highly severe condition, owns properly the term AIDS in a new fashion;
AIIDS, Artificially Induced Immuno-Dificiency Syndrome…a real one.
Hopefully the most of total fatalities under COVID-19, are not even with the virus, or due to the syndrome-condition.
But in the end, if Sweden had a clear and not polluted counting of the fatalities, then it stands to be said that it is very sad to consider the gravity of this condition as it happened.
Herd immunity is a term, a concept describing a natural process or condition, as for the population response to diseases.
It is not a term describing a human invention, or a human intellectual derivative.
And in the consideration of epidemics, especially highly infections diseases, can not be effected in any way by means of quarantining, isolation or lockdowns of any kind, as such can not effect the course and the timing of the epidemic.
Oh, well, you may disagree, but just saying.
Sweden is a very interesting case.
Oh well, if it helps… Japan did not do lockdowns either, as far as I know…and the severity-fatality in consideration of COVID-19 seems very low.
Maybe this case, a good candidate for consideration of herd immunity from the other side of spectrum.
No lockdown, no considerable severity or fatality in record thus far.
cheers
“Japan did not do lockdowns either”
Japan shut down or otherwise limited public gatherings, festivals, etc. My impression is that they closed the bars, restaurants and the big department stores in Tokyo. Sporting events when held at all were held without audiences. They also largely shut down incoming tourist traffic by imposing a 14 day quarantine for most visitors. At one point, when cases started to rise, they declared a state of emergency and asked folks to stay home and avoid crowds — which AFAICS the Japanese, being Japanese, treated as a reasonable request and complied with. They kept the state of emergency in place for a while in the Osaka and Tokyo metro areas as well as the Island of Hokkaido.
And, of course, they postponed the 2020 Olympics.
Overall, whatever they did seems to have worked so far. But it’s unclear that most other countries/regions could emulate them. And they may have just been really lucky. So Far.
Here’s a Guardian article from a month ago https://www.theguardian.com/world/2020/may/22/from-near-disaster-to-success-story-how-japan-has-tackled-coronavirus.
And a more recent article: https://asia.nikkei.com/Spotlight/Coronavirus/Japan-s-coronavirus-response-walks-thin-tightrope-in-foreign-PR-push
I’m sure glad that my oncologist didn’t dive into a model to figure out what testing I needed, and then upon assessing the results, applied tried & tested interventions, rather than ruminating on “what might have been”
Then again, in my case, my bloke was focused on preserving one life, for which he took accountability in applying his considerable expertise.
Self-appointed savers of humanity, on the other hand, claim to deal with millions (billions?) of lives, but take no accountability when applying their dubious expertise.
But they do manage to get heaps of excited headlines and fawning reports from the msm.
Same kind of research and modelling was used to claim dietary saturated fat was primary cause of heart disease. They were wrong and well controlled prospective studies proved it. Similar arguments about global warming – all predictions that have been tested are for the most part wrong. Modelling seems to have replaced real science and no appropriate consequences have been delivered to those who now spend taxpayer resources on this preschool version of science that has no rules.
Modelling is easier than genuine research.
1. You don’t get muddy.
2. You don’t get bitten.
3. You don’t catch something nasty.
4. You get to decide the result before you start.
To reduce mortality the best policy would be to protect the following 3 groups, as virtually all mortality, from what I’ve gathered, appears to have occurred in them:
* Those with pre-existing illness, or poor health in general
* The very elderly in nursing/end-of-life facilities
* Victims of harmful in-hospital treatments, particularly intubation which creates in both healthy and unhealthy people the very disease it is used to treat.
Based on this it is apparent that policy decisions were responsible for turning a generally not dangerous illness into a lethal one for these groups.
Yes, sending people already infected with an illness likely to kill elderly and fragile people and then locking them in care facilities is not not the way to protect people.
Isolating people infected by a virus which either kills in a relatively short time or doesn’t seems a better solution that locking up healthy people for 3 months.
Nic Lewis quote : “School closure is now found to have a slightly stronger effect on transmission than lockdown. This may seem rather unlikely in reality”
In France according to The Institut Pasteur and their study on schoolchildren in Crépy-en-Valois, (one of the principal epicentres in the country back in January) tends to show that children under 10 are less contagious than teenagers and adults.
Most children under 10 years of age have no flu receptors so they cannot either be infected or infect others. The number of under 10-year-olds that have flu receptors is so small that they are four times more likely to be struck by lightning than get the flu. (That’s a 1 in 5.3 million chance.)
Before delving into new questions such as this, mebbe we should complete the unfinished … how many angels really can dance on head of a pin?
That’s 5,000 words I’m not going to read. It doesn’t take a genius to figure out that unless there’s a cure in the way of effective drugs or a vaccine the virus is going to run its course and infect enough people until “herd immunity” is achieved. Back to normal now, or six months from now the virus will be there to say hello.
Steve Case-
Absolutely agree! From the start I have thought that what was needed was to “rip off the band-aid’.
We don’t need a lot of models to show that if the disease is spread by one human contacting another, if you prevent humans from contacting each other, you will SLOW the spread of the disease. But if the concept of “heard immunity” is correct, this doesn’t save any lives (unless the hospitals are overrun). It only prolongs the time people die. Since it is the area under the curve that counts, it doesn’t matter if 10 people a day die for 1000 days, or 100 people a day die for 100 days, the same 10,000 will die.
The only thing that the lockdown insured was that Covid19 will be with us for months, if not years, depending on how much we go back to what was normal human contact before Covid19.
I agree. The costs of the lockdowns may be much greater than the costs of unmodified spread of the virus. Also, when the dialy reports have to talk about ‘cases’ rather than deaths, you know the worst is over until another care home gets infected. As far as I can tell, only one of the hundreds of migrant workers and meat packing plant workers who got infected died so far. If I hear the sanctimonious voice of BC’s chief doc again, I will throw up.
Joy Behar (a TV personality, qualifications unknown) says she and her husband drive around looking for people not wearing masks. {If masks and spreading apart don’t work, why? And why to some places insist on masks and others say ‘why bother’?} [Look in a picture dictionary for Panic2020 and see Joy’s face!]
Schools closed but students log out of on-line learning.
Crowded protests are good. Going fishing with your children is bad.
Politicians and bureaucrats paid, but not working. Many hard working people have income drop to zero, but asked to pay their bills. {Governors and mayors should cut their pay to zero, and contribute all their wealth to those whose livelihoods have been destroyed.}
Good grief talk about someone with no life. She needs to start a band called Behar(d) and the Harangueing Harridans.
What about banning international travelers.
So they used informed priors in the Bayes process and the shear weight of those prior distributions drove the results? What’s wrong with using a non-informative prior and let the data drive the results? Oh, wait! I won’t get the answer I was paid to get.
With an unknown starting point and unknown starting date, we really have no idea of the infection rate of COVID-19. Some have the date around Christmas 2019, some before Thanksgiving, and others have it back in August. This means the period during which SARS-COv2 traveled around the world unopposed could be quite large, meaning it really wasn’t all that infectious.
You put your finger on it: inadequate data fed into theoretical, untested, models by people who have a lot to gain if they are right; and a lot to lose if they are demonstrated to be wrong.
So, they are going to produce results that feed them, even at the cost of self-delusion.
Can we just stop with this bucktooth, hillbilly speculation that the lockdowns were ineffective? Lockdowns/isolation etc have been the standard response to ‘plagues’ for centuries once the mass infection is recognised. We know that certain indigenous populations ( eg in the Americas and Australia) have been decimated by ‘plague’ infections due to lack of ‘herd immunity’ due to hithero isolation.
In Australia we have had effective ‘lockdown’ from very early on and currently have the grand total of 102 deaths out of 25 million+ populations. The media are dining out on ‘outbreaks’/’clusters’ of 5 in family groups and one state (Victoria) having ‘double digit’ new infections daily while the rest of the country has ZERO, yes ZERO.
We are lucky a) in that we have a far less partisan body politic than elsewhere, particularly the USA , b) it is easy for us to shut our borders being a separate island ( same as NZ) with a significant sea gap and c) because we have a low population density so can move about and social distancing is not that big an issue until you go to a supermarket ot a BLM protest. In other words the isolation thing works and we only have a few economist fundamentalists crapping on in our media like some of the people posting here.
Its pretty simple really, lives matter an effing lot more that dollars. Sure the economy keeps the food supply and other essentials going but then again much of western society has been shifting its economy onto the gratuitous consumption of just utterly transient crap, the blong of life. Good riddance to that rubbish, I hope all those businesses go broke. I’m all for genuine quality of life but the issue is whether a camping holiday in a beuatiful location with family and friends is better/worse than the same amount of time spend in bars, strip clubs, casinos and shopping malls.
“Its pretty simple really, lives matter an effing lot more that dollars”
Such a wearisome, ineffectual trope. People don’t have lives for long without “dollars”.
Actually in hard times all they eally need is food and shelter so a bit of helping your neighbour (fellow citizen) might not go astray. “E Pluribus Unum” and all that.
It is one thing to expect your fellow citizen to make his own way in general terms and normal times but quite another to leave them to suffer or die when bad times arrive. That’s not socialism, that’s Christianity last time I checked and probably Islam too for that matter. Why not give/lend them the dollars if needs be? The essence of community is giving and sharing not taking and holding like some Scrooge McTrump.
It seems to me that you have drawn up Plan A and spend all your time boasting about its genius and never bothered with Plan B. That is just empty headed arrogance spitting in the face of fate.
Helping your fellow citizen beats the heck out of letting COVID 19 figuratively kneel on their necks it seems to me.
Christianity and socialism are as far removed from each other as the east is from the west, as the former is a voluntary matter of the heart, whereas the latter is an involuntary matter accomplished only at the barrel of a gun.
Hi Komrade K., – Wishing “… good riddance … hope … go broke …” sounds somewhat mean spirited. Maybe it’s just an eructation of some comrade mind set bravado.
There are now 42 USA non-federal hospitals closing or bankrupt post pandemic lockdown according to the American Hospital Association. Just this past week 13 more USA companies filed for bankruptcy. These had represented jobs for the living & most of those people losing that income aren’t going to be able to take “… a camping holiday in a beautiful location …”
It sounds like you could “lock” yourself and your family down effectively, without impacting others and expecting others to follow your example.
Why don’t you go that direction instead?
The US health system is a godamned joke frankly. Twice as much money per capita is spent on health there as in AUstralia which has a fairly well albeit far from perfect functioning system which only has local pockets of typically specialist greed as distinct from a Wall St style greed culture generally and everybody is entitled to ‘public cover’. Gosh, Australia must be a communist dictatorship…
Sadly, the USA is demonstarting its utter incompetence in delivering sensible, affordable health to its people in much the same way it delivers utterly gratuitous, vicious, murderous policing to certain sectors of its people by promoting the hillbilly fringe to the front line. If you can’t join the KKK then sign up for some tin pot county police force, yeeehaw!
I kinda liked Trump and his drain the swamp thing and throwing brick after brick into the media-Democrat goldfish pond but now we see just what a fly wing plucking loon the guy is. He is your idea of democracy is he, making America a great big joke, again?
You wanna save those health jobs? Give them a government paypacket for the term necessary. You could do so with a short term tax on the greedheaded scum in your pharmaceutical sector.
As for the economy in general, yeah its gonna take a hit so maybe take the time for self reflection and wonder why it was so vulnerable to start with outsourcing all that supply line stuff to China and Mexico so the Wall St pigs can post bigger profits… Forget about Confederate generals and 18th century slave traders, they are born again virgins by comparison, remove the bull and bear statues and just replace it with a pack of wild pigs because they are the real cause of you problems.
The Chinese have the communist loons and you have uber capitalists. Free enterprise is the engine of an economy, a bit of restraint and community interest is the suspension, brakes, seats, seat belts and air bags not to mention the radio, stereo and GPS etc.
Short termism vs Long Termism, speculative punt vs considered investment. Not so bloody smart eh?
Gee that feels gooood. Time for mid morning coffee. :-))
K.K. , – Glad to hear that garbled venting finally gave you strength to consume imported coffee.
What can I say, I’m not perfect but I did have a moment of bliss for a while.. :-))
Why are you drinking coffee?
Via your own argument you should still be in lockdown. Coffee is a luxury good. Luxuries, by your own argument – or at least I think it was an argument, bit hard to tell in the middle… and end… and beginning – are less important than human lives. You should be drinking unfiltered water straight from the tap.
I don’t know, KK. You are all over the place. Maybe you should lay off the non essential imported luxuries and just stay at home eating your word salad.
Ummm, Craig … I bought a large packet of Lavazza grounds at the local supermarket some time ago and have coffee, at home, in the morning, in ‘lockdown’ with my wife and our dogs. As for water I have unfiltered water from our own rain tank ( the local tapwater is crap). I am otherwise happy to share my word salad with you and others and you obviously enjoyed it, look at you all fired up and lively, isn’t that the point of this ‘place’? I mean you don’t come here to put yourself to sleep… do you?
Precisely. Already in 2007 Rep. John Conyers (D-Michigan), sponsor of H.R. 676 “The United States National Health Insurance Act,” held a standing-room only June 20 event in Washington, D.C., with Michael Moore, and clips from his new movie, “Sicko,” on the U.S. health care disaster, to launch a national mobilization for universal health care. A panel presented personal stories of health care horrors. Rep. Darrell Issa (R-CA) was present, who is not a signer of H.R. 676, but wanted to show his concern that Congress must do something. Both Conyers and Moore made the point that 47 million Americans are uninsured, including 8 million children; and another 50 million are underinsured. An estimated 18,000 Americans die each year as a direct consequence of this.
The COVID19 horror stories are yet to surface….
K.K., most of the problems you describe in the USA were/are caused by the radical Democrats.
We hope to fix that this November by voting the radical Democrats out of power.
There is no officially-sponsored racism in the United States.
The Klu Klux Klan (KKK) is a creation of the Democrats, who used them to keep the Blacks in their place, and threatened and even killed them if they expressed support for Republicans, who were trying to free the Blacks.
As for your having a problem with Trump, I can’t imagine what that would be. Trump is a conservative’s dream, so you must not be a conservative.
Komrade Kuma June 23, 2020 at 1:28 pm
“Lockdowns/isolation etc have been the standard response to ‘plagues’ for centuries…”
Interesting comment recommending medieval medical orthodoxy during the 21st century corona virus. Recommend a 24 lecture series filmed in 2016 by Professor Dorsey Armstrong, Ph.D titled THE BLACK DEATH: THE WORLDS MOST DEVASTATING PLAGUE. I found it on Amazon TV.
Did lockdowns really save 3 million COVID-19 deaths, as Flaxman et al. claim?
No.
A good example of Betteridge’s law of headlines
Flaxman? Not Flashman?
I wrote a model that says 6 million lives were saved. Do I win? Oh, and no, you cannot see the source code. It’s my personal property, and you wouldn’t understand it because it’s so advanced. And I’m not sure where I put it. And besides, lockdowns work because lockdowns work – so there.
/sarc
Steve Mosher has highlighted that all lockdowns are not the Sam.
There are not units for lockdowns.
But there are costs for lockdowns.
BENEFIT = lives saved x DALYs per life(mean healthy years left in victim) x GDP per capita
For uk
BENEFIT = 470,000x10x35,000 =165B pounds
COST = 300B pounds
BENEFIT COST RATIO = 0.55
It is my understanding that normal hospital interventions are deemed cost effective if BCR higher than 0.9.
Numbers used above are estimates
Huh. Even looks like one of Mosh’s posts.
Michael
I am not arguing whether the lockdowns are effective or not.
I am an highlighting that they are clearly NOT COST EFFECTIVE.
Based on my simple numbers the UK government government response has costed 135 billion pounds too much.
By comparison the UK government spends 2 billion pounds on ambulance services.
Sorry Waza, but your argument is flawed.
You have failed to allow for the people who will die or have already died because of Lockdown.
Here in Oz some experts (hey, if the Media can use ‘Experts Say’ in arguments than so can I) that ADDITIONAL deaths due to Intentional Self Harm are likely to be 15 times that of total Wuhan Flu deaths in the first year and then continue for up to five years.
Intentional Self Harm is the 12th biggest killer in Australia and one that kills at a significantly younger mean age than all the other top 20 causes.
So your BCR of 0.55 is massively too high.
Using a model based on data from the number of deaths in 11 European countries, it is clear to us that non-pharmaceutical interventions– such as lockdown and school closures, have saved about 3.1 million lives in these countries
Of course it did, Dr. Flaxman, we can see the empty graves /sarc.
How on Earth can someone put out such unproveable nonsense and expect to be paid. If there were a Scientific Hall of Shame, think statistical epidemologists and climate scientists would be holding up the entrance lintel.
Yeah and all that money spent in international diplomacy has saved no lives from wars that never started. Similarly driving sensibly and within speed limits does not save any lives that were never lost and so on. Lets throu out all technical standards, OH&S practices, safety regimes etc etc. Why spend all those money of say airport control towers cos like they don’t save any lives from planes that haven’t crashed either…ya geddit?
How about we completely ignore economists suggestions too, now that make’s sense because boy is that the fountain of all simplistic assumptions where ceteras parabus is the first order of business. In other words assume we are operating in pixie land and base our rubric on that.
The logical argument Flaxman makes is neither proveable nor unproveable because it is untestable, although he does try with a lot of handwaving. The Meso-Americans ensured survival of millions by sacrificing to the Sun.
He does know on which side his bread is buttered, though; every politician around the world will be using the same argument.
When I read statistical evaluations about this WuhanFlu it usually makes me wonder why the populace is factored as a homogenous feature. Meaning: there are usually not identical human elements involved that contribute to the pandemic statistic(s).
For example: Sweden has a well recognized WuhanFlu strategy that has contrasts to many other national tactics. The Swedish data is frequently used for comparison & perspective about pandemic strategy. Yet the Swedish WuhanFlu data has long been known to be skewed by higher infectious rates among resident non-ethnic Swedes.
For any report coming out during the on-going course of this WuhanFlu to give the number of saved lives is an assumption of a homogenous population. Which can not be an accurate extrapolation worldwide.
Just my 3 cents worth (adjusted for inflation):
If you do not know the number of actual cases, you cannot jump to any conclusions.