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Post by terryconroysmagic on Dec 11, 2020 18:30:48 GMT
Hi Paul Is this a rehash of when he said this a couple of months ago or has he recently restated it. If recent, internal power struggle imminent within the WHO Edit: apologies, fat fingers, was trying to quote your previous post
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Post by Paul Spencer on Dec 11, 2020 18:35:44 GMT
Hi Paul Is this a rehash of when he said this a couple of months ago or has he recently restated it. If recent, internal power struggle imminent within the WHO Edit: apologies, fat fingers, was trying to quote your previous post I'm assuming the quotes are taken from that Andrew Neil interview last month, I just wanted to remind people what he said.
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Post by Paul Spencer on Dec 11, 2020 18:38:27 GMT
Stop knicker wetting HB, nothing to see here ... More mass testing for healthy citizens that will result in ever more incorrect positive tests. I wonder what the rate of false positives missing 14 days of school plus isolating your family versus finding an asymptomatic case. Are the parents supposed to leave work everytime a false positive comes in? They'd be better testing the over 65s every day rather than school kids. Lateral flow tests cannot rule out SARS-CoV-2 infection.Tucked into annex B of a government guide to community testing is the statement: “In the field evaluation in Liverpool, compared to PCR tests, these tests picked up 5 out of 10 of the cases PCR detected and more than 7 out of 10 cases with higher viral loads, who are likely to be the most infectious.” If a test misses 50% of infections, people with a negative result are not in the clear—their chances of active infection are simply half what they were before the test. Nobody can be considered free of risk of being infected or transmitting infection. Failing to identify 30% of people with high viral loads is six times worse than the almost 5% missed in the Porton Down/Oxford evaluation, and of particular concern. Allowing half of infected people, and one third of those with high viral loads, to unwittingly take the virus into hospitals, family homes, and care homes will not reduce the spread of the infection and could put lives at risk. Diligent maintenance of social distancing, personal protection, and other infection prevention control measures remains vital for people with a negative result. Uncertainties remain about who is actually infectious. “High viral load” has wrongly become synonymous with “infectious,” with tests being described equally wrongly as tests of infectiousness. Both scientists and politicians have used this wording, with the prime minister stating that lateral flow tests would “identify people who are infectious … allowing those who are not infectious to continue as normal.” Whatever decision making process the UK government used, it ignored key evidence and dismissed expert international advice. The result is a considerable burden on care home staff, universities, NHS staff, public health teams, and schools, with minimal additional safety compared with existing risk mitigation measures. Asymptomatic lateral flow testing is an unhelpful diversion from the important task of vaccination rollout. www.bmj.com/content/371/bmj.m4787And tucked away on the Government's own website: www.gov.uk/government/publications/innova-lateral-flow-sars-cov-2-antigen-test-accuracy-in-liverpool-pilot-preliminary-data-26-november-2020The Liverpoool Health Protection Board decided yesterday to pause plans to use Innova Later Flow SARS-COV-2 Antigen tests (LFT) in test-to-enable visitor access to care home settings due to the accuracy statistics presented below ...
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Post by andystokey on Dec 11, 2020 19:01:58 GMT
That's a reasonable response but to a slightly different point than I was making. I think we agree in 90%. My response was a reaction to the thread where Benji suggested that yesterday's 20k daily cases meant we jump into lockdown quick smart. Paul pointed out that there was no need to do so based on a richer interpretation of the data, exactly what you are advocating and I support. Unfortunately as I said in my opening sentence "We don't seem to get our narrative away from cases as the driver of policy". That's a 'we' collective not a them singular. The BBC and other MSM outlets insist, wrongly in my view, of flashing up a graphic every night labelled new cases. Every day at 4pm crouchy sticks up the graphic on here. Cases and Deaths. Both numbers have no narrative as you suggest they should, and I agree, for most people that remain poorly informed they see these two numbers and conflate them. The positive results aren't cases and the deaths aren't Covid deaths. On your other points as I've said before the only way to navigate through erroneous data is debate, but that is being narrowed every day. Interesting this lunchtime the BBC gave a brilliant graphic in support of vaccines that said every day this many people have a stroke after the vaccine they will still have a stroke so we cant interpret that as a risk in vaccine take up. Sensible and very important. ...here www.bbc.co.uk/news/health-55216047. Now compare the pro vaccine statement I highlight below with the case rate narrative and tell me that MSM is doing the same. I haven't seen a lucid piece on this on BBC with such enthusiasm as this one. "But the truth is that people get sick all the time. Every five minutes in the UK one person has a heart attack and one person has a stroke. More than 600,000 people die each year.
There will be cases where somebody has a jab one day and then, shortly after, has a serious health problem that would have happened whether they were jabbed or not.
"We could see things that happen by unhappy chance," cautions Dr Ward.That sentence in bold could be used verbatim about *cases [*rather tests] Let's try it for size. There will be cases where somebody has a positive test one day and then, shortly after, has a serious health problem that would have happened whether they were tested or not.
We could see things that happen by unhappy chance.I'm not following your logic here. In the case of the jab the point being made is that there isn't necessarily a causal link between the jab and a subsequent death. It is therefore not logically correct to claim the jab is dangerous. Are you trying to make the same sort of claim in relation to testing as a basis for public health policy? If so I don't think it makes sense. It is true that somebody would become ill from covid regardless of whether they had a test or not. It is also true that some people will become ill from covid even if they had a false negative and some people will not become ill even though they had a real positive. But that does not make a case for basing public health policy on people falling ill rather than on tests. There is no 100% causal link between a test result and a subsequent illness. But that isn't a problem. There is a significant correlation between the number of people infected (which tests give a rough estimate for) and the numbers of people likely to fall ill. The more people infected, the more people who will become ill. The fact that it is impossible to say for certain that x number of positive test means y people are infected which will result in z number becoming ill does not matter for the purposes of setting policy. If you have a reasonable measure of accuracy you can say that xish number of positive tests will result in yish people being actually infected which will result in zish number of people becoming ill - and that level of accuracy is perfectly good enough to make policy. It's true that the number of people actually falling ill is far more accurate than any estimates of number of people infected but that doesn't mean it is a better measure for determining public health policy. It's the equivalent of saying we'll see how many houses burn down before we decide if we need to build a fire station because we can't possibly accurately predict the number of fires and we can count burning houses. The way I visualise managing the pandemic is it's like trying to keep a massive iceberg underwater where the size of the iceberg is the equivalent of the number of infections. Public policy based on the use of tests is the equivalent of keeping tabs on the size of the iceberg, maintaining pressure to keep it below the surface and not letting it get too big. Basing policy on the number of people falling ill is the equivalent of easing off and waiting until the iceberg breaks through the surface of the water and measuring it's height - which is clearly far easier. The problem is that if you've let that happen the iceberg is already massive and rapidly increasing in size and before you know it that little bit of ice poking through the water is meters high and rising out of control. As well dealing with all the sinking ships you've got a massive problem in shrinking the iceberg back to a manageable level. Have you turned into JVT? 😎 I leave the fire stations and icebergs a moment. Apologies for the long reply but I'd like this to be as clear as I can. Simply put in March we only tested symptomatic people. Now we have mass testing. I think we can both agree therefore that case data correlation was different in April through the summer and is no largely irrelevant the two data sets are incompatible as are the rough ratios you elude to. If we can I will put that aside and talk only about August onwards. No one in the world defines a "case" the same way so problem is you cant compare world country data. Yet CMO Chris Whitty on 9th September described his worry about the increase in cases and compared the situation in the UK to other countries. This was not really possible was it because the case data isn't comparable. Let's go with your view it's near enough. Given I dispute that however because; ‘If a person has both a negative and a positive test, then only their positive test will be counted. If a person is tested as positive under both pillar 1 and pillar 2, then only the first positive case is counted.’ An asymptomatic person who tested positive could have two confirmatory negative tests, but would still count as a confirmed case. www.spectator.co.uk/article/what-does-a-case-of-covid-19-really-meanSo I think therefore we can both agree that case data is worse than the narrow error you might imply. So I dont buy the accuracy figure. Anyway let's go with your theory that "If you have a reasonable measure of accuracy you can say that xish number of positive tests will result in yish people being actually infected which will result in zish number of people becoming illMy very simple point is that having the "cases" which aren't accurate on the TV every night is suggesting a level of both precision and accuracy that is not possible. The lay person like Benji and others look at this number like it's a gospel truth which we both agree it isn't. So let's imagine as you suggest that the government have a magic model which takes all these variables into account. You put suspected cases in the front end and you get rough idea of people that might end up in a high dependency bed or even die and let's imagine that ratio is about 1 in 1000. That still leaves loads of people who are told to go home and take a paracetamol. Well we also know that 1:1000 that can't be true for everbody because of age effects so the model has to put the age data of the cases in too. Furthermore it needs to account for number of contacts, family size, urban environments and rural environments. So that variable needs to go into the model. Coming up with a unified model for the whole of the UK is a complex and potentially impossible model. One thing for sure you cant intuitively do it as a rule of thumb. A member of the public can't look at 20K yesterday and compare it with 16k the day before. At an extreme if 20k were all under 30 or 16k were all over 80 you have a huge outcome difference. You could maybe make a further assumption that the age population was average each day but that would be an approximation too. You could look at the rolling seven day average and look for inflections in the curve both up and down but here is the rub. Every single chart I've seen shows no consistent cause-effect correlation for any public measure. In fact the hysteresis in the system and the built in lag makes it impossible. Paul consistently shows charts that contradict the policy effects in each community. The government's own prediction models have never as yet predicted anything, in fact they have been shown to be wildly innacurate by orders of magnitude. Lockdown 2, pubs, all these policy desicions have done nothing notable on the case chart. So I don't believe that they have this magic box else I'd keep schtum So in summary telling me, Benji, or Paul the case data every night on TV is a complete waste of time resulting in terribly polarised arguments about the data. What an engineer would do immediately would be to adopt a much simpler measure which is unfortunately at the pointy end. You have to be closer to the effect you want to change. If the acute hospital bed data per region was on TV every night and the government and the population had a KPI to keep it to within say 10% of total capacity otherwise a restriction happens makes more sense, behaviour would be better. So in support of your argument and in the spirit if JVT the policy should be like a heating controller. A good design has the following attributes. 1. It uses its control measure based on what it is meant to control ie Temperature. (In the case of Covid, illness) 2. It needs to sample the measure as fast as practical. (Choose your illness measure, hospital referral maybe) 3. The control has the ability to turn on and off at speed on set limits. (ie the government tells us what happens in the upper and lower bound of the limits and acts immediately) Point #3 encourage businesses and every member of the public to see what is coming before the government officially tells us. We aren't in the business with this pandemic of controlling cases no one should care. We should be preventing death and illness and that is what should be used. The lag you refer to is irrelevant if the system is designed well. Your boiler can't heat the radiators instantaneously and neither can the countermeasures the government put in place. So... Get the case stats off TV now!! and replace them with something meaningful. The old adage says "you get what you measure" so measure illness and it will come down and policy will be targeted to that.
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Post by crouchpotato1 on Dec 11, 2020 19:08:59 GMT
Cases up again🤔
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Post by The Drunken Communist on Dec 11, 2020 19:35:48 GMT
Andy writes all that & is instantly followed by Mutters with that
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Post by andystokey on Dec 11, 2020 19:36:08 GMT
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Post by crouchpotato1 on Dec 11, 2020 19:47:45 GMT
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Post by crouchpotato1 on Dec 11, 2020 19:49:43 GMT
Not where I live it isn’t 🤔
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Post by starkiller on Dec 11, 2020 19:49:58 GMT
Because firstly a positive test isn't a good chance to have Covid-19 It's not even close to saying you do, you might possibly, so take another test. But we don't we isolate and wait and just disappear into the stats. They can do this time and again. We have no idea how many individuals fall into that category. After all the other ifs and maybes in the paragraph above it's approx 0.008% that you reach the end of your point and die. There is no correlation other than perhaps to say some people who might have died of Covid-19 also had a positive test within 28 days. That's your 500 people. There isn't even an agreed pathology for the deaths. Since every day the test quantity and death figures are assumed to be in tune. The ratio and quantity and implementation of PCR to LFT results in huge variables. To put it a different way 99.992% ish plus a margin of error of people who have had a positive test will not die of Covid-19.So basing public policy on positive tests per population is misleading. Edit: and if they now start testing kids at school your TV will report an increase in positive test results, none of which will die. Well let's see shall we. Positive tests are rising in London right now. So let's agree to take a look between us in 6 weeks time towards the end of January and see if the capital is serving up more dead bodies. It will be as sure as night follows day. Just as the sharp rise in September and October in positive tests has seen the body count rise in November and December from single figures to nearly 500 a day. To suggest anything else is a nonsense. I'm having to pinch myself that I've even got engaged in this discussion. People on their last legs in hospital for something else. Tested multiple times until a 'positive' is secured. And then, just like magic, another covid death comes along for the death graph enthusiasts.
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Post by Paul Spencer on Dec 11, 2020 19:50:05 GMT
Andy writes all that & is instantly followed by Mutters with that I've just opened the thread and nearly fell off my chair! 😁
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Post by crouchpotato1 on Dec 11, 2020 19:52:59 GMT
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Post by Paul Spencer on Dec 11, 2020 19:59:33 GMT
I'm not following your logic here. In the case of the jab the point being made is that there isn't necessarily a causal link between the jab and a subsequent death. It is therefore not logically correct to claim the jab is dangerous. Are you trying to make the same sort of claim in relation to testing as a basis for public health policy? If so I don't think it makes sense. It is true that somebody would become ill from covid regardless of whether they had a test or not. It is also true that some people will become ill from covid even if they had a false negative and some people will not become ill even though they had a real positive. But that does not make a case for basing public health policy on people falling ill rather than on tests. There is no 100% causal link between a test result and a subsequent illness. But that isn't a problem. There is a significant correlation between the number of people infected (which tests give a rough estimate for) and the numbers of people likely to fall ill. The more people infected, the more people who will become ill. The fact that it is impossible to say for certain that x number of positive test means y people are infected which will result in z number becoming ill does not matter for the purposes of setting policy. If you have a reasonable measure of accuracy you can say that xish number of positive tests will result in yish people being actually infected which will result in zish number of people becoming ill - and that level of accuracy is perfectly good enough to make policy. It's true that the number of people actually falling ill is far more accurate than any estimates of number of people infected but that doesn't mean it is a better measure for determining public health policy. It's the equivalent of saying we'll see how many houses burn down before we decide if we need to build a fire station because we can't possibly accurately predict the number of fires and we can count burning houses. The way I visualise managing the pandemic is it's like trying to keep a massive iceberg underwater where the size of the iceberg is the equivalent of the number of infections. Public policy based on the use of tests is the equivalent of keeping tabs on the size of the iceberg, maintaining pressure to keep it below the surface and not letting it get too big. Basing policy on the number of people falling ill is the equivalent of easing off and waiting until the iceberg breaks through the surface of the water and measuring it's height - which is clearly far easier. The problem is that if you've let that happen the iceberg is already massive and rapidly increasing in size and before you know it that little bit of ice poking through the water is meters high and rising out of control. As well dealing with all the sinking ships you've got a massive problem in shrinking the iceberg back to a manageable level. Have you turned into JVT? 😎 I leave the fire stations and icebergs a moment. Apologies for the long reply but I'd like this to be as clear as I can. Simply put in March we only tested symptomatic people. Now we have mass testing. I think we can both agree therefore that case data correlation was different in April through the summer and is no largely irrelevant the two data sets are incompatible as are the rough ratios you elude to. If we can I will put that aside and talk only about August onwards. No one in the world defines a "case" the same way so problem is you cant compare world country data. Yet CMO Chris Whitty on 9th September described his worry about the increase in cases and compared the situation in the UK to other countries. This was not really possible was it because the case data isn't comparable. Let's go with your view it's near enough. Given I dispute that however because; ‘If a person has both a negative and a positive test, then only their positive test will be counted. If a person is tested as positive under both pillar 1 and pillar 2, then only the first positive case is counted.’ An asymptomatic person who tested positive could have two confirmatory negative tests, but would still count as a confirmed case. www.spectator.co.uk/article/what-does-a-case-of-covid-19-really-meanSo I think therefore we can both agree that case data is worse than the narrow error you might imply. So I dont buy the accuracy figure. Anyway let's go with your theory that "If you have a reasonable measure of accuracy you can say that xish number of positive tests will result in yish people being actually infected which will result in zish number of people becoming illMy very simple point is that having the "cases" which aren't accurate on the TV every night is suggesting a level of both precision and accuracy that is not possible. The lay person like Benji and others look at this number like it's a gospel truth which we both agree it isn't. So let's imagine as you suggest that the government have a magic model which takes all these variables into account. You put suspected cases in the front end and you get rough idea of people that might end up in a high dependency bed or even die and let's imagine that ratio is about 1 in 1000. That still leaves loads of people who are told to go home and take a paracetamol. Well we also know that 1:1000 that can't be true for everbody because of age effects so the model has to put the age data of the cases in too. Furthermore it needs to account for number of contacts, family size, urban environments and rural environments. So that variable needs to go into the model. Coming up with a unified model for the whole of the UK is a complex and potentially impossible model. One thing for sure you cant intuitively do it as a rule of thumb. A member of the public can't look at 20K yesterday and compare it with 16k the day before. At an extreme if 20k were all under 30 or 16k were all over 80 you have a huge outcome difference. You could maybe make a further assumption that the age population was average each day but that would be an approximation too. You could look at the rolling seven day average and look for inflections in the curve both up and down but here is the rub. Every single chart I've seen shows no consistent cause-effect correlation for any public measure. In fact the hysteresis in the system and the built in lag makes it impossible. Paul consistently shows charts that contradict the policy effects in each community. The government's own prediction models have never as yet predicted anything, in fact they have been shown to be wildly innacurate by orders of magnitude. Lockdown 2, pubs, all these policy desicions have done nothing notable on the case chart. So I don't believe that they have this magic box else I'd keep schtum So in summary telling me, Benji, or Paul the case data every night on TV is a complete waste of time resulting in terribly polarised arguments about the data. What an engineer would do immediately would be to adopt a much simpler measure which is unfortunately at the pointy end. You have to be closer to the effect you want to change. If the acute hospital bed data per region was on TV every night and the government and the population had a KPI to keep it to within say 10% of total capacity otherwise a restriction happens makes more sense, behaviour would be better. So in support of your argument and in the spirit if JVT the policy should be like a heating controller. A good design has the following attributes. 1. It uses its control measure based on what it is meant to control ie Temperature. (In the case of Covid, illness) 2. It needs to sample the measure as fast as practical. (Choose your illness measure, hospital referral maybe) 3. The control has the ability to turn on and off at speed on set limits. (ie the government tells us what happens in the upper and lower bound of the limits and acts immediately) Point #3 encourage businesses and every member of the public to see what is coming before the government officially tells us. We aren't in the business with this pandemic of controlling cases no one should care. We should be preventing death and illness and that is what should be used. The lag you refer to is irrelevant if the system is designed well. Your boiler can't heat the radiators instantaneously and neither can the countermeasures the government put in place. So... Get the case stats off TV now!! and replace them with something meaningful. The old adage says "you get what you measure" so measure illness and it will come down and policy will be targeted to that. Excellent post Andy, hopefully this graph illustrates exactly what you're on about, positive 'cases' have become essentially meaningless ...
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Post by Paul Spencer on Dec 11, 2020 20:14:19 GMT
Well let's see shall we. Positive tests are rising in London right now. So let's agree to take a look between us in 6 weeks time towards the end of January and see if the capital is serving up more dead bodies. It will be as sure as night follows day. Just as the sharp rise in September and October in positive tests has seen the body count rise in November and December from single figures to nearly 500 a day. To suggest anything else is a nonsense. I'm having to pinch myself that I've even got engaged in this discussion. People on their last legs in hospital for something else. Tested multiple times until a 'positive' is secured. And then, just like magic, another covid death comes along for the death graph enthusiasts.
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Post by andystokey on Dec 11, 2020 20:19:00 GMT
Have you turned into JVT? 😎 I leave the fire stations and icebergs a moment. Apologies for the long reply but I'd like this to be as clear as I can. Simply put in March we only tested symptomatic people. Now we have mass testing. I think we can both agree therefore that case data correlation was different in April through the summer and is no largely irrelevant the two data sets are incompatible as are the rough ratios you elude to. If we can I will put that aside and talk only about August onwards. No one in the world defines a "case" the same way so problem is you cant compare world country data. Yet CMO Chris Whitty on 9th September described his worry about the increase in cases and compared the situation in the UK to other countries. This was not really possible was it because the case data isn't comparable. Let's go with your view it's near enough. Given I dispute that however because; ‘If a person has both a negative and a positive test, then only their positive test will be counted. If a person is tested as positive under both pillar 1 and pillar 2, then only the first positive case is counted.’ An asymptomatic person who tested positive could have two confirmatory negative tests, but would still count as a confirmed case. www.spectator.co.uk/article/what-does-a-case-of-covid-19-really-meanSo I think therefore we can both agree that case data is worse than the narrow error you might imply. So I dont buy the accuracy figure. Anyway let's go with your theory that "If you have a reasonable measure of accuracy you can say that xish number of positive tests will result in yish people being actually infected which will result in zish number of people becoming illMy very simple point is that having the "cases" which aren't accurate on the TV every night is suggesting a level of both precision and accuracy that is not possible. The lay person like Benji and others look at this number like it's a gospel truth which we both agree it isn't. So let's imagine as you suggest that the government have a magic model which takes all these variables into account. You put suspected cases in the front end and you get rough idea of people that might end up in a high dependency bed or even die and let's imagine that ratio is about 1 in 1000. That still leaves loads of people who are told to go home and take a paracetamol. Well we also know that 1:1000 that can't be true for everbody because of age effects so the model has to put the age data of the cases in too. Furthermore it needs to account for number of contacts, family size, urban environments and rural environments. So that variable needs to go into the model. Coming up with a unified model for the whole of the UK is a complex and potentially impossible model. One thing for sure you cant intuitively do it as a rule of thumb. A member of the public can't look at 20K yesterday and compare it with 16k the day before. At an extreme if 20k were all under 30 or 16k were all over 80 you have a huge outcome difference. You could maybe make a further assumption that the age population was average each day but that would be an approximation too. You could look at the rolling seven day average and look for inflections in the curve both up and down but here is the rub. Every single chart I've seen shows no consistent cause-effect correlation for any public measure. In fact the hysteresis in the system and the built in lag makes it impossible. Paul consistently shows charts that contradict the policy effects in each community. The government's own prediction models have never as yet predicted anything, in fact they have been shown to be wildly innacurate by orders of magnitude. Lockdown 2, pubs, all these policy desicions have done nothing notable on the case chart. So I don't believe that they have this magic box else I'd keep schtum So in summary telling me, Benji, or Paul the case data every night on TV is a complete waste of time resulting in terribly polarised arguments about the data. What an engineer would do immediately would be to adopt a much simpler measure which is unfortunately at the pointy end. You have to be closer to the effect you want to change. If the acute hospital bed data per region was on TV every night and the government and the population had a KPI to keep it to within say 10% of total capacity otherwise a restriction happens makes more sense, behaviour would be better. So in support of your argument and in the spirit if JVT the policy should be like a heating controller. A good design has the following attributes. 1. It uses its control measure based on what it is meant to control ie Temperature. (In the case of Covid, illness) 2. It needs to sample the measure as fast as practical. (Choose your illness measure, hospital referral maybe) 3. The control has the ability to turn on and off at speed on set limits. (ie the government tells us what happens in the upper and lower bound of the limits and acts immediately) Point #3 encourage businesses and every member of the public to see what is coming before the government officially tells us. We aren't in the business with this pandemic of controlling cases no one should care. We should be preventing death and illness and that is what should be used. The lag you refer to is irrelevant if the system is designed well. Your boiler can't heat the radiators instantaneously and neither can the countermeasures the government put in place. So... Get the case stats off TV now!! and replace them with something meaningful. The old adage says "you get what you measure" so measure illness and it will come down and policy will be targeted to that. Excellent post Andy, hopefully this graph illustrates exactly what you're on about ... There is some really great data in here for stuff like % of true symptomatic cases. Example who feel ill out of over 600,000 about 3.4% and hospital beds occupied by region and age betweenll 9-16%. Majority of the current symptomatic covid cases are between 10 and 49 yo. Download the pdf bottom of the page of the report sent to government each day. covid.joinzoe.com/your-contribution
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Post by Paul Spencer on Dec 11, 2020 20:40:58 GMT
Well the replies (not surprisingly) didn't go as expected ...
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Post by Gods on Dec 11, 2020 21:31:11 GMT
Well let's see shall we. Positive tests are rising in London right now. So let's agree to take a look between us in 6 weeks time towards the end of January and see if the capital is serving up more dead bodies. It will be as sure as night follows day. Just as the sharp rise in September and October in positive tests has seen the body count rise in November and December from single figures to nearly 500 a day. To suggest anything else is a nonsense. I'm having to pinch myself that I've even got engaged in this discussion. What do you make of the CDC report that I posted yesterday which states that just 6% of Covid-related deaths in the USA mention only Covid on death certificates? The rest of the deaths involved nearly three more causes of deaths or conditions. I think it's safe to assume that the same is happening in the UK. You seem to be under the impression that the people who are passing away (or stiffs as you so eloquently put it the other week) are perfect specimens of health cut down in the prime of life by this deadly disease which every single person in the world is susceptible to. Oh, and it's not 500 deaths a day. oatcakefanzine.proboards.com/post/6995639I'm looking at a Covid map of California in the Sunday Times. For each Region it has in Blue 'Total Cases' and in Red 'Total Deaths' So you have for example: Los Angeles: 421,881 Cases, 7,782 Deaths Orange: 81,653 Cases, 1,586 Deaths ... and so on. The ratio of Cases to Deaths varies somewhat from Region to Region but the unescapable fact is the more positive cases you have the more deaths you have and the fewer positive cases you have the fewer deaths you have. Caifornia is no different to anywhere else. The connection between the two is unequivocable and undeniable. For anyone to pretend otherwise is laughable.
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Post by mtrstudent on Dec 11, 2020 21:47:28 GMT
Arizona cases started going up last month. Now my gf's hospital is rammed again.
Normally if you need oxygen at all you'll go to the ICU but even the extra emergency ICUs aren't enough, so they're sending people on BiPAP (oxygen mask, no tube) to normal wards instead. They've cancelled other treatments and are triaging patients, rejecting a lot who'd normally get care.
Preparing to start "crisis" procedures within weeks, which would cause massive fuckups and deaths for non-COVID patients.
Gotta hope it levels off and the vaccine turns up asap.
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Post by Olgrligm on Dec 11, 2020 22:38:56 GMT
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Post by bayernoatcake on Dec 12, 2020 0:38:25 GMT
Andy writes all that & is instantly followed by Mutters with that It is utter fucking nonsense tbf.
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Post by bayernoatcake on Dec 12, 2020 0:39:52 GMT
What do you make of the CDC report that I posted yesterday which states that just 6% of Covid-related deaths in the USA mention only Covid on death certificates? The rest of the deaths involved nearly three more causes of deaths or conditions. I think it's safe to assume that the same is happening in the UK. You seem to be under the impression that the people who are passing away (or stiffs as you so eloquently put it the other week) are perfect specimens of health cut down in the prime of life by this deadly disease which every single person in the world is susceptible to. Oh, and it's not 500 deaths a day. oatcakefanzine.proboards.com/post/6995639I'm looking at a Covid map of California in the Sunday Times. For each Region it has in Blue 'Total Cases' and in Red 'Total Deaths' So you have for example: Los Angeles: 421,881 Cases, 7,782 Deaths Orange: 81,653 Cases, 1,586 Deaths ... and so on. The ratio of Cases to Deaths varies somewhat from Region to Region but the unescapable fact is the more positive cases you have the more deaths you have and the fewer positive cases you have the fewer deaths you have. Caifornia is no different to anywhere else. The connection between the two is unequivocable and undeniable. For anyone to pretend otherwise is laughable. It’s very odd. Tinfoil hat time again. You’d think after the Casedemic horseshit spouted in September people would learn 🤦♂️
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Post by Gods on Dec 12, 2020 10:24:11 GMT
I'm looking at a Covid map of California in the Sunday Times. For each Region it has in Blue 'Total Cases' and in Red 'Total Deaths' So you have for example: Los Angeles: 421,881 Cases, 7,782 Deaths Orange: 81,653 Cases, 1,586 Deaths ... and so on. The ratio of Cases to Deaths varies somewhat from Region to Region but the unescapable fact is the more positive cases you have the more deaths you have and the fewer positive cases you have the fewer deaths you have. Caifornia is no different to anywhere else. The connection between the two is unequivocable and undeniable. For anyone to pretend otherwise is laughable. It’s very odd. Tinfoil hat time again. You’d think after the Casedemic horseshit spouted in September people would learn 🤦♂️ Here is the Godfather of the 'Casedemic' telling us all everything is fine and dandy from back in August. 20,000 wave 2 deaths later... Carl Heneghan @carlheneghan · 1 Aug COVID-19: Death Data in England – Daily Update cebm.net/covid-19/covid-19-death-data-in-england-daily-update/ 4 deaths announced in hospitals in England - At some point we’ll focus on what is happening as opposed to what we think might happen next
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Post by CBUFAWKIPWH on Dec 12, 2020 10:40:10 GMT
Have you turned into JVT? 😎 I leave the fire stations and icebergs a moment. Apologies for the long reply but I'd like this to be as clear as I can. Simply put in March we only tested symptomatic people. Now we have mass testing. I think we can both agree therefore that case data correlation was different in April through the summer and is no largely irrelevant the two data sets are incompatible as are the rough ratios you elude to. If we can I will put that aside and talk only about August onwards. No one in the world defines a "case" the same way so problem is you cant compare world country data. Yet CMO Chris Whitty on 9th September described his worry about the increase in cases and compared the situation in the UK to other countries. This was not really possible was it because the case data isn't comparable. Let's go with your view it's near enough. Given I dispute that however because; ‘If a person has both a negative and a positive test, then only their positive test will be counted. If a person is tested as positive under both pillar 1 and pillar 2, then only the first positive case is counted.’ An asymptomatic person who tested positive could have two confirmatory negative tests, but would still count as a confirmed case. www.spectator.co.uk/article/what-does-a-case-of-covid-19-really-meanSo I think therefore we can both agree that case data is worse than the narrow error you might imply. So I dont buy the accuracy figure. Anyway let's go with your theory that "If you have a reasonable measure of accuracy you can say that xish number of positive tests will result in yish people being actually infected which will result in zish number of people becoming illMy very simple point is that having the "cases" which aren't accurate on the TV every night is suggesting a level of both precision and accuracy that is not possible. The lay person like Benji and others look at this number like it's a gospel truth which we both agree it isn't. So let's imagine as you suggest that the government have a magic model which takes all these variables into account. You put suspected cases in the front end and you get rough idea of people that might end up in a high dependency bed or even die and let's imagine that ratio is about 1 in 1000. That still leaves loads of people who are told to go home and take a paracetamol. Well we also know that 1:1000 that can't be true for everbody because of age effects so the model has to put the age data of the cases in too. Furthermore it needs to account for number of contacts, family size, urban environments and rural environments. So that variable needs to go into the model. Coming up with a unified model for the whole of the UK is a complex and potentially impossible model. One thing for sure you cant intuitively do it as a rule of thumb. A member of the public can't look at 20K yesterday and compare it with 16k the day before. At an extreme if 20k were all under 30 or 16k were all over 80 you have a huge outcome difference. You could maybe make a further assumption that the age population was average each day but that would be an approximation too. You could look at the rolling seven day average and look for inflections in the curve both up and down but here is the rub. Every single chart I've seen shows no consistent cause-effect correlation for any public measure. In fact the hysteresis in the system and the built in lag makes it impossible. Paul consistently shows charts that contradict the policy effects in each community. The government's own prediction models have never as yet predicted anything, in fact they have been shown to be wildly innacurate by orders of magnitude. Lockdown 2, pubs, all these policy desicions have done nothing notable on the case chart. So I don't believe that they have this magic box else I'd keep schtum So in summary telling me, Benji, or Paul the case data every night on TV is a complete waste of time resulting in terribly polarised arguments about the data. What an engineer would do immediately would be to adopt a much simpler measure which is unfortunately at the pointy end. You have to be closer to the effect you want to change. If the acute hospital bed data per region was on TV every night and the government and the population had a KPI to keep it to within say 10% of total capacity otherwise a restriction happens makes more sense, behaviour would be better. So in support of your argument and in the spirit if JVT the policy should be like a heating controller. A good design has the following attributes. 1. It uses its control measure based on what it is meant to control ie Temperature. (In the case of Covid, illness) 2. It needs to sample the measure as fast as practical. (Choose your illness measure, hospital referral maybe) 3. The control has the ability to turn on and off at speed on set limits. (ie the government tells us what happens in the upper and lower bound of the limits and acts immediately) Point #3 encourage businesses and every member of the public to see what is coming before the government officially tells us. We aren't in the business with this pandemic of controlling cases no one should care. We should be preventing death and illness and that is what should be used. The lag you refer to is irrelevant if the system is designed well. Your boiler can't heat the radiators instantaneously and neither can the countermeasures the government put in place. So... Get the case stats off TV now!! and replace them with something meaningful. The old adage says "you get what you measure" so measure illness and it will come down and policy will be targeted to that. Excellent post Andy, hopefully this graph illustrates exactly what you're on about, positive 'cases' have become essentially meaningless ... I assume the cases figures are based on actual tests - in which case the graph is pretty meaningless in absolute terms as the mount is far higher now than it was back in March - had there been the same level of testing (which the government abandoned because by then managing infections wasn't an option) then the number of cases would have been far higher than now. Setting aside the scale what the graph does show is a correlation between number of cases/infections and deaths - but to make sense of the figures you have to factor in the massive increase in the number of tests. It is naive to think that policy makers are assuming like for like over that time period. In modelling outcomes they will be far more nuanced than just projecting that graph. I agree the figures presented could well be causing confusion but to be fair the actual analysis going on behind the scenes is far too complex to present in a news briefing and would no doubt go over most people's heads (including mine). The JVT approach of using straightforward analogies might well be better. I fundamentally do not agree that inevitable inaccuracies in test data should be used as an excuse to abandon a public policy approach based on managing infections - that is putting the purity of numbers above the primary aim of saving lives. If the government were to revert back to managing hospital admissions and deaths (as they were back in March) there will be far more deaths. The only reason the government took this approach in the first place was because they had lost control of infections. It wasn't as if they they thought this was a better approach - they simply had no choice. In effect you are asking the government to allow a crisis to happen and then manage it rather than try to prevent the crisis from happening in the first place. Every successful country has based there approach on managing infections - and as well as testing the very best have had effective tracing in pace as well which is where we've failed abysmally. The only countries that are using an admissions approach are those that don't have the infrastructure, have gone for herd immunity or have lost control of the situation. Andy - to reverse your heating analogy in effect you are suggesting we stop measuring the outside temperature because it's difficult and prone to inaccuracies and use the far more accurate thermometers on cooling systems to keep people cool while the earth warms up rather than use the imperfect data and associated climate models to address global warming.
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Post by duckling on Dec 12, 2020 10:42:43 GMT
I'm pissed off.
The United States just approved the vaccine.
I am in my 30s and have an autoimmune disease. I'll have to speak with my immunologist about whether it's safe to get vaccinated.
If it is safe, I'll only be in the 10th group on the list, behind healthy 65+ and ahead of healthy 55-64.
Among the people ahead of me are people with >2 comorbidities. On the list of comorbidities are obesity and smoking. Also ahead of me are people in communal living areas, including prisoners.
So if I want to get the vaccine earlier, I should get really fat, start smoking, and commit a crime.
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Post by thehartshillbadger on Dec 12, 2020 11:38:56 GMT
I'm pissed off. The United States just approved the vaccine. I am in my 30s and have an autoimmune disease. I'll have to speak with my immunologist about whether it's safe to get vaccinated. If it is safe, I'll only be in the 10th group on the list, behind healthy 65+ and ahead of healthy 55-64. Among the people ahead of me are people with >2 comorbidities. On the list of comorbidities are obesity and smoking. Also ahead of me are people in communal living areas, including prisoners. So if I want to get the vaccine earlier, I should get really fat, start smoking, and commit a crime. Busy weekend for you then😉
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Post by ravey123 on Dec 12, 2020 11:52:50 GMT
I'm pissed off. The United States just approved the vaccine. I am in my 30s and have an autoimmune disease. I'll have to speak with my immunologist about whether it's safe to get vaccinated. If it is safe, I'll only be in the 10th group on the list, behind healthy 65+ and ahead of healthy 55-64. Among the people ahead of me are people with >2 comorbidities. On the list of comorbidities are obesity and smoking. Also ahead of me are people in communal living areas, including prisoners. So if I want to get the vaccine earlier, I should get really fat, start smoking, and commit a crime. That sounds like a belting weekend ahead for you
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Post by andystokey on Dec 12, 2020 12:58:05 GMT
Excellent post Andy, hopefully this graph illustrates exactly what you're on about, positive 'cases' have become essentially meaningless ... I assume the cases figures are based on actual tests - in which case the graph is pretty meaningless in absolute terms as the mount is far higher now than it was back in March - had there been the same level of testing (which the government abandoned because by then managing infections wasn't an option) then the number of cases would have been far higher than now. Setting aside the scale what the graph does show is a correlation between number of cases/infections and deaths - but to make sense of the figures you have to factor in the massive increase in the number of tests. It is naive to think that policy makers are assuming like for like over that time period. In modelling outcomes they will be far more nuanced than just projecting that graph. I agree the figures presented could well be causing confusion but to be fair the actual analysis going on behind the scenes is far too complex to present in a news briefing and would no doubt go over most people's heads (including mine). The JVT approach of using straightforward analogies might well be better. I fundamentally do not agree that inevitable inaccuracies in test data should be used as an excuse to abandon a public policy approach based on managing infections - that is putting the purity of numbers above the primary aim of saving lives. If the government were to revert back to managing hospital admissions and deaths (as they were back in March) there will be far more deaths. The only reason the government took this approach in the first place was because they had lost control of infections. It wasn't as if they they thought this was a better approach - they simply had no choice. In effect you are asking the government to allow a crisis to happen and then manage it rather than try to prevent the crisis from happening in the first place. Every successful country has based there approach on managing infections - and as well as testing the very best have had effective tracing in pace as well which is where we've failed abysmally. The only countries that are using an admissions approach are those that don't have the infrastructure, have gone for herd immunity or have lost control of the situation. Andy - to reverse your heating analogy in effect you are suggesting we stop measuring the outside temperature because it's difficult and prone to inaccuracies and use the far more accurate thermometers on cooling systems to keep people cool while the earth warms up rather than use the imperfect data and associated climate models to address global warming. The reason I think we are overly preoccupied with the Daily TV case rates is that they cause this misinterpretation. If I take the basic premise that Cases is related to infections and deaths can I just show you the variation in daily tests and hence daily cases if you go here all the raw data is available. coronavirus.data.gov.uk/Here is a plot of raw daily data for Cases and Tests on the same chart which I did with no treatment or smoothing with the specimen date aligned, The variation in Tests in a single week is nearly 200,000 at the moment. (Top Chart) If I now divide the number of Cases by the Number of Tests to even out the error into some sort of ratio then according to a simple model I should be finding a trend in number of cases per test on average each day. ( Don't get me going on the number of tests per individual false positive and negative. (Bottom Chart) You can see the enormous variation just on that simple idea. What I can also see is that UK policy lockdown did nothing tangible. My point is that posters saying " oooooh look the cases are up today (sad emoji), lets lockdown" is very naïve to say the least. If anything at the moment including yesterdays data shows a leveling or drop in the second wave across the UK Attachment DeletedBy way of a simple example the Gov quoted: 07/12/2020 21010 Cases from 218055 Tests 06/12/2020 13489 Cases from 273122 Tests that's 50 odd thousand tests more The daily even weekly variation in cases is pointless for you and I to discuss. Every time a new mass testing program is rolled out it gets worse. They either need to come off TV or carry a health warning, once a week at most.
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Post by andystokey on Dec 12, 2020 12:59:05 GMT
Here is the data for the period before and after lockdown as a policy intervention. Attachment Deleted
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Post by Soro's Sorrows on Dec 12, 2020 13:39:12 GMT
I'm pissed off. The United States just approved the vaccine. I am in my 30s and have an autoimmune disease. I'll have to speak with my immunologist about whether it's safe to get vaccinated. If it is safe, I'll only be in the 10th group on the list, behind healthy 65+ and ahead of healthy 55-64. Among the people ahead of me are people with >2 comorbidities. On the list of comorbidities are obesity and smoking. Also ahead of me are people in communal living areas, including prisoners. So if I want to get the vaccine earlier, I should get really fat, start smoking, and commit a crime. Would you class yourself as a people person?
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Post by Davef on Dec 12, 2020 14:01:21 GMT
Defend this...
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