Pre vaccine fatality rates

well and welcome to today's talk Thursday the 27th of October now the study I'm looking at today I found particularly interesting it's looking at the amount of infections before there was any vaccinations and the amount of people that died the so-called infection fatality rate the number of people infected versus the number of people that died and they've been able to look at International Studies where antibody data was collected before we had any vaccination and the infection fatality rate in the naught to 69 year olds at least where the data comes from in this study was way lower than anyone had thought and much lower than I had thought myself and this is important because we use these early assumptions to base this whole covert response on and it turns out obviously quite a few of these early assumptions were wrong the initial assumption that the UK modeling was based on based on the UK population was 0.9 percent infection fatality rate and it's been thankfully not that high why did these professional modelers get it wrong well it looks like to me what they've done is they've confused case fatality rate with infection fatality rate now of course they understand this of course but they were thinking they were looking at the fatality rate for cases that were diagnosed and not making enough allowances for the infections being way higher than the cases now if this information had been known about in the start of 2022 it's counter factual now but would our responses have been very different to this pandemic uh interesting that's what this is about remember this is all pre-vaccination data before there was any vaccination and later on in the video at the end we'll be trying to work out what the effect of vaccination and previous infection and lockdown strategies are but I can't really compress that but that's roughly what this video is about if you want to stick around now the original paper here now inFamous Paper Imperial College London uh Neil Ferguson's team this was the initial report that the UK lockdowns were originally based on so this was the most influential paper now um the reactions to this pandemic strangely enough I don't actually blame the politicians for once um it's disappointing that their senior academics and Senior doctors weren't more aware of what's going on because the politicians only acted on the advice from their senior medical officers now it's not as fine most of those have been knighted now we don't hear much about them anymore but that's that's where the problem lay these professionals really should have known more than they did at the time now it's easy to diagnose with what we call a retro spectroscope looking back but still there's errors errors have been made now let's let's not uh talk too much about it let's go straight on to the well this is the study here now the study we're looking at Supreme study but it's a big international study so it looks pretty good and I'm sure this is going to get into a journal fairly soon but anyway let's uh not speculate any further let's dive straight into the data now this is from the original uh Imperial report that the UK lockdown was based on 16th of March way back in 20. 20 I certainly remember talking about it then if you've got a good memory you will they made assumptions incubation period 5.1 days not unreasonable at the time remember this is the original Wu hand strain we're talking about now infectious 12 hours to 4.6 days after the r naught 2.4 each infected person infected in 2.4 other people without mitigation measures now non-uniform attack rate they knew about that that the news being so the amount of people getting sick they knew it wouldn't be the same across the whole population but it gave a resultant infection fatality rate of 0.9 according to their modeling 4.4 infections will be hospitalized according to their modeling the average day in hospital would be 10.4 days and 30 of people will go to intensive care with a 50 death rate giving rise to an overall infection fatality rate of 0.9 getting on for one percent and these uh death rates are not accounting for the knock-on effects in people that couldn't get care because of the hospital beds being crammed full of um people with covert so that was the prediction that the government based the lockdown strategy and indeed instigated the vaccination program on and as it turns out a lot of this data wasn't as accurate as we'd like so the infection fatality rate of 0.9 is is uh was very pessimistic probably because they were largely looking at cases rather than infections of course the case fatality rate and the infection fatality rate are going to be grossly different the case fatality rate people that I recognize we're going to get a much higher number there infection fatality rates because of infections we don't recognize we're going to get a much lower number there and um it really is disappointing that senior people weren't more aware of this at the time and able to advise more accurately now going on to the paper the firm data that we have this is the paper here age stratified infection fatality now this is a bit of a frustrating paper because it only deals up to the age of verse 69 but it gives rationales for that so we can't say what would have happened in in the older population because we simply don't have the data but this is the data for naught to 69 year olds of course the fatality rate in the older groups would have been higher we know that for sure how much higher we don't know because this data doesn't tell us now this is academics in Stanford California Rome Italy Montreal Canada so International academics now they took 40 eligible National zero prevalence surveys that's the number of people that had antibodies in their blood before vaccination so this is the time of the original Wuhan strain before there was any vaccination this is kind of the the pandemic In The Raw if you like before it had been mitigated by lots of infections and vaccination covering 38 countries with pre-vaccination zero prevalence data so pretty pretty good data scale here 29 countries probably available age stratified data was available so they're able to look at who had the antibodies and what age those people were and the age stratified zero prevalence information were available so this was available in quite a few countries and they were included in the primary analysis so as good academics do they took these numbers and they re-analyzed the numbers to come up with their own data using the numbers from the various studies that were cited quite an impressive piece of work actually quite complicated piece of work but impressive infection fatality rates for not to 59 year olds the median infection fatality rate was 0.035 0.035 percent and the interquartile range in other words 50 of the results fell in between here 0.02 and 0.056 so that's looking at the people that died compared to the number of people that had antibodies the infection fatality rate now this is without accounting for zero reversion reversion so I'm sure most of you know zero conversion is when someone gets an infection and they produce the antibodies they zero convert and show the antibodies but then after three or four months the antibodies can become difficult to detect and they zero revert so people might be only zero positive for three or four months depending on the sensitivity of the test the time from zero conversion to zero reversion and they haven't accounted for zero reversion here so that means these figures are as bad as they could be because a lot of people will have ready uh zero reverted so they'll have had the infection but they won't have shown up on the study so these are the most pessimistic of the death rates really because of that uh probability that well the certainty really that the average timeframe zero conversion to serial reversion is only three to four months with most of the studies that were done at the time there's more accurate studies available now but that was based on the time because remember this is all in the pre-vaccination mostly 2020 period all right infection fatality rates for not to 69 year olds as you would expect higher so remember it was 0.35 for the North to 59s the north to 69s the infection fatality rate uh was at 0.095 again you can see basically 10 times lower than the the UK calculated figure which was assumed by the modelers of 0.09 original Wuhan virus no vaccines at this stage so there you go not uh about getting on for 10 times less right not to 19 of course uh the infection fatality rate was 0.0003 percent and of course many of those would have a significant comorbidities 20 to 29 years of age it was a 0.0 to naught three percent so quite a lot higher uh between uh well Ten Times Higher isn't it between 20 to 29 and 0 to 19. uh 30 to 39 infection fatality rate 0.011 so again pretty uh low death rates 40 to 49 0.035 uh 50 to 59 0.12 9 and uh 60 to 69. infection fatality rate zero put getting on for half a percent isn't it so that is uh that is what they found now unfortunately frustratingly it doesn't go higher than that that's what this study was about but clearly um even that is quite a bit lower than the 0.9 percent that the modeling was based on now of course this philosophical measures philosophical questions here isn't there I mean of course all civilized societies have to protect their vulnerable members the young the ill and the elderly that is the measure of civilization but still this is this is hard data so so if I got the original Wuhan strain pre-vaccine uh my infection fatality risk would have been about half a percent um I don't have other metabolic diseases so maybe it would have been a bit better because a lot of those people of course in that half percent have got other uh comorbidities that we know about but um that would be the roughly the range an interesting idea you know you'd have a half percent chance of chance of dying us it's an interesting concept right at a global level um pre-vaccine infection fatality rates may have been as low as and these are the estimates here for the global level not to 59's uh 0. not three percent potential infection fatality rates not to 69 so that's everyone from newborn babies all the way up to 69 year olds taking the whole lot as an average clumping them all together in one group 0.07 but of course I've just seen the gradations between that now the reason they did this was 94 of the global population are younger than 70. and 91 younger than 65 and 86 percent younger than 60. now they don't say so here but the more vulnerable populations of course um well many were guarding at that time weren't they so were at least partly protected um while they waited for um immunity and vaccinations now other things noted from this paper the current analysis suggests a much lower pre-vaccination infection fatality rate in non-elderly populations than previously suggested so the original Wuhan strain before there was any vaccines was killing many fewer people than we had thought and that other data had suggested still killing substantial amounts but not as many as with thought in the pre-vaccination era a large differences did exist between countries and may reflect differences in comorbidities and other factors of course and we've looked at this many times for example we know that the comorbidities in the states have resulted in more deaths in the states we don't we don't know that it's very likely that that is the case we know there's been a lot of deaths in the states more in the Soviet Union in Russia I keep saying Soviet Union during my age more in Russia um but uh the United States higher uh death rate per capita than the UK for example and we suspect that's due to comorbidities although we don't know that for sure the correlation is there though um these estimates provide a Baseline from which to Fathom that is to work out suss out further infection fatality rate declines with the widespread use of vaccination so the infection fatality rates are way down now What proportion of this is due to vaccination What proportion of this is due to Prior infections and What proportion of this is due to Revolution of different variants this doesn't tease out they are the three main factors What proportion of the reduction in infection fatality rates that is attributable to each of those or indeed the combinations of these alas is beyond the scope of this paper too calculate but we're looking forward to more data on that not too many studies coming on this unfortunately but it would be interesting to know the proportion due to vaccination Prime infection unevolution of new variants don't hold your breath it could be some time before we know that now unmitigated uh epidemic March 2020 estimates the original model is estimated that in the UK that just be over half a million deaths and in the United States there'll be 2.2 million deaths they were the original um estimates what we've actually had as of today this 27th of October 168 913 in the United Kingdom and just over a million and sixty five thousand in the United States so despite the developing immunity despite the vaccinations despite the evolution of new variants unfortunately the death rates have still been way higher than we would have liked so one day we'll probably be able to assess just how efficacious all of these factors have been in reducing death rates vaccination prior infection Evolution view variants but unfortunately I don't see many studies coming along which are really differentiating between those factors so we might not know for some time but I thought that was interesting original infection fatality rates before vaccination with the original Wuhan strain which of course probably was the most deadly now we could we can't really say whether the Wuhan or the alpha or the Delta was more deadly we don't really know we know for sure the Omicron is less deadly um so it's hard really to tell but perhaps the Wuhan strain was the most deadly um so we would certainly get less infection fatality rates now in the age of Omicron than we would in the age of uh Wuhan alpha or Delta interesting things to come disappointing failures by some of our senior leaders but that's where we're at now and uh that's all I can think of to say so thank you for watching

Channel: Dr. John Campbell

Views: 731,164

Keywords: physiology, nursing, NCLEX, health, disease, biology, medicine, nurse education, medical education, pathophysiology, campbell, human biology, human body

Id: GxsD1UbVhGw

Length: 16min 55sec (1015 seconds)

Published: Thu Oct 27 2022