climateetc--2020-04-15--In favor of epistemic trespassing

In favor of epistemic trespassing

On the importance of expertise from other fields for COVD19 and climate change .
This post is motivated by a tweet from Steve McIntyre , with comment from Ken Rice : Here is the link to Annan ’ s post Dumb and Dumber , its actually quite good .
The money quote : “ All these people exhorting amateurs to “ stay in their lane ” and not muddy the waters by providing analyses and articles about the COVID-19 pandemic would have an easier job of it if it wasn ’ t for the supposed experts churning out dross on @ @ @ @ @ @ @ this twitter exchange , I spotted a link to a new paper entitled ( tweeted by Oxford Philosophy ) entitled Epistemic Trespassing .
Excerpts : Complex issues such as COVID19 and climate change , with massive policy implications , introduce a whole host of additional issues related to epistemic trespassing .
I ’ ve written many blog posts on expertise [ link ] .
Here is a pet peeve of mine : many academics who label themselves as ‘ climate scientists ’ , even though their degrees might be in economics , biology @ @ @ @ @ @ @ speaking publicly , and responding to reporters ’ queries , on climate topics well outside their expertise .
They use their status as ‘ climate scientist ’ to expound on aspects of climate science , economics , policy , whatever , that they know next to nothing about .
The flip side of this coin is the dismissal by academics of the likes of Steve McIntyre , who has brought much needed expertise in statistics and data probity to the field of paleoclimate .
Moving on to COVID19 , the problems with COVID19 @ @ @ @ @ @ @ posts in the most recent CoV Discussion Thread : • Don ’ t believe the COVID models – that ’ s not what they ’ for [ link ] • On the predictability of infectious disease outbreaks [ link ] • America ’ s most influential COV model just revised its estimates downward – but not all models agree [ link ] • How can COVID models get it so wrong ?
[ link ] James Annan has another good blog post : Model calibration , nowcasting and operational prediction of the COVID19 @ @ @ @ @ @ @ simple model with observational data is much the same whether it ’ s paleoclimate or epidemics .
The maths and the methods are much the same .
In fact this one is a particularly easy one as the model is embarrassingly linear ( once you take the logarithm of the time series ) .
” “ The basic concept is to use the model to invert the time series of reported deaths back through the time series of underlying infections in order to discover the model parameters such as the famous reproductive rate @ @ @ @ @ @ @ and I am still bemused by the fact that none of the experts ( in the UK at least ) are doing this .
I mean what on earth are mathematical epidemiologists actually for , if not this sort of thing ?
They should have been all over this like a rash .
The exponential trend in the data is a key diagnostic of the epidemic and the experts didn ’ t even bother with the most elementary calibration of this in their predictions that our entire policy is based on .
It @ @ @ @ @ @ @ s as if someone ran a simulation with a climate model and presented the prediction without any basic check on whether it reproduced the recent warming .
You ’ d get laughed out of the room if you tried that at any conference I was attending .
By me if no-one else ( no , really , I wouldn ’ t be the only one ) .
” While I haven ’ t dug into COVID models at all , with regards to model calibration and nowcasting my experience in weather forecasting is @ @ @ @ @ @ @ .
For operational weather prediction ( say for hurricanes ) , there are several components to calibration of the forecasts .
You can calibrate the model inputs and/or the model outputs .
You can calibrate the model based on historical forecasts for previous epidemics ( compared with observations ) .
You can also calibrate the model based on recent error statistics ( in model inputs and/or outputs ) .
The experiences/outcomes of each country impacted by COVID provides data to be used in calibration .
It appears that the weather/climate community has much @ @ @ @ @ @ @ .
Fortunately , weather and climate scientists haven ’ t been ‘ staying in their lane , ’ we ’ ve seen a number of COVID analyses from this community ( including a few at Climate Etc .
The postnormal science group ( Funtowicz , Ravetz , van der Sluijs et al . )
have written an article PostNormal Pandemics .
“ Despite the truly historic mobilization of science , our knowledge in crucial areas is still swamped by ignorance , especially on the sources of the virus but also on its progress @ @ @ @ @ @ @ in COVID-19 policy advice builds on speculative assumptions on the virus itself , and how far it ’ s possible to control and predict how people behave .
Known unknowns include the real prevalence of the virus in the population ; the role of asymptomatic cases in the rapid spread of the virus ; the degree to which humans develop immunity ; the dominant exposure pathways ; the disease ’ s seasonal behaviour ; the time to deliver global availability of an effective vaccine or cure ; and the nonlinear response of individuals @ @ @ @ @ @ @ in the complex system of communities interconnected across multiple scales , with many tipping points , and hysteresis loops ( implying that society may not be able to rebound to the state it was in before the coronavirus interventions took place ) .
These deep uncertainties make quantitative predictions speculative and unreliable .
Instead , following a pattern well known to PNS practitioners , predictions which purportedly “ jarred the U.S. and the U.K. to action ” can only be obtained by mathematical models that produce crisp numbers , even though these numbers @ @ @ @ @ @ @ artificially compressing the associated uncertainty .
“ There is no number-answer to your question , ” explodes an angry medical expert to the politician trying to force a number out of him .
It would be much more effective to run our societies under the assumption that our resources should not be allocated according to a strategy of prediction and control .
More data ( even ‘ reliable data ’ ) and better predictive models can not resolve the ‘ distribution of sacrifice ’ which involves , among other things , the arbitration @ @ @ @ @ @ @ .
This can not be delivered by artificial intelligence , algorithms and models alone .
We need to pursue an adaptability based on preserving diversity and flexible management .
”

by Judith Curry
On the importance of expertise from other fields for COVD19 and climate change.
This post is motivated by a tweet from Steve McIntyre, with comment from Ken Rice:
Here is the link to Annan’s post Dumb and Dumber, its actually quite good. The money quote:
“All these people exhorting amateurs to “stay in their lane” and not muddy the waters by providing analyses and articles about the COVID-19 pandemic would have an easier job of it if it wasn’t for the supposed experts churning out dross on an industrial scale.”
McIntyre responds:
Epistemic trespassing
Shortly after spotting this twitter exchange, I spotted a link to a new paper entitled (tweeted by Oxford Philosophy) entitled Epistemic Trespassing. Excerpts:
“Epistemic trespassers are thinkers who have competence or expertise to make good judgments in one field, but move to another field where they lack competence—and pass judgment nevertheless. We should doubt that trespassers are reliable judges in fields where they are outsiders.” In other words, stay in your lane. “Trespassing is a significant problem in an age of expertise and punditry, but it’s not new. In Plato’s Apology, Socrates tells us he tracked down citizens in Athens who had reputations for being skilled. He met politicians, poets, and craftsmen and tested their mettle. As Socrates says, he ‘found those who had the highest reputation were nearly the most deficient’ . Socrates diagnosed the problem: because these men had been so successful in their particular crafts, each one ‘thought himself very wise in most important pursuits, and this error of theirs overshadowed the wisdom they had’ . Puffed up by their achievements in one domain, the successful Athenians trespassed on matters about which they were ignorant.” “First, trespassing is a widespread problem that crops up especially in the practice of interdisciplinary research, as opposed to what we might call ‘single-discipline’ research. Second, reflecting on trespassing should lead us to have greater intellectual modesty, in the sense that we will have good reason to be far less confident we have the right answers to many important questions.” “Epistemic trespassing of the sort I’ve noted is easy to recognize. Experts drift over a highly-visible boundary line and into a domain where they lack either the relevant evidence or the skills to interpret the evidence well. But they keep talking nonetheless. Experts on a public stage are cast in the role of the ‘public intellectual’ or ‘celebrity academic’. They may find trespassing all but impossible to resist. Microphones are switched on, TV cameras zoom in, and ‘sound bites’ come forth, coaxed out of the commentators by journalists. So what do you have to say about philosophy, Neil deGrasse Tyson? And what about arguments for the existence of God, Professor Dawkins? I don’t think trespassing is exclusively a problem for scholars in the limelight, however, and one of my goals here is to explain why ordinary researchers often risk trespassing, too.” “But first we must understand what the epistemological problem with trespassing is. There is not only one problem. Consider three types of problematic trespassing cases, where two different fields share a particular question: (a) Experts in one field lack another field’s evidence and skills; (b) Experts in one field lack evidence from another field but have its skills; (c) Experts in one field have evidence from another field but lack its skills. “I will examine three strategies to justify acts of trespassing and thereby preserve rational confidence in trespassers’ answers to hybridized questions. Again, we are assuming some trespassers are experts in one field but encroach on another field. (D1) I am trespassing on another field, but that field does not feature any relevant evidence or skills that bear on my view about p;
(D2) I am trespassing on another field, but my own field’s evidence conclusively establishes that p is true;
(D3) I am trespassing on another field, but my own field’s skills successfully ‘transfer’ to the other field.” “I suspect we must trespass to answer most important questions. Perhaps this means we should never trespass alone. Instead, we must rely on the expertise of others. What we need, to extend the trespassing metaphor, is an ‘easement’ or ‘right of way’ for travel beyond our fields’ boundaries. The right of safe passage could be secured by our collaboration with cross-field experts. Imagine your colleague is a representative source of evidence, skills, and potential criticism from another field. Even if you don’t have direct knowledge of that field, if your colleague tests out your answer to a hybridized question and tells you it sounds right to her, then your view is appar- ently more reasonable than it would have been otherwise. Trespassers may gain reasonable beliefs by engaging in certain kinds of discussion with cross-field colleagues.” While this paper raises some interesting issues, its main goal seems to be protecting the turf of academic subfields.
COVID19 and climate change
Complex issues such as COVID19 and climate change, with massive policy implications, introduce a whole host of additional issues related to epistemic trespassing. I’ve written many blog posts on expertise [link].
Here is a pet peeve of mine: many academics who label themselves as ‘climate scientists’, even though their degrees might be in economics, biology, whatever, have no compunction about speaking publicly, and responding to reporters’ queries, on climate topics well outside their expertise. They use their status as ‘climate scientist’ to expound on aspects of climate science, economics, policy, whatever, that they know next to nothing about.
The flip side of this coin is the dismissal by academics of the likes of Steve McIntyre, who has brought much needed expertise in statistics and data probity to the field of paleoclimate.
Moving on to COVID19, the problems with COVID19 projections were summarized in a number of posts in the most recent CoV Discussion Thread:
Don’t believe the COVID models – that’s not what they’ for [link]
Mathematical models to characterize early epidemic growth: A review. [link]
On the predictability of infectious disease outbreaks [link]
Dramatic reduction in COVID disaster projections [link]
America’s most influential COV model just revised its estimates downward – but not all models agree [link]
How can COVID models get it so wrong? [link]
James Annan has another good blog post: Model calibration, nowcasting and operational prediction of the COVID19 pandemic
“Turns out that calibrating a simple model with observational data is much the same whether it’s paleoclimate or epidemics. The maths and the methods are much the same. In fact this one is a particularly easy one as the model is embarrassingly linear (once you take the logarithm of the time series).”
“The basic concept is to use the model to invert the time series of reported deaths back through the time series of underlying infections in order to discover the model parameters such as the famous reproductive rate R. It’s actually rather simple and I am still bemused by the fact that none of the experts (in the UK at least) are doing this. I mean what on earth are mathematical epidemiologists actually for, if not this sort of thing? They should have been all over this like a rash. The exponential trend in the data is a key diagnostic of the epidemic and the experts didn’t even bother with the most elementary calibration of this in their predictions that our entire policy is based on. It’s absolutely nuts. It’s as if someone ran a simulation with a climate model and presented the prediction without any basic check on whether it reproduced the recent warming. You’d get laughed out of the room if you tried that at any conference I was attending. By me if no-one else (no, really, I wouldn’t be the only one).”
While I haven’t dug into COVID models at all, with regards to model calibration and nowcasting my experience in weather forecasting is even more relevant here than climate modeling. For operational weather prediction (say for hurricanes), there are several components to calibration of the forecasts. You can calibrate the model inputs and/or the model outputs. You can calibrate the model based on historical forecasts for previous epidemics (compared with observations). You can also calibrate the model based on recent error statistics (in model inputs and/or outputs). The experiences/outcomes of each country impacted by COVID provides data to be used in calibration.
It appears that the weather/climate community has much to offer in terms of COVID modeling. Fortunately, weather and climate scientists haven’t been ‘staying in their lane,’ we’ve seen a number of COVID analyses from this community (including a few at Climate Etc.
Postnormal pandemics
The postnormal science group (Funtowicz, Ravetz, van der Sluijs et al.) have written an article PostNormal Pandemics.
“Despite the truly historic mobilization of science, our knowledge in crucial areas is still swamped by ignorance, especially on the sources of the virus but also on its progress and future outcomes. The expertise employed in COVID-19 policy advice builds on speculative assumptions on the virus itself, and how far it’s possible to control and predict how people behave.
Known unknowns include the real prevalence of the virus in the population; the role of asymptomatic cases in the rapid spread of the virus; the degree to which humans develop immunity; the dominant exposure pathways; the disease’s seasonal behaviour; the time to deliver global availability of an effective vaccine or cure; and the nonlinear response of individuals and collectives to the social distancing interventions in the complex system of communities interconnected across multiple scales, with many tipping points, and hysteresis loops (implying that society may not be able to rebound to the state it was in before the coronavirus interventions took place). These deep uncertainties make quantitative predictions speculative and unreliable.
Instead, following a pattern well known to PNS practitioners, predictions which purportedly “jarred the U.S. and the U.K. to action” can only be obtained by mathematical models that produce crisp numbers, even though these numbers have been obtained at the cost of artificially compressing the associated uncertainty. “There is no number-answer to your question,” explodes an angry medical expert to the politician trying to force a number out of him.
It would be much more effective to run our societies under the assumption that our resources should not be allocated according to a strategy of prediction and control.
More data (even ‘reliable data’) and better predictive models cannot resolve the ‘distribution of sacrifice’ which involves, among other things, the arbitration of dilemmas that appear at every scale. This cannot be delivered by artificial intelligence, algorithms and models alone. We need to pursue an adaptability based on preserving diversity and flexible management.”
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr


climateetc--2020-04-05--Sunday fun: personality testing

Sunday fun: personality testing

And now for something different .
Please take the Enneagram personality test ( with instinctual variant ) and report your @ @ @ @ @ I scored a strong 1 ( reformer ) .
Hard to argue with that As @ @ @ @ @ , knowing our fellow denizen ’ s enneagram type will help us deal more constructively @ @ @ @ @ number of years ago ( 2011 ! )
we conducted the Jungian personality test as @ @ @ @ @ INTJ .
[ link ]

by Judith Curry
And now for something different.
A sociological experiment for the Denizens.
Please take the Enneagram personality test (with instinctual variant) and report your results in a comment.
I scored a strong 1 (reformer). Hard to argue with that
As tensions elevate around the world, knowing our fellow denizen’s enneagram type will help us deal more constructively with their comments.
A number of years ago (2011!) we conducted the Jungian personality test as a group – I scored INTJ. [link]
Have fun, and avoid insulting fellow Denizens.
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr


climateetc--2020-03-25--COVID-19: Updated data implies that UK modelling hugely overestimates the expected death rates from

COVID-19: Updated data implies that UK modelling hugely overestimates the expected death rates from infection

There has been much media coverage about the danger to life posed by the COVID-19 coronavirus pandemic .
While it is clearly a serious threat , one should consider whether the best evidence supports the current degree of panic and hence government policy .
Much of the concern in the UK resulted from a non-peer reviewed study published by the COVID-19 Response Team from Imperial College ( Ferguson et al 2020 [ 1 ] ) .
In this article , I examine whether data from the Diamond Princess cruise ship – arguably the most useful data set available – support @ @ @ @ @ @ @ study .
I find that it does not do so .
The likely fatality rates for age groups from 60 upwards , which account for the vast bulk of projected deaths , appear to be much lower than those in the Ferguson et al .
study .
Metrics for COVID-19 ’ s fatality rate and their estimation The fatality rate from infection ( IFR ) , by age group , is a key parameter in determining how serious a threat the COVID-19 pandemic represents .
Unfortunately , the IFR is difficult to determine @ @ @ @ @ @ @ the fatality rate for cases where the COVID-19 virus can be shown , by a standard test , to be present , whether or not there are any symptoms .
This is referred to as the true case fatality rate ( tCFR ) .
The tCFR will overestimate the IFR , since a proportion of people who actually have been infected may show no viral presence when tested , either because they have already fought off and cleared an infection without any noticeable symptoms , or perhaps because they have pre-existing immunity .
@ @ @ @ @ @ @ to a sample of people without regard to whether they show symptoms , the tCFR may provide a reasonable , albeit somewhat biased high , estimate of the IFR .
However , determining tCFR is not simple either , since in most cases infected people with no or mild symptoms will not be tested for COVID-19 .
Attempts have nevertheless been made to estimate tCFR by adjusting estimates of the CFR based on symptomatic cases only ( sCFR ) , by adjusting for the non-random nature of testing , and also for the @ @ @ @ @ @ @ being known for some time .
The Ferguson et al .
study used estimates of the IFR [ 2 ] from another paper from the same team , Verity et al .
( 2020 ) [ 3 ] , which had been published a few days earlier on 13 March .
Very helpfully , Verity et al.
, unlike Ferguson et al.
, published the computer code and data that they used .
The Verity et al .
CFR estimates were derived primarily from Chinese data , which reflected non-random testing .
The @ @ @ @ @ @ @ reality , tCFR estimates ) by adjusting their CFR estimates using infection prevalence data for expatriates evacuated from Wuhan , all of whom were tested for COVID-19 infection .
This approach involves very large uncertainties .
An alternative approach to estimating the tCFR , as a proxy for the IFR , is to use data from a large sample of people , all of whom were tested for the presence of the virus without regard to whether they showed any symptoms , with all who tested positive subsequently being isolated and the case @ @ @ @ @ @ @ .
While the sample of expatriates evacuated from Wuhan is too small for this purpose , [ 4 ] occupants of the Diamond Princess cruise ship do provide a suitable such sample .
[ 5 ] Moreover , the Diamond Princess sample has the advantage that it consists mainly of people from high income countries , and those requiring hospitalisation were treated in such countries .
The Diamond Princess sample may well represent the best available evidence regarding tCFR for older age groups , who are most at risk .
Verity et al @ @ @ @ @ @ @ the Diamond Princess , but did not use sCFR or tCFR estimates from them for their main CFR and IFR estimates .
[ 6 ] When Verity et al .
was prepared , the final death toll was not known .
The data available only ran to 5 March 2020 , at which point 7 passengers had died .
The authors therefore used a fitted probability distribution for the delay from testing positive to dying to estimate that those deaths would represent 56 % of the eventual death toll .
They accordingly therefore @ @ @ @ @ @ @ of 12.5 deaths .
Here , I adopt the same death rate model and use the same data set , but brought up to date .
By 21 March the number of deaths had barely changed , increasing from 7 to 8 .
Of those 8 deaths , 3 are reported to have been in their 70s and 4 in their 80s .
I allocate the remaining , unknown age , person pro rata between those two age groups .
As at 21 March the Verity et al .
model estimates that 96 @ @ @ @ @ @ @ occurred , so we can scale up to 100 % , giving an estimated ultimate death toll of 8.34 , allocated as to 3.58 to the 70-79 age group and 4.77 to the 80+ age group .
Accordingly , the Verity et al central estimate for the Diamond Princess death toll , of 12.5 eventual deaths , is 50 % too high .
This necessarily means that the estimates of tCFR and sCFR they derived from it are too high by the same proportion .
The Diamond Princess dataset was published by the @ @ @ @ @ @ @ NIID ) .
I use the second version published on 21 February [ 7 ] , which gives detailed data for 619 confirmed cases , updating it for subsequent test results .
[ 8 ] Verity used the original 19 February version of NIID , which gave data for 531 confirmed cases , although they did update it for subsequent test results .
The entire set of passengers and crew , totalling 3711 individuals , was tested for COVID-19 .
Some 706 ( 19.0 % ) ultimately had positive test results , of @ @ @ @ @ @ @ for 619 of them ) 51 % were asymptomatic .
The infection rate varied between 10.0 % for ages under 30 years to 24.5 % for ages 60+ years .
The age-distribution was only known for cases included in the NIID data .
Verity et al .
assumed that the age distribution for the overall total of 706 confirmed cases was the same as for the 531 NIID reported cases that they used .
I do the same , but using the later NIID data , with 619 reported cases .
On that @ @ @ @ @ @ @ people in respectively the 60–69 , 70–79 and 80+ key age groups had positive test results .
Recall that tCFR is the eventual death toll divided by the total numbers testing positive .
My overall tCFR central estimates from the Diamond Princess 70+ age groups , where all the deaths are taken to have occurred , are 2.54 % overall ( 8.34/328.5 ) , [ 9 ] with a breakdown of 1.34 % for ages 70-79 ( 3.58/266.9 ) and 8.04 % ( 4.77/61.6 ) for ages 80+ .
For the 60–69 age @ @ @ @ @ @ @ to make a crude median estimate of the tCFR , by calculating what it would need to be for there to be a 50 % probability that no 60-69 year-old has died , as appears to have been the case .
The thus-implied tCFR is 0.34 % .
There were too few Diamond Princess occupants in age groups below 60 with positive test results to provide any useful information about the COVID-19 tCFR for those groups .
It appears that in about 30 % of symptomatic cases the standard RT-PCR test for COVID-19 @ @ @ @ @ @ @ patient is in fact infected .
[ 10 ] There is no evidence of any COVID-19 related deaths among Diamond Princess occupants who tested negative , which would be consistent with a lower viral load being associated with a lower probability both of a positive RT-PCR test result and of eventual death .
The false-negative rate may be slightly lower for Diamond Princess occupants , a few of whom may have been retested or tested by a more reliable method where they had typical COVID-19 symptoms but an initially negative RT-PCR test result @ @ @ @ @ @ @ the proportion of asymptomatic infected cases that are not detected by a RT-PCR test will be somewhat higher than the 30 % estimated for symptomatic cases .
We accordingly adjust all the tCFR ratios estimated from Diamond Princess case data down by 30 % on account of false-negative test results .
The observed deaths of Diamond Princess occupants occurred over a 45 day period , during which a non-negligible percentage of old people would be expected to die from non-COVID-19 related causes .
I have accordingly deducted from the adjusted tCFR ratios an @ @ @ @ @ @ @ groups , based on UK age-stratified 2018 death rates , [ 11 ] to arrive at estimates of deaths caused by COVID-19 .
There are arguments for the non-COVID death rates being either higher or lower than those for the UK population of the same age , but using those death statistics appears to be a reasonable first approximation .
Comparing the Ferguson et al .
UK and Diamond Princess based fatality rate estimates The results of the foregoing analysis are set out in Table 1 .
The key finding is that the @ @ @ @ @ @ @ groups , which must if anything overestimate their IFRs , are far lower than the corresponding IFR estimates used by Ferguson et al .
in the study adopted by the UK government .
[ 12 ] Those age groups account for the vast bulk of projected deaths .
For people aged 60–69 , the Ferguson et al IFR estimate is 19.4 times as high as the best tCFR estimate based on Diamond Princess data , for the 70–79 age group it is 8.3 times as high , and for the 80+ age group @ @ @ @ @ @ @ Table 1 : True Case Fatality Rates estimated from the latest Diamond Princess data compared with Infection Fatality Rates per Ferguson et al .
2019 , used by the UK government Note : An all-causes tCFR of 0.34 % ( and hence 0.69 notional ultimate fatalities ) is assumed for age-group 60-69 despite there being no actual fatalities in that age group ( see text ) .
Expected non-COVID-19 fatalities are based on UK 2018 death rates by age group applied to the DP positive test cases , scaled by the 45 day @ @ @ @ @ @ @ and divided by the same 0.96 factor used to scale up the 8 actual deaths .
DP= Diamond Princess .
Based on the Diamond Princess data , the COVID-19 fatality rates by age-group assumed by Ferguson et al .
appear to be far too pessimistic for all 60+ age groups , where the vast bulk of fatalities are projected to occur .
It is quite possible that they are also too pessimistic for younger age groups as well , but unfortunately the Diamond Princess data are uninformative about death rates below age 60 @ @ @ @ @ @ @ the 60+ age groups the projected excess death rates , based on Diamond Princess case data , caused by COVID-19 is substantially lower than the underlying non-COVID-19 annual death rate .
Even assuming , very pessimistically , that there is no overlap between the two , and that the same proportion of each age group becomes infected , projected COVID-19 related deaths from an epidemic in which the vast bulk of the population became infected with COVID-19 are only 9 % of expected annual non-COVID deaths for the 60–69 age group .
[ @ @ @ @ @ @ @ , the proportion is 20 % , and for the 80+ age group it is 26 % .
Relative to the expected non-COVID deaths over two years , the approximate period during which very onerous restrictions are projected to be in force in the UK , these COVID-19 excess death proportions would each be reduced by almost half .
In practice , a high proportion of people killed by COVID-19 will have serious underlying health conditions , and would be much more likely than average to die from non-COVID-19 causes .
Since writing @ @ @ @ @ @ @ people from the Diamond Princess have died , bringing the death toll to 10 .
Although , as one or two commenters have pointed out , the Worldometer website has for the last day or two been showing 10 deaths , it cites no source for that , the World Health Authority Situation Reports show no change in the number of deaths , and my original searching of the Japanese language Ministry of Health , Labor and Welfare reports showed deaths remaining at 8 .
However , I have now , using Japanese @ @ @ @ @ @ @ 24 to 26 March , which show an increase from 8 to 10 deaths .
I have therefore updated my analysis to reflect that increase , as per the revised Table 1 below .
No information as to age at death was given , so I have allocated the 2 further deaths pro rata to the 70-79 and 80+ age groups .
I have not reduced the scaling up for possible future deaths to reflect the later date , as it appears that the probability distribution that Verity et al .
used may @ @ @ @ @ @ @ than a month after testing positive for COVID-19 .
I have also updated the total number of positive test results from 706 to 712 , in line with the latest Japanese report .
Although the tCFRs that I estimate for older age groups are now slightly higher , they are still far below the Ferguson et al .
estimates .
The projected excess COVID-19 deaths over annual UK deaths rates given in the final paragraph become 10 % , 27 % and 34 % for the 60-69 , 70-79 and 80+ age groups @ @ @ @ @ @ @ are qualitatively unchanged .
Table 1 ( revised ) : True Case Fatality Rates estimated from the latest Diamond Princess data compared with Infection Fatality Rates per Ferguson et al .
2019 , used by the UK government [ 1 ] Neil M Ferguson , Impact of non-pharmaceutical interventions ( NPIs ) to reduce COVID-19 mortality and healthcare demand , Imperial College COVID-19 Response Team Report 9 , 16 March 2020 , https : //spiral.imperial.ac.uk:8443/handle/10044/1/77482 [ 2 ] Ferguson et al .
adjusted the Verity et al .
IFR estimates “ to account @ @ @ @ @ @ @ without giving further information about the assumed attack rates .
They appear to have increased the Verity et al .
IFR estimates for all 60+ age groups by approximately 19 % , while making little or no changes to those for younger age groups .
It is unclear whether doing so was justified .
[ 3 ] 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 .
[ 4 ] Their sample of evacuated @ @ @ @ @ @ @ on 6 tested positive for COVID-19 , none of whom died .
[ 5 ] Russell et al.
, “ Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship ” , medRxiv preprint dated 9 March 2020 , did use exclusively Diamond Princess data .
However , the early data that they used was incomplete and their IFR ( actually tCFR ) estimates appear to be based on assuming 26 eventual deaths , and hence are far too high .
[ @ @ @ @ @ @ @ noted that that figures derived from the Diamond Princess data set were “ consistent ” with their main estimates , meaning that they fell within their very wide main estimate uncertainty ranges .
[ 8 ] Using the same sources as given in Verity et al.
, for dates from 20 February on .
Doing so yields a total of 704 positive test results , which I adjust to equal the cumulative total of 706 results stated in the final , 2 March 2020 , update .
[ 9 ] A simple estimate @ @ @ @ @ @ @ the 97.5 % upper uncertainty bound is approximately double this figure .
[ 12 ] Moreover , I am unable to reproduce the Ferguson et al .
estimate of 510,000 deaths in the ‘ Do nothing ’ case , based on their estimate of 81 % of the population being infected and using their IFRs .
Note that the Ferguson et al .
IFR estimates assume that , as was the case for the infected Diamond Princess occupants , health systems have not been overwhelmed by COVID-19 cases .
[ 13 ] Based @ @ @ @ @ @ @ that 81 % of the population eventually becomes infected .

By Nic Lewis
Introduction
There has been much media coverage about the danger to life posed by the COVID-19 coronavirus pandemic. While it is clearly a serious threat, one should consider whether the best evidence supports the current degree of panic and hence government policy. Much of the concern in the UK resulted from a non-peer reviewed study published by the COVID-19 Response Team from Imperial College (Ferguson et al 2020[1]). In this article, I examine whether data from the Diamond Princess cruise ship – arguably the most useful data set available – support the fatality rate assumptions underlying the Imperial study. I find that it does not do so. The likely fatality rates for age groups from 60 upwards, which account for the vast bulk of projected deaths, appear to be much lower than those in the Ferguson et al. study.
Metrics for COVID-19’s fatality rate and their estimation
The fatality rate from infection (IFR), by age group, is a key parameter in determining how serious a threat the COVID-19 pandemic represents. Unfortunately, the IFR is difficult to determine. It is more practical to estimate the fatality rate for cases where the COVID-19 virus can be shown, by a standard test, to be present, whether or not there are any symptoms. This is referred to as the true case fatality rate (tCFR). The tCFR will overestimate the IFR, since a proportion of people who actually have been infected may show no viral presence when tested, either because they have already fought off and cleared an infection without any noticeable symptoms, or perhaps because they have pre-existing immunity. Nevertheless, where testing has been applied to a sample of people without regard to whether they show symptoms, the tCFR may provide a reasonable, albeit somewhat biased high, estimate of the IFR.
However, determining tCFR is not simple either, since in most cases infected people with no or mild symptoms will not be tested for COVID-19. Attempts have nevertheless been made to estimate tCFR by adjusting estimates of the CFR based on symptomatic cases only (sCFR), by adjusting for the non-random nature of testing, and also for the outcome of positive test result cases not being known for some time.
The Imperial studies
The Ferguson et al. study used estimates of the IFR[2] from another paper from the same team, Verity et al. (2020)[3], which had been published a few days earlier on 13 March. Very helpfully, Verity et al., unlike Ferguson et al., published the computer code and data that they used.
The Verity et al. CFR estimates were derived primarily from Chinese data, which reflected non-random testing. The authors obtained age-stratified IFR estimates (in reality, tCFR estimates) by adjusting their CFR estimates using infection prevalence data for expatriates evacuated from Wuhan, all of whom were tested for COVID-19 infection. This approach involves very large uncertainties.
An alternative approach to estimating the tCFR, as a proxy for the IFR, is to use data from a large sample of people, all of whom were tested for the presence of the virus without regard to whether they showed any symptoms, with all who tested positive subsequently being isolated and the case outcome recorded. I use that approach. While the sample of expatriates evacuated from Wuhan is too small for this purpose,[4] occupants of the Diamond Princess cruise ship do provide a suitable such sample.[5] Moreover, the Diamond Princess sample has the advantage that it consists mainly of people from high income countries, and those requiring hospitalisation were treated in such countries.
The Diamond Princess sample may well represent the best available evidence regarding tCFR for older age groups, who are most at risk. Verity et al (2020) did analyse data from the Diamond Princess, but did not use sCFR or tCFR estimates from them for their main CFR and IFR estimates.[6]
The Diamond Princess death toll
When Verity et al. was prepared, the final death toll was not known. The data available only ran to 5 March 2020, at which point 7 passengers had died. The authors therefore used a fitted probability distribution for the delay from testing positive to dying to estimate that those deaths would represent 56% of the eventual death toll. They accordingly therefore estimated the tCFR using a scaled figure of 12.5 deaths.
Here, I adopt the same death rate model and use the same data set, but brought up to date. By 21 March the number of deaths had barely changed, increasing from 7 to 8. Of those 8 deaths, 3 are reported to have been in their 70s and 4 in their 80s. I allocate the remaining, unknown age, person pro rata between those two age groups. As at 21 March the Verity et al. model estimates that 96% of the eventual deaths should have occurred, so we can scale up to 100%, giving an estimated ultimate death toll of 8.34, allocated as to 3.58 to the 70-79 age group and 4.77 to the 80+ age group.
Accordingly, the Verity et al central estimate for the Diamond Princess death toll, of 12.5 eventual deaths, is 50% too high. This necessarily means that the estimates of tCFR and sCFR they derived from it are too high by the same proportion.
Numbers testing positive
The Diamond Princess dataset was published by the Japan National Institute of Infectious Diseases (NIID). I use the second version published on 21 February[7], which gives detailed data for 619 confirmed cases, updating it for subsequent test results.[8] Verity used the original 19 February version of NIID, which gave data for 531 confirmed cases, although they did update it for subsequent test results.
The entire set of passengers and crew, totalling 3711 individuals, was tested for COVID-19. Some 706 (19.0%) ultimately had positive test results, of whom (based on the NIID data for 619 of them) 51% were asymptomatic. The infection rate varied between 10.0% for ages under 30 years to 24.5% for ages 60+ years. The age-distribution was only known for cases included in the NIID data. Verity et al. assumed that the age distribution for the overall total of 706 confirmed cases was the same as for the 531 NIID reported cases that they used. I do the same, but using the later NIID data, with 619 reported cases. On that basis, 201.9, 266.9 and 61.6 people in respectively the 60–69, 70–79 and 80+ key age groups had positive test results.
tCFR estimate
Recall that tCFR is the eventual death toll divided by the total numbers testing positive.
My overall tCFR central estimates from the Diamond Princess 70+ age groups, where all the deaths are taken to have occurred, are 2.54% overall (8.34/328.5),[9] with a breakdown of 1.34% for ages 70-79 (3.58/266.9) and 8.04% (4.77/61.6) for ages 80+. For the 60–69 age group, there are sufficient test-positive occupants to make a crude median estimate of the tCFR, by calculating what it would need to be for there to be a 50% probability that no 60-69 year-old has died, as appears to have been the case. The thus-implied tCFR is 0.34%. There were too few Diamond Princess occupants in age groups below 60 with positive test results to provide any useful information about the COVID-19 tCFR for those groups.
Adjustments for false negatives and underlying death rates
It appears that in about 30% of symptomatic cases the standard RT-PCR test for COVID-19 infection gives a negative result when the patient is in fact infected.[10] There is no evidence of any COVID-19 related deaths among Diamond Princess occupants who tested negative, which would be consistent with a lower viral load being associated with a lower probability both of a positive RT-PCR test result and of eventual death. The false-negative rate may be slightly lower for Diamond Princess occupants, a few of whom may have been retested or tested by a more reliable method where they had typical COVID-19 symptoms but an initially negative RT-PCR test result. However, it seems likely that the proportion of asymptomatic infected cases that are not detected by a RT-PCR test will be somewhat higher than the 30% estimated for symptomatic cases. We accordingly adjust all the tCFR ratios estimated from Diamond Princess case data down by 30% on account of false-negative test results.
The observed deaths of Diamond Princess occupants occurred over a 45 day period, during which a non-negligible percentage of old people would be expected to die from non-COVID-19 related causes. I have accordingly deducted from the adjusted tCFR ratios an allowance for non-COVID-19 deaths for 70+ age groups, based on UK age-stratified 2018 death rates,[11] to arrive at estimates of deaths caused by COVID-19. There are arguments for the non-COVID death rates being either higher or lower than those for the UK population of the same age, but using those death statistics appears to be a reasonable first approximation.
Comparing the Ferguson et al. UK and Diamond Princess based fatality rate estimates
The results of the foregoing analysis are set out in Table 1. The key finding is that the estimated tCFRs for Diamond Princess 60+ age groups, which must if anything overestimate their IFRs, are far lower than the corresponding IFR estimates used by Ferguson et al. in the study adopted by the UK government.[12] Those age groups account for the vast bulk of projected deaths. For people aged 60–69, the Ferguson et al IFR estimate is 19.4 times as high as the best tCFR estimate based on Diamond Princess data, for the 70–79 age group it is 8.3 times as high, and for the 80+ age group it is 2.1 times as high.
Table 1: True Case Fatality Rates estimated from the latest Diamond Princess data compared with Infection Fatality Rates per Ferguson et al. 2019, used by the UK government
Note: An all-causes tCFR of 0.34% (and hence 0.69 notional ultimate fatalities) is assumed for age-group 60-69 despite there being no actual fatalities in that age group (see text). Expected non-COVID-19 fatalities are based on UK 2018 death rates by age group applied to the DP positive test cases, scaled by the 45 day period over which COVID-19 deaths were recorded and divided by the same 0.96 factor used to scale up the 8 actual deaths. DP= Diamond Princess.
Discussion
Based on the Diamond Princess data, the COVID-19 fatality rates by age-group assumed by Ferguson et al. appear to be far too pessimistic for all 60+ age groups, where the vast bulk of fatalities are projected to occur. It is quite possible that they are also too pessimistic for younger age groups as well, but unfortunately the Diamond Princess data are uninformative about death rates below age 60.
It is notable that for all the 60+ age groups the projected excess death rates, based on Diamond Princess case data, caused by COVID-19 is substantially lower than the underlying non-COVID-19 annual death rate. Even assuming, very pessimistically, that there is no overlap between the two, and that the same proportion of each age group becomes infected, projected COVID-19 related deaths from an epidemic in which the vast bulk of the population became infected with COVID-19 are only 9% of expected annual non-COVID deaths for the 60–69 age group.[13] For the 70–79 age group, the proportion is 20%, and for the 80+ age group it is 26%. Relative to the expected non-COVID deaths over two years, the approximate period during which very onerous restrictions are projected to be in force in the UK, these COVID-19 excess death proportions would each be reduced by almost half. In practice, a high proportion of people killed by COVID-19 will have serious underlying health conditions, and would be much more likely than average to die from non-COVID-19 causes.
Nicholas Lewis 25 March 2020
Originally posted here
Update 27 March 2020
Since writing this article it appears that two more people from the Diamond Princess have died, bringing the death toll to 10. Although, as one or two commenters have pointed out, the Worldometer website has for the last day or two been showing 10 deaths, it cites no source for that, the World Health Authority Situation Reports show no change in the number of deaths, and my original searching of the Japanese language Ministry of Health, Labor and Welfare reports showed deaths remaining at 8. However, I have now, using Japanese language keywords, found their reports for 24 to 26 March, which show an increase from 8 to 10 deaths. I have therefore updated my analysis to reflect that increase, as per the revised Table 1 below. No information as to age at death was given, so I have allocated the 2 further deaths pro rata to the 70-79 and 80+ age groups. I have not reduced the scaling up for possible future deaths to reflect the later date, as it appears that the probability distribution that Verity et al. used may not allow sufficiently for deaths occurring more than a month after testing positive for COVID-19. I have also updated the total number of positive test results from 706 to 712, in line with the latest Japanese report.
Although the tCFRs that I estimate for older age groups are now slightly higher, they are still far below the Ferguson et al. estimates. The projected excess COVID-19 deaths over annual UK deaths rates given in the final paragraph become 10%, 27% and 34% for the 60-69, 70-79 and 80+ age groups respectively. Accordingly, my original conclusions are qualitatively unchanged.
Table 1 (revised): True Case Fatality Rates estimated from the latest Diamond Princess data compared with Infection Fatality Rates per Ferguson et al. 2019, used by the UK government
[1] Neil M Ferguson, Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Imperial College COVID-19 Response Team Report 9, 16 March 2020, https://spiral.imperial.ac.uk:8443/handle/10044/1/77482
[2] Ferguson et al. adjusted the Verity et al. IFR estimates “to account for a non-uniform attack rate”, without giving further information about the assumed attack rates. They appear to have increased the Verity et al. IFR estimates for all 60+ age groups by approximately 19%, while making little or no changes to those for younger age groups. It is unclear whether doing so was justified.
[3] 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.
[4] Their sample of evacuated expatriates is 689 people, of whom on 6 tested positive for COVID-19, none of whom died.
[5]Russell et al., “Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship”, medRxiv preprint dated 9 March 2020, did use exclusively Diamond Princess data. However, the early data that they used was incomplete and their IFR (actually tCFR) estimates appear to be based on assuming 26 eventual deaths, and hence are far too high.
[6] Verity et al. simply noted that that figures derived from the Diamond Princess data set were “consistent” with their main estimates, meaning that they fell within their very wide main estimate uncertainty ranges.
[7] Field Briefing: Diamond Princess COVID-19 Cases, 20 Feb Update. National Institute of Infectious Diseases, Japan https://www.niid.go.jp/niid/en/2019-ncov-e/9417-covid-dp-fe-02.html
[8] Using the same sources as given in Verity et al., for dates from 20 February on. Doing so yields a total of 704 positive test results, which I adjust to equal the cumulative total of 706 results stated in the final, 2 March 2020, update.
[9] A simple estimate based on the binomial distribution suggests that the 97.5% upper uncertainty bound is approximately double this figure.
[10] https://www.sciencedaily.com/releases/2020/02/200226151951.htm
[11] https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathregistrationssummarytablesenglandandwalesdeathsbysingleyearofagetables and https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fpopulationestimatesforukenglandandwalesscotlandandnorthernireland%2fmid20182019laboundaries/ukmidyearestimates20182018ladcodes.xls
[12] Moreover, I am unable to reproduce the Ferguson et al. estimate of 510,000 deaths in the ‘Do nothing’ case, based on their estimate of 81% of the population being infected and using their IFRs. Note that the Ferguson et al. IFR estimates assume that, as was the case for the infected Diamond Princess occupants, health systems have not been overwhelmed by COVID-19 cases.
[13] Based on the Ferguson et al. assumption that 81% of the population eventually becomes infected.
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr


Coronavirus  uncertainty

My thoughts on coronavirus and deep uncertainty .
I and my family are in isolation , in relatively comfortable , well-stocked and in safe circumstances ( solar power with Tesla power wall ) .
My community ( Reno NV ) has relatively few cases and no apparent transmission as of yet ; however Las Vegas NV is pretty hard hit as well as neighboring communities in California .
I am very fortunate not have to worry personally about jobs or $ $ through this ( apart from the financial well being of our extended family members and employees at CFAN @ @ @ @ @ @ @ quasi-isolated location near the edge of wilderness , with many walking trails .
I am working to continue supporting the service people that we rely on ( housekeeper , personal trainers , handyman etc ) .
My daughter is working to set up online music lessons , and our Tai Chi instructor is working on setting up online instruction .
Once the weather is nice , both music lessons and and Tai Chi sessions will be held outside in our very large driveway .
I have no idea where this is all headed @ @ @ @ @ @ @ be surprised .
My ‘ prediction ’ ( well , hope ) is that effective treatments will be identified ( I have flagged dozens of such publications on twitter , lets discuss this on the technical thread ) , and that this will be brought under control within a few months .
Personally , my most immediate concern is that Netflix streaming doesn ’ t bring down the internet !
There is much discussion about parallels with decision making under corona virus and climate change .
The climate change alarmists say that CV @ @ @ @ @ @ @ to act urgently on climate change .
Au contraire .
The main similarity between CC and CV are that they are both situations of deep uncertainty .
For CV , we are working from data from countries having the early outbreaks , plus epidemiological models .
Extreme precaution is advised immediately to save lives .
Individual people feel the urgent , visceral need for extreme precaution ( well not all them , viz .
the riotous spring breakers in Florida ) .
The economic and social consequences of this extreme precaution is beginning @ @ @ @ @ @ @ flexibility in our responses so we can shift course as needed .
I have written much on the topic of climate change and the precautionary principle .
Simply put , the precautionary principle is wrong trousers from the climate change problem .
Apart from the brainwashed Extinction Rebellion folk ( assuming that they are doing this not because they are being paid to do it ) , no one feels the urgent visceral need to drop everything and ‘ act ’ on climate change .
The reason for that is that the potential @ @ @ @ @ @ @ long time horizon ( decades to centuries ) , there is no simple ‘ action ’ that will reverse climate change , premature actions could lock us into infrastructure that is not in our best long term interests .
And finally , diversion of all our resources to the climate change problem could make us more vulnerable to more urgent problems such as CV .
This is not to say that we shouldn ’ t act on climate change ; I have long argued for no regrets actions such as energy technology R @ @ @ @ @ @ @ events , planning for 21st century infrastructures , and minimizing pollution of air , water and soils .
However , inferring that the coronavirus policies will lead to comparably urgent climate change action is misguided , IMO .
We will eventually get past this .
What are the long-term implications of our CV experiences ?
Some of them arguably have implications for climate change .
But there are much broader implications for how we live and work , and also the social safety net .
The rate of epidemiological and immunological research that @ @ @ @ @ @ @ ; after all this settles , the old publication model with peer review and paywall will arguably be dead .
The insanity of people travelling nonstop all over the world for conferences and meetings has been illuminated .
Apart from the CO2 emissions , this travelling is an insane loss of productivity and takes a toll on people ’ s health .
Even the IPCC is conducting its working group meetings via the internet .
With regards to scientific conferences , these are being posted on the internet .
These days on the @ @ @ @ @ @ @ I don ’ t attend any talks ( look at them later on the internet ) , but set up meetings with people ( collaborators , old friends , clients and potential clients ) .
That is the hard part , but everyone staying home has more of an opportunity to reach out to people over the internet .
Traveling and tourism will undoubtedly change .
While I have never been on a big cruise trip , I can ’ t imagine getting on such a ship after all this .
I suspect @ @ @ @ @ @ @ to its previous level .
Restaurants will change dramatically , more a focus on pick up and delivery than on dining .
Apparently millennials are already in this mode , rarely going to restaurants but relying on food pick up or delivery .
How we value employees at the lower end of the economic ladder will change , e.g .
grocery store employees , truck drivers , health care workers , teachers , etc .
The CV experience has been a real eye-opener for the U.S. in terms of its fragile safety net @ @ @ @ @ @ @ one is vulnerable at one time or another ) .
Sick leave ( and pay ) , health care are being reconsidered .
Much flux for both political parties in the U.S. ; here ’ s to hoping that something sensible emerges .
The perception of expertise is in flux .
As a result of climate change alarmism ( both scientists and the media ) , scientific expertise took a hit in terms of political and public perceptions .
By contrast , the scientific experts on coronavirus are doing a superb job and @ @ @ @ @ @ @ lessons the climate alarmed and dismissives will eventually learn from this remains to be seen .
With regards to health care , we are seeing the rise of TeleHealth .
Also this experience is highlighting the deep problems with the U.S. FDA ; apart from the COV testing regulations fiasco , the latest is that hydroxychlorquine is not approved for coronavirus treatment in the U.S. ; approval would take 90 days .
CO2 emissions are down , which is likely only temporary , but some of these post CV changes could have longterm @ @ @ @ @ @ @ enough to kick off the discussion .
Stay safe , everyone .

by Judith Curry
My thoughts on coronavirus and deep uncertainty.
I and my family are in isolation, in relatively comfortable, well-stocked and in safe circumstances (solar power with Tesla power wall). My community (Reno NV) has relatively few cases and no apparent transmission as of yet; however Las Vegas NV is pretty hard hit as well as neighboring communities in California.
I am very fortunate not have to worry personally about jobs or $$ through this (apart from the financial well being of our extended family members and employees at CFAN). We also live in a quasi-isolated location near the edge of wilderness, with many walking trails.
I am working to continue supporting the service people that we rely on (housekeeper, personal trainers, handyman etc). My daughter is working to set up online music lessons, and our Tai Chi instructor is working on setting up online instruction. Once the weather is nice, both music lessons and and Tai Chi sessions will be held outside in our very large driveway.
I have no idea where this is all headed, only that I expect us to be surprised.
My ‘prediction’ (well, hope) is that effective treatments will be identified (I have flagged dozens of such publications on twitter, lets discuss this on the technical thread), and that this will be brought under control within a few months.
Personally, my most immediate concern is that Netflix streaming doesn’t bring down the internet!
CV vs CC
There is much discussion about parallels with decision making under corona virus and climate change. The climate change alarmists say that CV shows us how and why we need to act urgently on climate change.
Au contraire. The main similarity between CC and CV are that they are both situations of deep uncertainty.
For CV, we are working from data from countries having the early outbreaks, plus epidemiological models.
Extreme precaution is advised immediately to save lives. Individual people feel the urgent, visceral need for extreme precaution (well not all them, viz. the riotous spring breakers in Florida). The economic and social consequences of this extreme precaution is beginning to be realized; the key is flexibility in our responses so we can shift course as needed.
I have written much on the topic of climate change and the precautionary principle. Simply put, the precautionary principle is wrong trousers from the climate change problem. Apart from the brainwashed Extinction Rebellion folk (assuming that they are doing this not because they are being paid to do it), no one feels the urgent visceral need to drop everything and ‘act’ on climate change. The reason for that is that the potential adverse impacts of climate change have a long time horizon (decades to centuries), there is no simple ‘action’ that will reverse climate change, premature actions could lock us into infrastructure that is not in our best long term interests. And finally, diversion of all our resources to the climate change problem could make us more vulnerable to more urgent problems such as CV.
This is not to say that we shouldn’t act on climate change; I have long argued for no regrets actions such as energy technology R&D, adaptation to extreme weather events, planning for 21st century infrastructures, and minimizing pollution of air, water and soils.
However, inferring that the coronavirus policies will lead to comparably urgent climate change action is misguided, IMO.
Post CV
We will eventually get past this. What are the long-term implications of our CV experiences? Some of them arguably have implications for climate change. But there are much broader implications for how we live and work, and also the social safety net.
The rate of epidemiological and immunological research that is getting conducted and published is breathtaking; after all this settles, the old publication model with peer review and paywall will arguably be dead.
The insanity of people travelling nonstop all over the world for conferences and meetings has been illuminated. Apart from the CO2 emissions, this travelling is an insane loss of productivity and takes a toll on people’s health. Even the IPCC is conducting its working group meetings via the internet.
With regards to scientific conferences, these are being posted on the internet. These days on the rare occasions i go to conferences, I don’t attend any talks (look at them later on the internet), but set up meetings with people (collaborators, old friends, clients and potential clients). That is the hard part, but everyone staying home has more of an opportunity to reach out to people over the internet.
Traveling and tourism will undoubtedly change. While I have never been on a big cruise trip, I can’t imagine getting on such a ship after all this. I suspect that air travel won’t recover to its previous level.
Restaurants will change dramatically, more a focus on pick up and delivery than on dining. Apparently millennials are already in this mode, rarely going to restaurants but relying on food pick up or delivery.
How we value employees at the lower end of the economic ladder will change, e.g. grocery store employees, truck drivers, health care workers, teachers, etc.
The CV experience has been a real eye-opener for the U.S. in terms of its fragile safety net for vulnerable people (pretty much every one is vulnerable at one time or another). Sick leave (and pay), health care are being reconsidered. Much flux for both political parties in the U.S.; here’s to hoping that something sensible emerges.
The perception of expertise is in flux. As a result of climate change alarmism (both scientists and the media), scientific expertise took a hit in terms of political and public perceptions. By contrast, the scientific experts on coronavirus are doing a superb job and politicians are taking them seriously. What lessons the climate alarmed and dismissives will eventually learn from this remains to be seen.
With regards to health care, we are seeing the rise of TeleHealth. Also this experience is highlighting the deep problems with the U.S. FDA; apart from the COV testing regulations fiasco, the latest is that hydroxychlorquine is not approved for coronavirus treatment in the U.S.; approval would take 90 days.
CO2 emissions are down, which is likely only temporary, but some of these post CV changes could have longterm impacts.
Well, this should be enough to kick off the discussion. Stay safe, everyone.
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr


Coronavirus technical thread

Post was not sent - check your email addresses !
Email check failed , please try again Sorry , your @ @ @ @ @ by email .

by Judith Curry
A thread devoted to technical topics, e.g. epidemiology, immunology, treatments. A more general thread will be coming shortly.
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr