Science and politics

“ I ’ m reaching out to scientists this week about the election .
How do you feel about it ?
Which of the candidates has the best plan , for you , in science and technology ?
” The above question was emailed to me today by a reporter .
I am not happy with either the Democratic or Republican plans for science in the U.S .
Both sides seem to want to use and misuse science as a club to further their political agendas .
The Republicans seem to prefer to ignore science , while the Democrats cherry @ @ @ @ @ @ @ .
Here is the long response , some text from something that I ’ m working on : Dutch regulatory lawyer Lucas Bergkamp summarizes the challenge in this way .
Science has become an instrument used by politicians and agencies to arm themselves with powerful arguments in complex value-laden debates .
Scientists have let the politicians hijack the scientific enterprise .
Both policy makers and scientists exploit scientific uncertainty to avoid debate on the relation between science and politics , facts and values .
Armed with science , politicians are able to avoid @ @ @ @ @ @ @ avoided because politicized science has purged doubt and skepticism .
Activist climate science makes use of a series of strategies and tactics to influence public opinion and politics .
Bergkamp concludes that climate science itself has come under siege .
Scientization of policy is a response to intractable political conflicts that transforms the political issues into scientific ones .
The rationale for scientization is the belief that science can act as a neutral arbiter of policy — if we could only determine the facts of a matter , the appropriate course of action @ @ @ @ @ @ @ that science is neither neutral nor capable of answering political questions .
The answers that science gives depend on what questions are asked , which inevitably involves value judgments .
Science is not designed to answer questions about how the world ought to be , which is the work of politics .
Policy makers are culpable in the misuse of science for policy making by : • regarding science as a vehicle to avoid ‘ hot potato ’ policy issues • using scientific facts as a substitute for matters of public concern .
@ @ @ @ @ @ @ science for policy making by : • combining expert knowledge with values that entangle disputed facts with identity-defining group commitments .
In political debates , ‘ I believe in science ’ is a statement generally made by people who don ’ t understand much about it .
They use such statements about science as a way of declaring belief in scientific proposition that is outside their knowledge and understanding .
The belief of such individuals in climate change is often more akin to believing in Santa Claus than relating to actual understanding of @ @ @ @ @ @ @ climate change , ‘ I believe in science ’ uses the overall reputation of science to give authority to the climate change ‘ consensus ’ , shielding it from questioning and skepticism .
‘ I believe in science ’ is a signifier of social group identity that supports massive government legislation to limit or ban fossil fuels .
‘ Belief in science ’ makes it appear that disagreement on this solution is equivalent to a rejection of the scientific method and worldview .
When exposed to science that challenges their political biases , @ @ @ @ @ @ @ to claim ‘ pseudo-science , ’ without considering ( or even understanding ) the actual evidence or arguments .
On the other side of the climate debate , calls for ‘ sound science ’ are made that weaponize uncertainty and rigor to make it more difficult to use science in regulatory decision making .
Individuals promoting ‘ sound science ’ work to amplify uncertainty , create doubt and undermine scientific discoveries that threaten their interests .
The ‘ sound science ’ tactic exploits a fundamental feature of the scientific process : science does @ @ @ @ @ @ @ and subject to change in the face of new evidence .
Encroachment of values into science is unavoidable .
Problems arise when : • Driven by external pressures or for their own political purposes , scientists ignore data and research paths that would make their political point weaker or undermine their ideological perspective .
• Politicians interfere with the activities of science • Narrow framing of the scientific problem by policy makers , whereby government funding draws the efforts of scientists towards a narrow range of projects that supports preferred policies .
• @ @ @ @ @ @ @ even scientists attempt to intimidate or otherwise silence scientists whose research is judged to interfere with their policy preferences .
The war on science is being fought on two fronts : politicians ignoring science ; and using bad science to justify a political agenda .
In 2005 , science journalist Chris Mooney published a best-selling book , The Republican War on Science , which examines the politics of science policy in the U.S .
The book focuses on the behavior of the U.S. Republican party , particularly the administration of President George W. @ @ @ @ @ @ @ Bush administration regularly distorted and/or suppressed scientific research to further its own political aims .
Science journalist John Tierney provides a different perspective from Mooney ’ s , again with reference to U.S. politics .
He correctly states that both sides cherry-pick research and misrepresent evidence to support their agendas , and that stupidity and dishonesty are bipartisan .
He also argues that Conservatives just don ’ t have that much impact on science , asking “ where are the victims ?
” Tierney regards the real dangers to science to come from @ @ @ @ @ @ @ from the Left is confirmation bias , since academics have traditionally leaned left politically .
The second threat from the Left is its long tradition of mixing science and politics .
Leftists have much more confidence in experts and the state , leading to temptations to politicize science .
By contrast , Conservatives are concerned by what Friedrich Hayek called the ‘ fatal conceit : ’ the delusion that experts are wise enough to redesign society .
Conservatives distrust central planners , preferring to protect individuals ’ natural rights .

by Judith Curry
“I’m reaching out to scientists this week about the election. How do you feel about it? Which of the candidates has the best plan, for you, in science and technology?”
The above question was emailed to me today by a reporter.
My response:
I am not happy with either the Democratic or Republican plans for science in the U.S. Both sides seem to want to use and misuse science as a club to further their political agendas. The Republicans seem to prefer to ignore science, while the Democrats cherry pick science to further their political agendas.
Here is the long response,some text from something that I’m working on:
Dutch regulatory lawyer Lucas Bergkamp summarizes the challenge in this way. Science has become an instrument used by politicians and agencies to arm themselves with powerful arguments in complex value-laden debates. Scientists have let the politicians hijack the scientific enterprise. Both policy makers and scientists exploit scientific uncertainty to avoid debate on the relation between science and politics, facts and values. Armed with science, politicians are able to avoid accountability for decisions. Serious debate is avoided because politicized science has purged doubt and skepticism. Activist climate science makes use of a series of strategies and tactics to influence public opinion and politics. Bergkamp concludes that climate science itself has come under siege.
Scientization of policy is a response to intractable political conflicts that transforms the political issues into scientific ones. The rationale for scientization is the belief that science can act as a neutral arbiter of policy — if we could only determine the facts of a matter, the appropriate course of action would become clear. The problem is that science is neither neutral nor capable of answering political questions. The answers that science gives depend on what questions are asked, which inevitably involves value judgments. Science is not designed to answer questions about how the world ought to be, which is the work of politics.
Policy makers are culpable in the misuse of science for policy making by:
regarding science as a vehicle to avoid ‘hot potato’ policy issues
expecting black-and-white answers to complex problems
demanding scientific arguments for their desired policies
using scientific facts as a substitute for matters of public concern.
Scientists are culpable in the misuse of science for policy making by:
naivete about expecting scientific evidence to drive policy
conflating evidence with expert judgment
playing power politics with their expertise
combining expert knowledge with values that entangle disputed facts with identity-defining group commitments.
In political debates, ‘I believe in science’ is a statement generally made by people who don’t understand much about it. They use such statements about science as a way of declaring belief in scientific proposition that is outside their knowledge and understanding. The belief of such individuals in climate change is often more akin to believing in Santa Claus than relating to actual understanding of science.
In the context of the climate change, ‘I believe in science’ uses the overall reputation of science to give authority to the climate change ‘consensus’, shielding it from questioning and skepticism. ‘I believe in science’ is a signifier of social group identity that supports massive government legislation to limit or ban fossil fuels. ‘Belief in science’ makes it appear that disagreement on this solution is equivalent to a rejection of the scientific method and worldview. When exposed to science that challenges their political biases, these same ‘believers’ are quick to claim ‘pseudo-science,’ without considering (or even understanding) the actual evidence or arguments.
On the other side of the climate debate, calls for ‘sound science’ are made that weaponize uncertainty and rigor to make it more difficult to use science in regulatory decision making. Individuals promoting ‘sound science’ work to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. The ‘sound science’ tactic exploits a fundamental feature of the scientific process: science does not produce absolute certainty but is provisional and subject to change in the face of new evidence.
Encroachment of values into science is unavoidable. Problems arise when:
Driven by external pressures or for their own political purposes, scientists ignore data and research paths that would make their political point weaker or undermine their ideological perspective.
Politicians interfere with the activities of science
Narrow framing of the scientific problem by policy makers, whereby government funding draws the efforts of scientists towards a narrow range of projects that supports preferred policies.
Politicians, advocacy groups, journalists and even scientists attempt to intimidate or otherwise silence scientists whose research is judged to interfere with their policy preferences.
The war on science is being fought on two fronts: politicians ignoring science; and using bad science to justify a political agenda.
In 2005, science journalist Chris Mooney published a best-selling book, The Republican War on Science, which examines the politics of science policy in the U.S. The book focuses on the behavior of the U.S. Republican party, particularly the administration of President George W. Bush. The book argues that the Bush administration regularly distorted and/or suppressed scientific research to further its own political aims.
Science journalist John Tierney provides a different perspective from Mooney’s, again with reference to U.S. politics. He correctly states that both sides cherry-pick research and misrepresent evidence to support their agendas, and that stupidity and dishonesty are bipartisan. He also argues that Conservatives just don’t have that much impact on science, asking “where are the victims?”
Tierney regards the real dangers to science to come from the political Left. The first threat from the Left is confirmation bias, since academics have traditionally leaned left politically. The second threat from the Left is its long tradition of mixing science and politics. Leftists have much more confidence in experts and the state, leading to temptations to politicize science. By contrast, Conservatives are concerned by what Friedrich Hayek called the ‘fatal conceit:’ the delusion that experts are wise enough to redesign society. Conservatives distrust central planners, preferring to protect individuals’ natural rights.
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr


climateetc--2020-10-16--Climate science and the Supreme Court

Climate science and the Supreme Court

An alternative assessment of U.S. Supreme Court Justice nominee Amy Coney Barrett ’ s statements on climate change .
For those of you not in the U.S. , confirmation hearings on the nomination of Amy Coney Barrett for the Supreme Court are currently underway .
There are many very political issues surrounding this nomination and its timing .
Lets put all that aside for the moment , and consider her statements on climate change .
“ I will not express a view on a matter of public policy , especially one that is politically controversial.
” “ I don ’ @ @ @ @ @ @ @ or global warming are relevant to the job I would do as a judge .
Nor do I feel like I have views that are informed enough.
” “ I ’ m certainly not a scientist , ” she said when asked by Sen. John Kennedy ( R-La . )
whether she had a personal opinion on the issue .
“ I mean , I ’ ve read things about climate change .
I would not say I have firm views on it.
” “ I don ’ t think I ’ m @ @ @ @ @ @ @ warming or not.
” The twitterati are hysterical over these statements .
From a Washington Post article : “ The judge ’ s exchange on climate change was short , but her critics say it is disqualifying ” An article in the Esquire is entitled : Amy Coney Barrett ’ s answer on this climate change question is completely disqualifying .
“ Put simply , this is just totally disqualifying for any official holding public office in the year 2020 .
This isn ’ t even an up-to-date Republican bullshit line on the @ @ @ @ @ @ @ a scientist ” is so 2014 , maybe because even the Elite Political Media—pockets of which are just today allowing themselves to be hoodwinked by another Emails caper—caught on to how dumb it is .
Does Judge Amy Coney Barrett accept the scientific consensus that gravity is keeping her in that chair ?
If so , why ?
She ’ s not a scientist , so how could she possibly know ?
” There are two issues here that deserve discussion : • Whether ‘ belief ’ in climate change actually means anything @ @ @ @ @ @ @ • What judges should be expected to know about climate science .
I think that Amy Coney Barrett ’ s answers to the climate question was admirable .
She wanted to stay out of a contentious political debate .
But more importantly she wasn ’ t going to pass a judgement on something for which she had not carefully evaluated the evidence and did not find herself qualified to make a judgement on .
I thought her stance on this showed wisdom and humility .
In the 2016 presidential debates , Hillary Clinton @ @ @ @ @ @ @ science ” , with specific reference to climate change .
In the political debate on climate change , ‘ I believe in climate science ’ is a statement generally made by people who don ’ t understand much about it .
They use such statements as a way of declaring belief in a scientific proposition that is outside their knowledge and understanding .
The belief of individuals making such a statement is often more akin to believing in Santa Claus than relating to actual understanding of science .
In the case of Hillary @ @ @ @ @ @ @ U.S. Democratic National Convention , Clinton ’ s appeal to science was a partisan rallying cry that was coupled to the mockery of Donald Trump and his supporters as ‘ anti-science.
’ In the context of the climate change , ‘ I believe in science ’ uses the overall reputation of science to give authority to the climate change ‘ consensus ’ , shielding it from questioning and skepticism .
‘ I believe in climate science ’ is a signifier of social group identity that supports one particular solution : massive government legislation @ @ @ @ @ @ @ ‘ Belief in climate science ’ makes it look as though disagreement on this solution is equivalent to a rejection of the scientific method and worldview .
When exposed to science that challenges their political biases , these same ‘ believers ’ are quick to claim ‘ pseudo-science , ’ without considering ( or even understanding ) the actual evidence or arguments .
An excellent summary of all this is provided in a previous blog post discussing an article by Robert Tracinski .
In my albeit limited experience , very few politicians have @ @ @ @ @ @ @ science , beyond being able to parrot talking points provided to them by advocacy groups .
Here is what we are left with : One side attacks science and the other side uses science for political attacks .
Neither side actually cares about or understands the science .
Kudos to Amy Coney Barrett for providing an appropriate answer to the climate change questions A New York Times article discusses why judge ’ s ‘ opinions ’ on climate change are relevant to the Supreme Court .
The EPA endangerment finding may be facing @ @ @ @ @ @ @ .
There are also lawsuits against the U.S. government and oil companies that could make it the Supreme Court .
The Wikipedia has a good overview on the Juliana case against the U.S. government as well as previous cases .
Apart from procedural issues , I don ’ t see what kind of ruling by the Supreme Court on climate change that would hinge on the Justices ’ understanding or ruling on details of the science .
The Dutch Urgenda ruling accepted the authority of the IPCC assessment reports .
This was an @ @ @ @ @ @ @ system , which leaves matters of policy to the legislative and executive branches .

by Judith Curry
An alternative assessment of U.S. Supreme Court Justice nominee Amy Coney Barrett’s statements on climate change.
For those of you not in the U.S., confirmation hearings on the nomination of Amy Coney Barrett for the Supreme Court are currently underway. There are many very political issues surrounding this nomination and its timing. Lets put all that aside for the moment, and consider her statements on climate change.
Barrett’s statements [link]:
“I will not express a view on a matter of public policy, especially one that is politically controversial.”
“I don’t think my views on climate change or global warming are relevant to the job I would do as a judge. Nor do I feel like I have views that are informed enough.”
“I’m certainly not a scientist,” she said when asked by Sen. John Kennedy (R-La.) whether she had a personal opinion on the issue. “I mean, I’ve read things about climate change. I would not say I have firm views on it.”
“I don’t think I’m competent to opine on what causes global warming or not.”
The twitterati are hysterical over these statements. From a Washington Post article:
“The judge’s exchange on climate change was short, but her critics say it is disqualifying”
“It is a requirement that a Supreme Court Justice be able to review evidence to make a decision,” he said. “The scientific evidence of climate change is beyond reasonable doubt or debate, yet Amy Coney Barrett refused to acknowledge reality.”
“A climate change case is already on the Supreme Court’s docket next year. It will hear a case involving several oil companies, including Dutch Royal Shell, being sued by the city of Baltimore, which is seeking to hold them financially responsible for their greenhouse gas contributions. Barrett’s father spent much of his own career as a lawyer for Shell. “
An article in the Esquire is entitled: Amy Coney Barrett’s answer on this climate change question is completely disqualifying.
“Put simply, this is just totally disqualifying for any official holding public office in the year 2020. This isn’t even an up-to-date Republican bullshit line on the topic. “I’m not a scientist” is so 2014, maybe because even the Elite Political Media—pockets of which are just today allowing themselves to be hoodwinked by another Emails caper—caught on to how dumb it is. Does Judge Amy Coney Barrett accept the scientific consensus that gravity is keeping her in that chair? If so, why? She’s not a scientist, so how could she possibly know?”
There are two issues here that deserve discussion:
Whether ‘belief’ in climate change actually means anything when spouted by politicians and other non-scientists What judges should be expected to know about climate science.
“I believe in climate science”
I think that Amy Coney Barrett’s answers to the climate question was admirable. She wanted to stay out of a contentious political debate. But more importantly she wasn’t going to pass a judgement on something for which she had not carefully evaluated the evidence and did not find herself qualified to make a judgement on. I thought her stance on this showed wisdom and humility.
In the 2016 presidential debates, Hillary Clinton said: “And I believe in science” , with specific reference to climate change.
In the political debate on climate change, ‘I believe in climate science’ is a statement generally made by people who don’t understand much about it. They use such statements as a way of declaring belief in a scientific proposition that is outside their knowledge and understanding. The belief of individuals making such a statement is often more akin to believing in Santa Claus than relating to actual understanding of science. In the case of Hillary Clinton’s acceptance speech at the U.S. Democratic National Convention, Clinton’s appeal to science was a partisan rallying cry that was coupled to the mockery of Donald Trump and his supporters as ‘anti-science.’
In the context of the climate change, ‘I believe in science’ uses the overall reputation of science to give authority to the climate change ‘consensus’, shielding it from questioning and skepticism. ‘I believe in climate science’ is a signifier of social group identity that supports one particular solution: massive government legislation to limit or ban fossil fuels. ‘Belief in climate science’ makes it look as though disagreement on this solution is equivalent to a rejection of the scientific method and worldview. When exposed to science that challenges their political biases, these same ‘believers’ are quick to claim ‘pseudo-science,’ without considering (or even understanding) the actual evidence or arguments. An excellent summary of all this is provided in a previous blog post discussing an article by Robert Tracinski.
In my albeit limited experience, very few politicians have made a serious attempt to understand climate science, beyond being able to parrot talking points provided to them by advocacy groups.
Here is what we are left with: One side attacks science and the other side uses science for political attacks. Neither side actually cares about or understands the science.
Kudos to Amy Coney Barrett for providing an appropriate answer to the climate change questions
Supreme Court
A New York Times article discusses why judge’s ‘opinions’ on climate change are relevant to the Supreme Court. The EPA endangerment finding may be facing a challenge in a future Republican administrations. There are also lawsuits against the U.S. government and oil companies that could make it the Supreme Court.
The Wikipedia has a good overview on the Juliana case against the U.S. government as well as previous cases. Apart from procedural issues, I don’t see what kind of ruling by the Supreme Court on climate change that would hinge on the Justices’ understanding or ruling on details of the science.
The Dutch Urgenda ruling accepted the authority of the IPCC assessment reports. This was an unusual ruling based upon the U.S. court system, which leaves matters of policy to the legislative and executive branches.
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr


climateetc--2020-10-14--T cell cross-reactivity and the Herd immunity threshold

An interesting new paper by Marc Lipsitch and co-authors , “ Cross-reactive memory T cells and herd immunity to SARS-CoV-2 ” , has recently been published .
[ 1 ] It discusses immunological and epidemiological aspects and implications of pre-existing cross-reactive adaptive immune system memory arising from previous exposure to circulating common cold coronaviruses .
They argue that key potential impacts of cross- reactive T cell memory are already incorporated into epidemiological models based on data of transmission dynamics , particularly with regard to their implications for herd immunity .
I believe that they are mistaken on the herd immunity @ @ @ @ @ @ @ this article .
The first point to make is that cross-reactive T cells were never thought to be the main cause of the herd immunity threshold ( HIT ) [ 2 ] being lower for COVID-19 than the oft-quoted { 1 – 1/R0 } level , which generally applies for vaccination .
Heterogeneity in social connectivity ( contact rates ) is typically estimated to lower the HIT much more than heterogeneity in biological susceptibility to the causative SARS-CoV-2 virus .
[ 3 ] Possible effects of cross-reactive T cells on infection progression Lipsitch @ @ @ @ @ @ @ shown that SARS- CoV-2 cross-reactive memory T cells , very largely CD4+ T-cells arising from previous exposure to circulating common cold coronaviruses , are detectable in ~28–50 % of individuals not exposed to SARS- CoV-2 .
They say that only tissue-resident memory T cells ( T cells ) can mount a fast response , with recirculating T and T T cells taking several days to start fighting an infection .
They point out that CD4+ T-cells generally limit disease severity , reduce the viral burden and/or limit the duration of the disease rather @ @ @ @ @ @ @ I do not intend to challenge any of the foregoing points here , it should be noted that they treat an ‘ infection ’ as including a case where so few cells have been infected that any ( RT- ) PCR test for the virus would be negative .
The paper states that CD4+ T cell-mediated memory responses to a virus may involve some or all of CD4+ T cell types ( required for B cell help and thus almost all neutralizing antibody responses ) , T 1 and CTL cell types ( @ @ @ @ @ @ @ ) , and that the CD4+ T cells involved may be T cells , or slower to respond recirculating T /T cells .
The authors go on to propose four immunological scenarios for the impact of cross-reactive CD4+ memory T cells on COVID-19 severity and viral transmission .
The four model scenarios they put forward are : • Reduction of lung burden : CD4+ T cells reduce COVID-19 symptoms and lung viral load but have minimal impact on upper respiratory tract ( URT ) viral load .
• TFH cell-accelerated antibodies : CD4+ @ @ @ @ @ @ @ antibody response , resulting in accelerated control of virus in the URT and lungs .
• TRM cells in the URT : CD4+ T cells at the site of infection enable rapid control of virus in the URT and lungs .
• Transient infection : T cell immunity ‘ blitzes ’ viral replication in the URT leading to the elimination of all infected cells within a day of the initial infection , at the portal of entry .
The first three scenarios , along with the case where no cross-reactive T cells exist @ @ @ @ @ @ @ paper , reproduced below .
Which models do the data fit ?
The authors argue that biological evidence implies model scenario 4 is very unlikely where only CD4+ T cells are involved .
They point out that if pre-existing CD4+ T cell immunity was so extreme as to preclude significant viral replication , seroconversion ( that is , a de novo antibody response to SARS- CoV-2 ) would not occur .
Such individuals would not be detectable by virological ( e.g.
, PCR ) or serological diagnostic tests and would not shed virus @ @ @ @ @ @ @ immune to infection and not reported as cases .
The authors say that evidence for other human coronaviruses makes this implausible , and that when epidemiological evidence of very high attack rates in some ship-based outbreaks is added scenario 4 is highly unlikely .
However , in the most studied ship-based outbreak the proportion infected was under 20 % .
[ 4 ] Moreover , the results of a study that Lipsitch et al .
do not cite [ 5 ] show that , in households where one person was confirmed as having @ @ @ @ @ @ @ household members had negative PCR test results , implying that they were not infective , despite most of them having typical COVID-19 symptoms .
Moreover , these individuals did not develop detectable SARS-CoV-2 specific antibodies , but did develop SARS-CoV-2 specific ( as opposed to cross reactive ) T cell responses , implying that they had been infected to some degree by SARS-CoV-2 .
Notwithstanding that the sample size was small in that study , it appears to cast some doubt on Lipsitch et al.
’ s assertion that scenario 4 is highly @ @ @ @ @ @ @ their subsequent assertion that almost all people infected by SARS-CoV-2 seroconvert ( develop antibodies against it ) , although the test used might have been insufficiently sensitive to detect low antibody levels .
In that connection , Lipsitch et al .
say that a recent study [ 6 ] observed [ only ] about 3 cases of non-PCR confirmed potentially asymptomatic COVID-19 cases with T cell responses in the absence of seroconversion , but their interpretation of that study ’ s results has been challenged .
[ 7 ] A substantial proportion of @ @ @ @ @ @ @ outbreaks , the vast majority of them – have asymptomatic infections .
In the most studied ship-based outbreak4 almost half of infected individuals remained asymptomatic throughout .
[ 8 ] If that is due to T-cell cross reactivity , acting in combination with innate immune responses , then only model scenarios 3 or 4 would fit , since model scenarios 1 and 2 imply significant symptoms .
In the remainder of this article I will not pursue the possibility of model scenario 4 being relevant .
Rather , I will focus on showing @ @ @ @ @ @ @ model scenario 3 ( possibly involving also model scenario 2 ) , as varied to take account of variation in viral dose and innate immune system strength , are very likely not already taken into account in simple epidemiological models based on transmission dynamics data .
In this connection , it should be noted that the extent and quality of the available data , both biological and epidemiological , does not provide high quality evidence , so drawing firm conclusions either way is difficult .
Importantly , there is quite strong evidence that @ @ @ @ @ @ @ if they are asymptomatic ( and not just presymptomatic ) .
Biological evidence neither proves nor disproves that a positive PCR test for SARS-CoV-2 implies significant infectivity ( although a negative PCR test can be taken as implying a lack of significant infectivity ) .
[ 9 ] However , epidemiological evidence strongly suggests that transmission by asymptomatic individuals is far lower than that by symptomatic or presymptomatic individuals .
A number of studies have investigated transmission from index cases who remained asymptomatic throughout their infections .
A review study [ 10 ] @ @ @ @ @ @ @ rate from asymptomatic cases was only 3.5 % of that from symptomatic cases .
As that study noted , household secondary attack rate provides a useful estimate of both the susceptibility of contacts and infectiousness of index cases .
However , both in that study , and in another review study [ 11 ] that estimated a much higher ratio than 3.5 % , the statistical analysis appears to be seriously flawed .
[ 12 ] It is therefore necessary to consider the actual results of the relevant original studies that they reviewed @ @ @ @ @ @ @ ] [ 14 ] found no instances of asymptomatic transmission , although the number of contacts concerned was very small in one case .
Two other studies each found one case of asymptomatic transmission , out of respectively 305 and 119 contacts , [ 15 ] [ 16 ] with corresponding relative risks of 6 % and 19 % .
Averaging the risk ratios of all four studies , in a way that gives appropriate weight to the evidence each provides , gives an overall transmission risk ratio estimate of 8 % for @ @ @ @ @ @ @ presymptomatic cases .
[ 17 ] That is , an asymptomatic infected person , as in Lipsitch et al .
model scenario 3 , appears to be only one-tenth or less as likely to transmit the virus as does a symptomatic or presymptomatic person .
This conclusion does not have to depend on asymptomatic infectees having a much lower viral load in their URT , which may not be the case .
They could for instance transmit less because of an absence of coughing ( particularly that involving expectoration15 ) , less deep @ @ @ @ @ @ @ more of their PCR-measured viral load does not represent viable viruses ; and/or because their viral load remains at an infective level for a shorter period .
The likely importance of viral dose ( inoculum ) and innate immune responses Two factors that Lipsitch et al .
do not include in their model scenarios , but which seem likely to be very relevant , are the magnitude of the viral dose and the strength of a person ’ s fast responding innate immune system .
Both the symptoms , as to their likelihood @ @ @ @ @ @ @ an individual exposed to SARS-CoV-2 are expected to depend on the viral dose that their exposure involves [ 18 ] [ 19 ] and on the strength of their innate immune system , as well as on any cross-reactive T cell and/or antibody adaptive immune system memory .
I will concentrate here on differences arising from the interaction between viral dose and cross-reactive T cells , but in reality differences in innate immune system strength , and in general health and other factors , will likewise affect how somebody reacts to viral doses @ @ @ @ @ @ @ they become ill and/or infective .
It seems entirely possible that where the viral dose is sufficiently low , a person with cross reactive CD4+ T cells might either be infected so little that – whether or not a PCR test would be positive , at a sufficiently large cycle threshold ( high sensitivity ) – they not only remain asymptomatic but also have negligible infectivity .
In effect , given a sufficiently low viral dose , Lipsitch et al.
’ s model scenario 3 might produce rather similar effects to what their @ @ @ @ @ @ @ high viral dose .
It appears , for most purposes , to be inappropriate to regard such a non-infective , healthy person as a COVID-19 case or even as being infected at all .
[ 23 ] Such persons are accordingly treated here as not having been infected .
However , it appears possible that a low viral load might , without resulting in symptoms or non-negligible infectivity , nevertheless induce effective immunity through the development of SARS-CoV-2 specific antibodies and/or T cells .
By contrast , Lipsitch et al .
appear to @ @ @ @ @ @ @ if only a single cell in their body has been invaded by a virus .
While this definition may be logical from a technical biological angle , it does not seem appropriate from an epidemiological viewpoint .
For epidemiological purposes , what is relevant is whether and to what extent a person is or will become ill , infective and/or immune .
Where the viral dose is fairly high , even if a person has cross reactive CD4+ T cells , they would almost certainly test PCR positive , and be more likely @ @ @ @ @ @ @ and 3 in the paper may both be relevant in these cases .
While in such a case the person concerned would be infective , albeit much less so if asymptomatic , if they had cross reactive CD4+ T cells they would probably be considerably less infective ( and be much more likely to be asymptomatic or to have mild symptoms ) .
Insofar as infected individuals are asymptomatic and of low ( but non-negligible ) infectivity , it may be that in cases where they do transmit infection the viral dose is @ @ @ @ @ @ @ infected also to be asymptomatic and of low infectivity , in which case such asymptomatic transmission will contribute to the gradual spread of immunity while not leading to disease .
[ 24 ] Modelling the effects of varying susceptibility and infectivity arising from cross-reactive T cells I have built a greatly simplified toy model that illustrates the possible implications for epidemic progression and herd immunity of cross-reactive T cells that have the effects discussed in this article .
The model stratifies the population into two equal parts , one possessing cross-reactive T cells @ @ @ @ @ @ @ symptomatic and asymptomatic infections , the latter having only one-ninth as high a probability of causing infection as the former .
The detailed assumptions made in the model are set out in an Appendix .
While these assumptions are purely illustrative , they are intended to be broadly consistent with existing evidence and the foregoing discussion in this article .
The key assumptions regarding the effects of cross-reactive T cells are that their presence halves the risk of infection from a potentially infective contact , quarters the probability of any infection being symptomatic @ @ @ @ @ @ @ in a substantial proportion of those cases where infection does not occur .
The modelled epidemic is seeded by the symptomatic infection of one naïve individual ( a person without cross-reactive T cells ) .
The number of close contacts per generation is then adjusted to produce , after the epidemic has adjusted from the initial seeding pattern to its natural pattern , a reproduction number early in the epidemic – which will therefore closely approximate R0 – of 2.4 .
The toy model ’ s projections show that , after initial exponential @ @ @ @ @ @ @ , indicating that herd immunity has been achieved .
At that point , 41 % of the population has been infected , with approximately 43 % of infections being asymptomatic .
At 41 % , the HIT is slightly over two-thirds of the classical HIT level for a homogeneous population , being 58 % .
[ 25 ] A further 20 % of the population will have become immune without , for all practical purposes , having had an infection .
If on the other hand no exposed but uninfected ( i.e.
, @ @ @ @ @ @ @ , then the HIT is closer to the classical level , but still lies more than 10 % below it .
If the probability of being infected is reduced by 85 % in the presence of cross-reactive T cell memory , the HIT could be one-third below the classical HIT even if no exposed but uninfected person develops immunity .
It is not suggested that such a large differential is likely .
However , it does prove that cross-reactive T cell memory , in combination with varying viral dose ( and innate immune @ @ @ @ @ @ @ substantially lower herd immunity threshold than that estimated from data earlier in the epidemic using homogeneous population compartmental SIR/SEIR models , as is routinely done .
A more realistic model would incorporate continuous probability distributions for all the key parameters .
But the basic point illustrated by the very simple model would remain valid .
Homogeneous population based compartmental models imply that epidemic growth will slow pro rata to the shrinking pool of uninfected people .
But where there is variation within the population as to how susceptible people are to infection , @ @ @ @ @ @ @ average infected earlier , the epidemic growth is bound to reduce more rapidly than that .
As a result , the herd immunity threshold will be lower than if the population were homogeneous , with the reduction of the HIT being greater if less biologically susceptible individuals also have , if infected , lower biological infectivity .
I have demonstrated that the claim by Lipsitch et al .
that the potential impacts on the herd immunity threshold of cross- reactive T cell memory are already incorporated into epidemiological models based on data of @ @ @ @ @ @ @ that they are correct in arguing that their model scenario 4 is highly implausible .
In this article I have only considered the possible effects of cross-reactive T cells .
However , even when combined with other causes of interpersonal variation in biological susceptibility , including age , such heterogeneity is not thought to be the main reason why the herd immunity threshold will be lower than the classical level for a homogeneous population .
In practice , interpersonal variation in contact rates ( social connectivity ) is usually thought to be a @ @ @ @ @ @ @ – Assumptions made in the toy model of the effects of cross-reactive T cells • The population is one million and is homogeneous except that only 50 % of people have cross-reactive memory T-cells .
• The generation interval is fixed , infected individuals are only infectious in the generation interval after they become infected and are uninfectious and immune thereafter .
• Infections are by close contact only .
The number of close contacts by an infected person is independent of their T cell status and whether or not their infection is @ @ @ @ @ @ @ each person who becomes infected has only had one contact with an infectious person during the generation interval in which they become infected .
• A close contact between a symptomatic ( including presymptomatic ) infectee and a naïve individual ( one without cross-reactive T cells ) results in infection 90 % of the time , with 80 % of such infections being symptomatic , due to a high average viral dose being involved .
• A close contact between an asymptomatic infectee and a naïve individual results in infection 10 % of @ @ @ @ @ @ @ such infections being symptomatic , the viral dose being lower .
• A close contact between a symptomatic infectee and a resistant individual ( one with cross-reactive T cells ) results in infection 45 % of the time , with 20 % of such infections being symptomatic .
• A close contact between an asymptomatic infectee and a resistant individual results in infection 5 % of the time , with 5 % of such infections being symptomatic .
• Where such a low viral dose is transmitted on a close contact that a @ @ @ @ @ @ @ but is completely non-infective , they are treated as not being infected but ( except if stated otherwise ) in 60 % of such cases they nevertheless become immune .
[ 1 ] Marc Lipsitch , Yonatan H. Grad , Alessandro Sette and Shane Crotty : Cross-reactive memory T cells and herd immunity to SARS-CoV-2 .
Nature Reviews Immunology 6 October 2020 https : //doi.org/10.1038/s41577-020-00460-4 [ 2 ] The herd immunity threshold is the proportion of the population that have become infected at the point where each new infection causes , on average @ @ @ @ @ @ @ .
For an epidemic in a homogeneous population , it will be { 1 – 1/R0 } , where R0 is the basic ( initial ) reproduction number .
[ 3 ] e.g.
, Tkachenko , A.V .
et al .
: Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19 .
medRxiv 29 July 2020 https : //doi.org/10.1101/2020.07.26.20162420 [ 4 ] The Diamond Princess .
712 out of 3,711 on board tested PCR-positive , with at least 295 and probably closer to 334 cases ( 295 from Tokyo plus 40 returned @ @ @ @ @ @ @ died from COVID-19 ) remaining asymptomatic throughout their infection .
https : //www.mhlw.go.jp/stf/newpage_11441.html [ 5 ] Gallais , F. , Velay , A. , Wendling , M.J. , Nazon , C. , Partisani , M. , Sibilia , J. , Candon , S. and Fafi-Kremer , S. , 2020 .
Intrafamilial exposure to SARS-CoV-2 induces cellular immune response without seroconversion .
MedRxiv .
https : //www.medrxiv.org/content/medrxiv/early/2020/06/22/2020.06.21.20132449.full.pdf [ 6 ] Sekine , T. et al .
Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19 .
Cell https : //doi.org/10.1016/j.cell.2020.08.017 ( @ @ @ @ @ @ @ is consistent with the findings of Sakurai et al.
, Natural History of Asymptomatic SARS-CoV-2 Infection .
New England Journal of Medicine .
2020 Jun 12 https : //www.nejm.org/doi/full/10.1056/NEJMc2013020 [ 10 ] Madewell , Z.J.
, Yang , Y. , Longini Jr , I.M.
, Halloran , M.E .
and Dean , N.E.
, 2020 .
Household transmission of SARS-CoV-2 : a systematic review and meta-analysis of secondary attack rate .
medRxiv .
[ 1 August version ] [ 11 ] Buitrago-Garcia D , et al .
( 2020 ) Occurrence and transmission @ @ @ @ @ @ @ : A living systematic review and metaanalysis .
PLoS Med 17 ( 9 ) : e1003346 .
https : //doi.org/10.1371/journal.pmed.1003346 [ 12 ] Madewell et al .
included a study where the secondary attack case rather than the index case was asymptomatic .
Buitrago-Garcia D , et al.
, who estimated a relative risk of 0.35 for asymptomatic transmission , included in that estimate a study of presymptomatic transmission ( their reference 111 ) and an estimate based on combined asymptomatic and presymptomatic transmission ( from their reference 65 ) , and they @ @ @ @ @ @ @ from studies that found zero cases of it .
[ 13 ] Cheng HY , Jian SW , Liu DP , Ng TC , Huang WT , Lin HH , et al .
Contact Tracing Assessment of Covid-19 Transmission Dynamics in Taiwan and Risk at Different Exposure Periods before and after Symptom Onset .
JAMA Intern Med .
2020 .
Epub 2020/05/02 .
https : //doi.org/10.1001/jamainternmed.2020 .
[ 15 ] Luo L , Liu D , Liao X-l , Wu X-b , Jing Q-l , Zheng J-z , et al .
Modes of @ @ @ @ @ @ @ among Close Contacts .
bioRxiv .
2020 [ March 26 version ] https : //www.medrxiv.org/content/10.1101/2020.03.24.20042606v1 [ 16 ] Zhang W , Cheng W , Luo L , Ma Y , Xu C , Qin P , et al .
Secondary Transmission of Coronavirus Disease from Presymptomatic Persons , China .
Emerg Infect Dis .
2020. https : //doi.org/10.3201/eid2608.201142 [ 17 ] Averaging over the four studies , weighting by the number of contacts by asymptomatic index cases , gives a pooled relative risk estimate of 8.2 % .
A more sophisticated meta-analysis using @ @ @ @ @ @ @ ) function in the rmeta package estimates a pooled study relative risk of 7.9 % , with a 75 % probability that it does not exceed 13 % and a 90 % probability that it does not exceed 20 % ( assuming that the confidence intervals are symmetrical ) .
[ 18 ] Steinmeyer , Shelby H. , Claus O. Wilke , and Kim M. Pepin .
“ Methods of modelling viral disease dynamics across the within-and between-host scales : the impact of virus dose on host population immunity.
” Philosophical Transactions of @ @ @ @ @ @ @ 365.1548 ( 2010 ) : 1931-1941 .
[ 19 ] Goyal , Ashish , et al .
“ Wrong person , place and time : viral load and contact network structure predict SARS-CoV-2 transmission and super-spreading events.
” medRxiv ( 2020 ) [ 7 August version ] .
[ 20 ] Hibino , Sawako , et al .
“ Dynamic Change of COVID-19 Seroprevalence among Asymptomatic Population in Tokyo during the Second Wave.
” medRxiv ( 2020 ) .
[ 23 September version ] [ 21 ] Gandhi , Monica , Chris Beyrer @ @ @ @ @ @ @ do more than protect others during COVID-19 : Reducing the inoculum of SARS-CoV-2 to protect the wearer.
” Journal of general internal medicine ( 2020 ) : 1-4 .
[ 22 ] Gandhi , Monica , and George W. Rutherford .
“ Facial Masking for Covid-19—Potential for “ Variolation ” as We Await a Vaccine.
” New England Journal of Medicine ( 2020 ) .
[ 23 ] While such cases might give a positive result on a PCR test at some point , such a test result might represent detection only of @ @ @ @ @ @ @ viral load that is too low to be infective .
[ 24 ] Unfortunately there are too few recorded cases of asymptomatic transmission to provide reliable evidence on this point , but what evidence does appear to exist is consistent with this argument .
Zhang et al .
identified one case of asymptomatic transmission , which resulted in an asymptomatic infection , while 73 % of the eleven cases of symptomatic or presymptomatic transmission they identified resulted in a symptomatic infection .
( Luo et al .
unfortunately did not indicate the symptom @ @ @ @ @ @ @ they identified . )
[ 25 ] For an R0 of 2.4 , the classical herd immunity threshold is { 1 – 1/2.4 } = 0.583

By Nic Lewis
An interesting new paper by Marc Lipsitch and co-authors, “Cross-reactive memory T cells and herd immunity to SARS-CoV-2”, has recently been published.[1] It discusses immunological and epidemiological aspects and implications of pre-existing cross-reactive adaptive immune system memory arising from previous exposure to circulating common cold coronaviruses. They argue that key potential impacts of cross- reactive T cell memory are already incorporated into epidemiological models based on data of transmission dynamics, particularly with regard to their implications for herd immunity. I believe that they are mistaken on the herd immunity point, as I will show in this article.
The first point to make is that cross-reactive T cells were never thought to be the main cause of the herd immunity threshold (HIT)[2] being lower for COVID-19 than the oft-quoted {1 – 1/R0} level, which generally applies for vaccination. Heterogeneity in social connectivity (contact rates) is typically estimated to lower the HIT much more than heterogeneity in biological susceptibility to the causative SARS-CoV-2 virus.[3]
Possible effects of cross-reactive T cells on infection progression
Lipsitch and co-authors note that recent reports have shown that SARS- CoV-2 cross-reactive memory T cells, very largely CD4+ T-cells arising from previous exposure to circulating common cold coronaviruses, are detectable in ~28–50% of individuals not exposed to SARS- CoV-2. They say that only tissue-resident memory T cells (T RM cells) can mount a fast response, with recirculating T CM and T EM T cells taking several days to start fighting an infection. They point out that CD4+ T-cells generally limit disease severity, reduce the viral burden and/or limit the duration of the disease rather than preventing an initial infection.
While I do not intend to challenge any of the foregoing points here, it should be noted that they treat an ‘infection’ as including a case where so few cells have been infected that any (RT-)PCR test for the virus would be negative.
The paper states that CD4+ T cell-mediated memory responses to a virus may involve some or all of CD4+ T FH cell types (required for B cell help and thus almost all neutralizing antibody responses), T H 1 and CTL cell types (with direct antiviral activities in infected tissues), and that the CD4+ T cells involved may be T RM cells, or slower to respond recirculating T CM /T EM cells.
The authors go on to propose four immunological scenarios for the impact of cross-reactive CD4+ memory T cells on COVID-19 severity and viral transmission. The four model scenarios they put forward are:
Reduction of lung burden: CD4+ T cells reduce COVID-19 symptoms and lung viral load but have minimal impact on upper respiratory tract (URT) viral load. TFH cell-accelerated antibodies: CD4+ T FH cells trigger a faster and better antibody response, resulting in accelerated control of virus in the URT and lungs. TRM cells in the URT: CD4+ T RM cells at the site of infection enable rapid control of virus in the URT and lungs. Transient infection: T RM cell immunity ‘blitzes’ viral replication in the URT leading to the elimination of all infected cells within a day of the initial infection, at the portal of entry.
The first three scenarios, along with the case where no cross-reactive T cells exist, are represented in Fig.1 of the paper, reproduced below.
Which models do the data fit?
The authors argue that biological evidence implies model scenario 4 is very unlikely where only CD4+ T cells are involved. They point out that if pre-existing CD4+ T RM cell immunity was so extreme as to preclude significant viral replication, seroconversion (that is, a de novo antibody response to SARS- CoV-2) would not occur. Such individuals would not be detectable by virological (e.g., PCR) or serological diagnostic tests and would not shed virus; effectively, these individuals would be immune to infection and not reported as cases. The authors say that evidence for other human coronaviruses makes this implausible, and that when epidemiological evidence of very high attack rates in some ship-based outbreaks is added scenario 4 is highly unlikely.
However, in the most studied ship-based outbreak the proportion infected was under 20%.[4] Moreover, the results of a study that Lipsitch et al. do not cite[5] show that, in households where one person was confirmed as having COVID-19, a substantial proportion of other household members had negative PCR test results, implying that they were not infective, despite most of them having typical COVID-19 symptoms. Moreover, these individuals did not develop detectable SARS-CoV-2 specific antibodies, but did develop SARS-CoV-2 specific (as opposed to cross reactive) T cell responses, implying that they had been infected to some degree by SARS-CoV-2 .
Notwithstanding that the sample size was small in that study, it appears to cast some doubt on Lipsitch et al.’s assertion that scenario 4 is highly implausible. It also casts doubt on their subsequent assertion that almost all people infected by SARS-CoV-2 seroconvert (develop antibodies against it), although the test used might have been insufficiently sensitive to detect low antibody levels. In that connection, Lipsitch et al. say that a recent study[6] observed [only] about 3 cases of non-PCR confirmed potentially asymptomatic COVID-19 cases with T cell responses in the absence of seroconversion, but their interpretation of that study’s results has been challenged.[7]
A substantial proportion of PCR-test positive individuals – in some localised outbreaks, the vast majority of them – have asymptomatic infections. In the most studied ship-based outbreak4 almost half of infected individuals remained asymptomatic throughout.[8] If that is due to T-cell cross reactivity, acting in combination with innate immune responses, then only model scenarios 3 or 4 would fit, since model scenarios 1 and 2 imply significant symptoms.
In the remainder of this article I will not pursue the possibility of model scenario 4 being relevant. Rather, I will focus on showing that the implications for herd immunity of model scenario 3 (possibly involving also model scenario 2), as varied to take account of variation in viral dose and innate immune system strength, are very likely not already taken into account in simple epidemiological models based on transmission dynamics data. In this connection, it should be noted that the extent and quality of the available data, both biological and epidemiological, does not provide high quality evidence, so drawing firm conclusions either way is difficult.
The low level of asymptomatic transmission
Importantly, there is quite strong evidence that infected individuals transmit SARS-CoV-2 much more weakly if they are asymptomatic (and not just presymptomatic). Biological evidence neither proves nor disproves that a positive PCR test for SARS-CoV-2 implies significant infectivity (although a negative PCR test can be taken as implying a lack of significant infectivity).[9] However, epidemiological evidence strongly suggests that transmission by asymptomatic individuals is far lower than that by symptomatic or presymptomatic individuals.
A number of studies have investigated transmission from index cases who remained asymptomatic throughout their infections. A review study[10] estimated that the mean household secondary attack rate from asymptomatic cases was only 3.5% of that from symptomatic cases. As that study noted, household secondary attack rate provides a useful estimate of both the susceptibility of contacts and infectiousness of index cases. However, both in that study, and in another review study[11] that estimated a much higher ratio than 3.5%, the statistical analysis appears to be seriously flawed.[12] It is therefore necessary to consider the actual results of the relevant original studies that they reviewed. Two of those studies[13] [14] found no instances of asymptomatic transmission, although the number of contacts concerned was very small in one case. Two other studies each found one case of asymptomatic transmission, out of respectively 305 and 119 contacts,[15] [16] with corresponding relative risks of 6% and 19%. Averaging the risk ratios of all four studies, in a way that gives appropriate weight to the evidence each provides, gives an overall transmission risk ratio estimate of 8% for asymptomatic cases, relative to symptomatic and presymptomatic cases.[17]
That is, an asymptomatic infected person, as in Lipsitch et al. model scenario 3, appears to be only one-tenth or less as likely to transmit the virus as does a symptomatic or presymptomatic person. This conclusion does not have to depend on asymptomatic infectees having a much lower viral load in their URT, which may not be the case. They could for instance transmit less because of an absence of coughing (particularly that involving expectoration15), less deep breathing or a similar factor; because more of their PCR-measured viral load does not represent viable viruses; and/or because their viral load remains at an infective level for a shorter period.
The likely importance of viral dose (inoculum) and innate immune responses
Two factors that Lipsitch et al. do not include in their model scenarios, but which seem likely to be very relevant, are the magnitude of the viral dose and the strength of a person’s fast responding innate immune system. Both the symptoms, as to their likelihood and severity, and the infectivity of an individual exposed to SARS-CoV-2 are expected to depend on the viral dose that their exposure involves[18] [19] and on the strength of their innate immune system, as well as on any cross-reactive T cell and/or antibody adaptive immune system memory. I will concentrate here on differences arising from the interaction between viral dose and cross-reactive T cells, but in reality differences in innate immune system strength, and in general health and other factors, will likewise affect how somebody reacts to viral doses of varying strength and to what extent they become ill and/or infective.
Box 1: Might low viral dose explain Tokyo’s COVID-19 epidemic?Evidence from a study[20] of 1877 asymptomatic (at time of testing) company employees from 11 disparate locations in Tokyo is consistent with the importance of viral dose. The study showed seroprevalence increasing from 6% to 47% between late May and late August. If the sample is representative of the Tokyo metropolitan area, which the authors suggest it may be, that implies seroconversion of about 5.7 million individuals during the study period.
Since the corresponding number of deaths attributed to COVID-19 appears to have been little more than 30, that implies an infection fatality rate in Tokyo that might be as low as 0.0006% – around a thousand times lower than generally estimated. In Japan, the very high rate of mask wearing (and generally high personal hygiene standards) may have reduced viral doses sufficiently for the vast majority of infections to be asymptomatic, and for almost all symptomatic cases to be mild, irrespective of the presence of cross-reactive T cells.[21] [22]
It seems entirely possible that where the viral dose is sufficiently low, a person with cross reactive CD4+ T cells might either be infected so little that – whether or not a PCR test would be positive, at a sufficiently large cycle threshold (high sensitivity) – they not only remain asymptomatic but also have negligible infectivity. In effect, given a sufficiently low viral dose, Lipsitch et al.’s model scenario 3 might produce rather similar effects to what their model scenario 4 would do for a high viral dose. It appears, for most purposes, to be inappropriate to regard such a non-infective, healthy person as a COVID-19 case or even as being infected at all.[23] Such persons are accordingly treated here as not having been infected. However, it appears possible that a low viral load might, without resulting in symptoms or non-negligible infectivity, nevertheless induce effective immunity through the development of SARS-CoV-2 specific antibodies and/or T cells.
By contrast, Lipsitch et al. appear to regard someone as having been infected even if only a single cell in their body has been invaded by a virus. While this definition may be logical from a technical biological angle, it does not seem appropriate from an epidemiological viewpoint. For epidemiological purposes, what is relevant is whether and to what extent a person is or will become ill, infective and/or immune.
Where the viral dose is fairly high, even if a person has cross reactive CD4+ T cells, they would almost certainly test PCR positive, and be more likely to develop symptoms. Model scenarios 2 and 3 in the paper may both be relevant in these cases. While in such a case the person concerned would be infective, albeit much less so if asymptomatic, if they had cross reactive CD4+ T cells they would probably be considerably less infective (and be much more likely to be asymptomatic or to have mild symptoms).
Insofar as infected individuals are asymptomatic and of low (but non-negligible) infectivity, it may be that in cases where they do transmit infection the viral dose is usually sufficiently low for the person thereby infected also to be asymptomatic and of low infectivity, in which case such asymptomatic transmission will contribute to the gradual spread of immunity while not leading to disease.[24]
Modelling the effects of varying susceptibility and infectivity arising from cross-reactive T cells
I have built a greatly simplified toy model that illustrates the possible implications for epidemic progression and herd immunity of cross-reactive T cells that have the effects discussed in this article. The model stratifies the population into two equal parts, one possessing cross-reactive T cells and the other not. It distinguishes symptomatic and asymptomatic infections, the latter having only one-ninth as high a probability of causing infection as the former.
The detailed assumptions made in the model are set out in an Appendix. While these assumptions are purely illustrative, they are intended to be broadly consistent with existing evidence and the foregoing discussion in this article. The key assumptions regarding the effects of cross-reactive T cells are that their presence halves the risk of infection from a potentially infective contact, quarters the probability of any infection being symptomatic, and may result in immunity developing in a substantial proportion of those cases where infection does not occur.
The modelled epidemic is seeded by the symptomatic infection of one naïve individual (a person without cross-reactive T cells). The number of close contacts per generation is then adjusted to produce, after the epidemic has adjusted from the initial seeding pattern to its natural pattern, a reproduction number early in the epidemic – which will therefore closely approximate R0 – of 2.4.
The toy model’s projections show that, after initial exponential growth, new infections start to decline, indicating that herd immunity has been achieved. At that point, 41% of the population has been infected, with approximately 43% of infections being asymptomatic. At 41%, the HIT is slightly over two-thirds of the classical HIT level for a homogeneous population, being 58%.[25]
A further 20% of the population will have become immune without, for all practical purposes, having had an infection. If on the other hand no exposed but uninfected (i.e., asymptomatic and non-infective) individuals develop immunity, then the HIT is closer to the classical level, but still lies more than 10% below it. If the probability of being infected is reduced by 85% in the presence of cross-reactive T cell memory, the HIT could be one-third below the classical HIT even if no exposed but uninfected person develops immunity. It is not suggested that such a large differential is likely. However, it does prove that cross-reactive T cell memory, in combination with varying viral dose (and innate immune system strength) can result in a substantially lower herd immunity threshold than that estimated from data earlier in the epidemic using homogeneous population compartmental SIR/SEIR models, as is routinely done.
A more realistic model would incorporate continuous probability distributions for all the key parameters. But the basic point illustrated by the very simple model would remain valid. Homogeneous population based compartmental models imply that epidemic growth will slow pro rata to the shrinking pool of uninfected people. But where there is variation within the population as to how susceptible people are to infection, so that more susceptible individuals are on average infected earlier, the epidemic growth is bound to reduce more rapidly than that. As a result, the herd immunity threshold will be lower than if the population were homogeneous, with the reduction of the HIT being greater if less biologically susceptible individuals also have, if infected, lower biological infectivity.
Conclusion
I have demonstrated that the claim by Lipsitch et al. that the potential impacts on the herd immunity threshold of cross- reactive T cell memory are already incorporated into epidemiological models based on data of transmission dynamics is mistaken, even assuming that they are correct in arguing that their model scenario 4 is highly implausible.
In this article I have only considered the possible effects of cross-reactive T cells. However, even when combined with other causes of interpersonal variation in biological susceptibility, including age, such heterogeneity is not thought to be the main reason why the herd immunity threshold will be lower than the classical level for a homogeneous population. In practice, interpersonal variation in contact rates (social connectivity) is usually thought to be a much more important reason. 3
Appendix – Assumptions made in the toy model of the effects of cross-reactive T cells
The population is one million and is homogeneous except that only 50% of people have cross-reactive memory T-cells. The generation interval is fixed, infected individuals are only infectious in the generation interval after they become infected and are uninfectious and immune thereafter. Infections are by close contact only. The number of close contacts by an infected person is independent of their T cell status and whether or not their infection is asymptomatic (never symptomatic), and each person who becomes infected has only had one contact with an infectious person during the generation interval in which they become infected. A close contact between a symptomatic (including presymptomatic) infectee and a naïve individual (one without cross-reactive T cells) results in infection 90% of the time, with 80% of such infections being symptomatic, due to a high average viral dose being involved. A close contact between an asymptomatic infectee and a naïve individual results in infection 10% of the time, with 20% of such infections being symptomatic, the viral dose being lower. A close contact between a symptomatic infectee and a resistant individual (one with cross-reactive T cells) results in infection 45% of the time, with 20% of such infections being symptomatic. A close contact between an asymptomatic infectee and a resistant individual results in infection 5% of the time, with 5% of such infections being symptomatic. Where such a low viral dose is transmitted on a close contact that a resistant recipient not only has no symptoms but is completely non-infective, they are treated as not being infected but (except if stated otherwise) in 60% of such cases they nevertheless become immune.
Nicholas Lewis 14 October 2020
[1] Marc Lipsitch, Yonatan H. Grad, Alessandro Sette and Shane Crotty: Cross-reactive memory T cells and herd immunity to SARS-CoV-2. Nature Reviews Immunology 6 October 2020 https://doi.org/10.1038/s41577-020-00460-4
[2] The herd immunity threshold is the proportion of the population that have become infected at the point where each new infection causes, on average, no more than one further infection. For an epidemic in a homogeneous population, it will be {1 – 1/R0}, where R0 is the basic (initial) reproduction number.
[3] e.g., Tkachenko, A.V. et al.: Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19. medRxiv 29 July 2020 https://doi.org/10.1101/2020.07.26.20162420
[4] The Diamond Princess. 712 out of 3,711 on board tested PCR-positive, with at least 295 and probably closer to 334 cases (295 from Tokyo plus 40 returned on charter flights, one of whom died from COVID-19) remaining asymptomatic throughout their infection. https://www.mhlw.go.jp/stf/newpage_11441.html
[5] Gallais, F., Velay, A., Wendling, M.J., Nazon, C., Partisani, M., Sibilia, J., Candon, S. and Fafi-Kremer, S., 2020. Intrafamilial exposure to SARS-CoV-2 induces cellular immune response without seroconversion. MedRxiv. https://www.medrxiv.org/content/medrxiv/early/2020/06/22/2020.06.21.20132449.full.pdf
[6] Sekine, T. et al. Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19. Cell https://doi.org/10.1016/j.cell.2020.08.017 (2020).
[7] https://twitter.com/WesPegden/status/1313649435642077184
[8] That is consistent with the findings of Sakurai et al., Natural History of Asymptomatic SARS-CoV-2 Infection. New England Journal of Medicine. 2020 Jun 12 https://www.nejm.org/doi/full/10.1056/NEJMc2013020
[9] https://www.cebm.net/covid-19/infectious-positive-pcr-test-result-covid-19/
[10] Madewell, Z.J., Yang, Y., Longini Jr, I.M., Halloran, M.E. and Dean, N.E., 2020. Household transmission of SARS-CoV-2: a systematic review and meta-analysis of secondary attack rate. medRxiv. [1 August version]
[11] Buitrago-Garcia D, et al. (2020) Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and metaanalysis. PLoS Med 17(9): e1003346. https://doi.org/10.1371/journal.pmed.1003346
[12] Madewell et al. included a study where the secondary attack case rather than the index case was asymptomatic. Buitrago-Garcia D, et al., who estimated a relative risk of 0.35 for asymptomatic transmission, included in that estimate a study of presymptomatic transmission (their reference 111) and an estimate based on combined asymptomatic and presymptomatic transmission (from their reference 65), and they wrongly estimated very high asymptomatic transmission risk from studies that found zero cases of it.
[13] Cheng HY, Jian SW, Liu DP, Ng TC, Huang WT, Lin HH, et al. Contact Tracing Assessment of Covid-19 Transmission Dynamics in Taiwan and Risk at Different Exposure Periods before and after Symptom Onset. JAMA Intern Med. 2020. Epub 2020/05/02. https://doi.org/10.1001/jamainternmed.2020.
[14] Park SY, Kim YM, Yi S, Lee S, Na BJ, Kim CB, et al. Coronavirus Disease Outbreak in Call Center,
South Korea. Emerg Infect Dis. 2020. https://doi.org/10.3201/eid2608.201274
[15] Luo L, Liu D, Liao X-l, Wu X-b, Jing Q-l, Zheng J-z, et al. Modes of Contact and Risk of Transmission in Covid-19 among Close Contacts. bioRxiv. 2020 [March 26 version] https://www.medrxiv.org/content/10.1101/2020.03.24.20042606v1
[16] Zhang W, Cheng W, Luo L, Ma Y, Xu C, Qin P, et al. Secondary Transmission of Coronavirus Disease from Presymptomatic Persons, China. Emerg Infect Dis. 2020. https://doi.org/10.3201/eid2608.201142
[17] Averaging over the four studies, weighting by the number of contacts by asymptomatic index cases, gives a pooled relative risk estimate of 8.2%. A more sophisticated meta-analysis using the R software Fixed effects (Mantel-Haenszel) function in the rmeta package estimates a pooled study relative risk of 7.9%, with a 75% probability that it does not exceed 13% and a 90% probability that it does not exceed 20% (assuming that the confidence intervals are symmetrical).
[18] Steinmeyer, Shelby H., Claus O. Wilke, and Kim M. Pepin. “Methods of modelling viral disease dynamics across the within-and between-host scales: the impact of virus dose on host population immunity.” Philosophical Transactions of the Royal Society B: Biological Sciences 365.1548 (2010): 1931-1941.
[19] Goyal, Ashish, et al. “Wrong person, place and time: viral load and contact network structure predict SARS-CoV-2 transmission and super-spreading events.” medRxiv (2020) [7 August version].
[20] Hibino, Sawako, et al. “Dynamic Change of COVID-19 Seroprevalence among Asymptomatic Population in Tokyo during the Second Wave.” medRxiv (2020). [23 September version]
[21] Gandhi, Monica, Chris Beyrer, and Eric Goosby. “Masks do more than protect others during COVID-19: Reducing the inoculum of SARS-CoV-2 to protect the wearer.” Journal of general internal medicine (2020): 1-4.
[22] Gandhi, Monica, and George W. Rutherford. “Facial Masking for Covid-19—Potential for “Variolation” as We Await a Vaccine.” New England Journal of Medicine (2020).
[23] While such cases might give a positive result on a PCR test at some point, such a test result might represent detection only of non-viable virus fragments, or a viable viral load that is too low to be infective.
[24] Unfortunately there are too few recorded cases of asymptomatic transmission to provide reliable evidence on this point, but what evidence does appear to exist is consistent with this argument. Zhang et al. identified one case of asymptomatic transmission, which resulted in an asymptomatic infection, while 73% of the eleven cases of symptomatic or presymptomatic transmission they identified resulted in a symptomatic infection. (Luo et al. unfortunately did not indicate the symptom status of the asymptomatic transmission case that they identified.)
[25] For an R0 of 2.4, the classical herd immunity threshold is {1 – 1/2.4} = 0.583
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr


climateetc--2020-10-10--What the pandemic has taught us about science

The scientific method remains the best way to solve many problems , but bias , overconfidence and politics can sometimes lead scientists astray It ’ s been awhile since I have been so struck by an article that I felt moved to immediately do a blog post .
Well , maybe because today is Saturday and it is one day after the landfall of Hurricane Delta , I actually have a half hour to do this .
Matt Ridley has published an article in the WSJ What the pandemic has taught us about science , that is highly relevant for @ @ @ @ @ @ @ .
It is excellent , I agree with and endorse every word of this .
The paper is behind paywall ; Dan Hughes kindly sent me a topic of the text .
Here are extensive excerpts The Covid-19 pandemic has stretched the bond between the public and the scientific profession as never before .
Scientists have been revealed to be neither omniscient demigods whose opinions automatically outweigh all political disagreement , nor unscrupulous fraudsters pursuing a political agenda under a cloak of impartiality .
Somewhere between the two lies the truth : Science @ @ @ @ @ @ @ affair , but it can generate timeless truths , and reliable practical guidance , in a way that other approaches can not .
In a lecture at Cornell University in 1964 , the physicist Richard Feynman defined the scientific method .
First , you guess , he said , to a ripple of laughter .
Then you compute the consequences of your guess .
Then you compare those consequences with the evidence from observations or experiments .
“ If [ your guess ] disagrees with experiment , it ’ s wrong .
In @ @ @ @ @ @ @ science .
It does not make a difference how beautiful the guess is , how smart you are , who made the guess or what his name is…it ’ s wrong.
” So when people started falling ill last winter with a respiratory illness , some scientists guessed that a novel coronavirus was responsible .
The evidence proved them right .
Some guessed it had come from an animal sold in the Wuhan wildlife market .
The evidence proved them wrong .
Some guessed vaccines could be developed that would prevent infection .
@ @ @ @ @ @ @ science as a game of guess-and-test clarifies what has been happening these past months .
Science is not about pronouncing with certainty on the known facts of the world ; it is about exploring the unknown by testing guesses , some of which prove wrong .
Bad practice can corrupt all stages of the process .
Some scientists fall so in love with their guesses that they fail to test them against evidence .
They just compute the consequences and stop there .
Mathematical models are elaborate , formal guesses , and there @ @ @ @ @ @ @ years to describe their output with words like data , result or outcome .
They are nothing of the sort .
An epidemiological model developed last March at Imperial College London was treated by politicians as hard evidence that without lockdowns , the pandemic could kill 2.2 million Americans , 510,000 Britons and 96,000 Swedes .
The Swedes tested the model against the real world and found it wanting : They decided to forgo a lockdown , and fewer than 6,000 have died there .
In general , science is much better at @ @ @ @ @ @ @ present than the future .
As Philip Tetlock of the University of Pennsylvania and others have shown , forecasting economic , meteorological or epidemiological events more than a short time ahead continues to prove frustratingly hard , and experts are sometimes worse at it than amateurs , because they overemphasize their pet causal theories .
A second mistake is to gather flawed data .
On May 22 , the respected medical journals the Lancet and the New England Journal of Medicine published a study based on the medical records of 96,000 patients from @ @ @ @ @ @ @ to disprove the guess that the drug hydroxychloroquine could cure Covid-19 .
The study caused the World Health Organization to halt trials of the drug .
It then emerged , however , that the database came from Surgisphere , a small company with little track record , few employees and no independent scientific board .
When challenged , Surgisphere failed to produce the raw data .
The papers were retracted with abject apologies from the journals .
Nor has hydroxychloroquine since been proven to work .
Uncertainty about it persists .
A third @ @ @ @ @ @ @ but inadequate .
Evidence-based medicine teaches doctors to fully trust only science based on the gold standard of randomized controlled trials .
But there have been no randomized controlled trials on the wearing of masks to prevent the spread of respiratory diseases ( though one is now under way in Denmark ) .
In the West , unlike in Asia , there were months of disagreement this year about the value of masks , culminating in the somewhat desperate argument of mask foes that people might behave too complacently when wearing them .
@ @ @ @ @ @ @ is good enough and the inconvenience small enough that we need not wait for absolute certainty before advising people to wear masks .
This is an inverted form of the so-called precautionary principle , which holds that uncertainty about possible hazards is a strong reason to limit or ban new technologies .
But the principle cuts both ways .
If a course of action is known to be safe and cheap and might help to prevent or cure diseases—like wearing a face mask or taking vitamin D supplements , in the case of @ @ @ @ @ @ @ trying it .
A fourth mistake is to gather data that are compatible with your guess but to ignore data that contest it .
This is known as confirmation bias .
You should test the proposition that all swans are white by looking for black ones , not by finding more white ones .
Yet scientists “ believe ” in their guesses , so they often accumulate evidence compatible with them but discount as aberrations evidence that would falsify them—saying , for example , that black swans in Australia don ’ t count @ @ @ @ @ @ @ to see the same data in different ways .
Last January , Chinese scientists published a genome sequence known as RaTG13 from the virus most closely related to the one that causes Covid-19 , isolated from a horseshoe bat in 2013 .
But there are questions surrounding the data .
When the sequence was published , the researchers made no reference to the previous name given to the sample or to the outbreak of illness in 2012 that led to the investigation of the mine where the bat lived .
It emerged only @ @ @ @ @ @ @ sequenced in 2017-2018 instead of post-Covid , as originally claimed .
These anomalies have led some scientists , including Dr. Li-Meng Yan , who recently left the University of Hong Kong School of Public Health and is a strong critic of the Chinese government , to claim that the bat virus genome sequence was fabricated to distract attention from the truth that the SARS-CoV-2 virus was actually manufactured from other viruses in a laboratory .
These scientists continue to seek evidence , such as a lack of expected bacterial DNA in the supposedly @ @ @ @ @ @ @ the official story .
By contrast , Dr. Kristian Andersen of Scripps Research in California has looked at the same confused announcements and stated that he does not “ believe that any type of laboratory-based scenario is plausible.
” Having checked the raw data , he has “ no concerns about the overall quality of [ the genome of ] RaTG13.
” As this example illustrates , one of the hardest questions a science commentator faces is when to take a heretic seriously .
It ’ s tempting for established scientists to use @ @ @ @ @ @ @ , but not every maverick is a new Galileo .
Peer review is supposed to be the device that guides us away from unreliable heretics .
Investigations show that peer review is often perfunctory rather than thorough ; often exploited by chums to help each other ; and frequently used by gatekeepers to exclude and extinguish legitimate minority scientific opinions in a field .
Herbert Ayres , an expert in operations research , summarized the problem well several decades ago : “ As a referee of a paper that threatens to disrupt his @ @ @ @ @ @ @ in a conflict-of-interest position , pure and simple .
Unless we ’ re convinced that he , we , and all our friends who referee have integrity in the upper fifth percentile of those who have so far qualified for sainthood , it is beyond naive to believe that censorship does not occur.
” Rosalyn Yalow , winner of the Nobel Prize in medicine , was fond of displaying the letter she received in 1955 from the Journal of Clinical Investigation noting that the reviewers were “ particularly emphatic in rejecting ” her @ @ @ @ @ @ @ on tolerating , even encouraging , at least some disagreement .
In practice , science is prevented from turning into religion not by asking scientists to challenge their own theories but by getting them to challenge each other , sometimes with gusto .
Where science becomes political , as in climate change and Covid-19 , this diversity of opinion is sometimes extinguished in the pursuit of a consensus to present to a politician or a press conference , and to deny the oxygen of publicity to cranks .
This year has driven home @ @ @ @ @ @ @ is no such thing as “ the science ” ; there are different scientific views on how to suppress the virus .
Anthony Fauci , the chief scientific adviser in the U.S. , was adamant in the spring that a lockdown was necessary and continues to defend the policy .
His equivalent in Sweden , Anders Tegnell , by contrast , had insisted that his country would not impose a formal lockdown and would keep borders , schools , restaurants and fitness centers open while encouraging voluntary social distancing .
At first , @ @ @ @ @ @ @ as Sweden ’ s case load increased .
Now , with cases low and the Swedish economy in much better health than other countries , he looks wise .
Both are good scientists looking at similar evidence , but they came to different conclusions .
Prof. Ritchie argues that the way scientists are funded , published and promoted is corrupting : “ Peer review is far from the guarantee of reliability it is cracked up to be , while the system of publication that ’ s supposed to be a crucial strength of @ @ @ @ @ @ @ He says that we have “ ended up with a scientific system that doesn ’ t just overlook our human foibles but amplifies them.
” Organized science is indeed able to distill sufficient expertise out of debate in such a way as to solve practical problems .
It does so imperfectly , and with wrong turns , but it still does so .
How should the public begin to make sense of the flurry of sometimes contradictory scientific views generated by the Covid-19 crisis ?
The only way to be absolutely sure that @ @ @ @ @ @ @ is not is to examine the evidence yourself .
Relying on the reputation of the scientist , or the reporter reporting it , is the way that many of us go , and is better than nothing , but it is not infallible .
If in doubt , do your homework .

The scientific method remains the best way to solve many problems, but bias, overconfidence and politics can sometimes lead scientists astray
It’s been awhile since I have been so struck by an article that I felt moved to immediately do a blog post. Well, maybe because today is Saturday and it is one day after the landfall of Hurricane Delta, I actually have a half hour to do this.
Matt Ridley has published an article in the WSJ What the pandemic has taught us about science, that is highly relevant for climate change as well as for Covid-19. It is excellent, I agree with and endorse every word of this.
The paper is behind paywall; Dan Hughes kindly sent me a topic of the text. Here are extensive excerpts
The Covid-19 pandemic has stretched the bond between the public and the scientific profession as never before. Scientists have been revealed to be neither omniscient demigods whose opinions automatically outweigh all political disagreement, nor unscrupulous fraudsters pursuing a political agenda under a cloak of impartiality. Somewhere between the two lies the truth: Science is a flawed and all too human affair, but it can generate timeless truths, and reliable practical guidance, in a way that other approaches cannot.
In a lecture at Cornell University in 1964, the physicist Richard Feynman defined the scientific method. First, you guess, he said, to a ripple of laughter. Then you compute the consequences of your guess. Then you compare those consequences with the evidence from observations or experiments. “If [your guess] disagrees with experiment, it’s wrong. In that simple statement is the key to science. It does not make a difference how beautiful the guess is, how smart you are, who made the guess or what his name is…it’s wrong.”
So when people started falling ill last winter with a respiratory illness, some scientists guessed that a novel coronavirus was responsible. The evidence proved them right. Some guessed it had come from an animal sold in the Wuhan wildlife market. The evidence proved them wrong. Some guessed vaccines could be developed that would prevent infection. The jury is still out.
Seeing science as a game of guess-and-test clarifies what has been happening these past months. Science is not about pronouncing with certainty on the known facts of the world; it is about exploring the unknown by testing guesses, some of which prove wrong.
Bad practice can corrupt all stages of the process. Some scientists fall so in love with their guesses that they fail to test them against evidence. They just compute the consequences and stop there. Mathematical models are elaborate, formal guesses, and there has been a disturbing tendency in recent years to describe their output with words like data, result or outcome. They are nothing of the sort.
An epidemiological model developed last March at Imperial College London was treated by politicians as hard evidence that without lockdowns, the pandemic could kill 2.2 million Americans, 510,000 Britons and 96,000 Swedes. The Swedes tested the model against the real world and found it wanting: They decided to forgo a lockdown, and fewer than 6,000 have died there.
In general, science is much better at telling you about the past and the present than the future. As Philip Tetlock of the University of Pennsylvania and others have shown, forecasting economic, meteorological or epidemiological events more than a short time ahead continues to prove frustratingly hard, and experts are sometimes worse at it than amateurs, because they overemphasize their pet causal theories.
A second mistake is to gather flawed data. On May 22, the respected medical journals the Lancet and the New England Journal of Medicine published a study based on the medical records of 96,000 patients from 671 hospitals around the world that appeared to disprove the guess that the drug hydroxychloroquine could cure Covid-19. The study caused the World Health Organization to halt trials of the drug.
It then emerged, however, that the database came from Surgisphere, a small company with little track record, few employees and no independent scientific board. When challenged, Surgisphere failed to produce the raw data. The papers were retracted with abject apologies from the journals. Nor has hydroxychloroquine since been proven to work. Uncertainty about it persists.
A third problem is that data can be trustworthy but inadequate. Evidence-based medicine teaches doctors to fully trust only science based on the gold standard of randomized controlled trials. But there have been no randomized controlled trials on the wearing of masks to prevent the spread of respiratory diseases (though one is now under way in Denmark). In the West, unlike in Asia, there were months of disagreement this year about the value of masks, culminating in the somewhat desperate argument of mask foes that people might behave too complacently when wearing them. The scientific consensus is that the evidence is good enough and the inconvenience small enough that we need not wait for absolute certainty before advising people to wear masks.
This is an inverted form of the so-called precautionary principle, which holds that uncertainty about possible hazards is a strong reason to limit or ban new technologies. But the principle cuts both ways. If a course of action is known to be safe and cheap and might help to prevent or cure diseases—like wearing a face mask or taking vitamin D supplements, in the case of Covid-19—then uncertainty is no excuse for not trying it.
A fourth mistake is to gather data that are compatible with your guess but to ignore data that contest it. This is known as confirmation bias. You should test the proposition that all swans are white by looking for black ones, not by finding more white ones. Yet scientists “believe” in their guesses, so they often accumulate evidence compatible with them but discount as aberrations evidence that would falsify them—saying, for example, that black swans in Australia don’t count.
Advocates of competing theories are apt to see the same data in different ways. Last January, Chinese scientists published a genome sequence known as RaTG13 from the virus most closely related to the one that causes Covid-19, isolated from a horseshoe bat in 2013. But there are questions surrounding the data. When the sequence was published, the researchers made no reference to the previous name given to the sample or to the outbreak of illness in 2012 that led to the investigation of the mine where the bat lived. It emerged only in July that the sample had been sequenced in 2017-2018 instead of post-Covid, as originally claimed.
These anomalies have led some scientists, including Dr. Li-Meng Yan, who recently left the University of Hong Kong School of Public Health and is a strong critic of the Chinese government, to claim that the bat virus genome sequence was fabricated to distract attention from the truth that the SARS-CoV-2 virus was actually manufactured from other viruses in a laboratory. These scientists continue to seek evidence, such as a lack of expected bacterial DNA in the supposedly fecal sample, that casts doubt on the official story.
By contrast, Dr. Kristian Andersen of Scripps Research in California has looked at the same confused announcements and stated that he does not “believe that any type of laboratory-based scenario is plausible.” Having checked the raw data, he has “no concerns about the overall quality of [the genome of] RaTG13.”
As this example illustrates, one of the hardest questions a science commentator faces is when to take a heretic seriously. It’s tempting for established scientists to use arguments from authority to dismiss reasonable challenges, but not every maverick is a new Galileo.
Peer review is supposed to be the device that guides us away from unreliable heretics. Investigations show that peer review is often perfunctory rather than thorough; often exploited by chums to help each other; and frequently used by gatekeepers to exclude and extinguish legitimate minority scientific opinions in a field.
Herbert Ayres, an expert in operations research, summarized the problem well several decades ago: “As a referee of a paper that threatens to disrupt his life, [a professor] is in a conflict-of-interest position, pure and simple. Unless we’re convinced that he, we, and all our friends who referee have integrity in the upper fifth percentile of those who have so far qualified for sainthood, it is beyond naive to believe that censorship does not occur.” Rosalyn Yalow, winner of the Nobel Prize in medicine, was fond of displaying the letter she received in 1955 from the Journal of Clinical Investigation noting that the reviewers were “particularly emphatic in rejecting” her paper.
The health of science depends on tolerating, even encouraging, at least some disagreement. In practice, science is prevented from turning into religion not by asking scientists to challenge their own theories but by getting them to challenge each other, sometimes with gusto. Where science becomes political, as in climate change and Covid-19, this diversity of opinion is sometimes extinguished in the pursuit of a consensus to present to a politician or a press conference, and to deny the oxygen of publicity to cranks. This year has driven home as never before the message that there is no such thing as “the science”; there are different scientific views on how to suppress the virus.
Anthony Fauci, the chief scientific adviser in the U.S., was adamant in the spring that a lockdown was necessary and continues to defend the policy. His equivalent in Sweden, Anders Tegnell, by contrast, had insisted that his country would not impose a formal lockdown and would keep borders, schools, restaurants and fitness centers open while encouraging voluntary social distancing. At first, Dr. Tegnell’s experiment looked foolish as Sweden’s case load increased. Now, with cases low and the Swedish economy in much better health than other countries, he looks wise. Both are good scientists looking at similar evidence, but they came to different conclusions.
Prof. Ritchie argues that the way scientists are funded, published and promoted is corrupting: “Peer review is far from the guarantee of reliability it is cracked up to be, while the system of publication that’s supposed to be a crucial strength of science has become its Achilles heel.” He says that we have “ended up with a scientific system that doesn’t just overlook our human foibles but amplifies them.”
Organized science is indeed able to distill sufficient expertise out of debate in such a way as to solve practical problems. It does so imperfectly, and with wrong turns, but it still does so.
How should the public begin to make sense of the flurry of sometimes contradictory scientific views generated by the Covid-19 crisis? The only way to be absolutely sure that one scientific pronouncement is reliable and another is not is to examine the evidence yourself. Relying on the reputation of the scientist, or the reporter reporting it, is the way that many of us go, and is better than nothing, but it is not infallible. If in doubt, do your homework.
Share this: Email
Twitter
Facebook
Print
Reddit
Tumblr