climateetc--2020-07-27--Why herd immunity to COVID-19 is reached much earlier than thought – update

I showed in my May 10th article Why herd immunity to COVID-19 is reached much earlier than thought that inhomogeneity within a population in the susceptibility and in the social-connectivity related infectivity of individuals would reduce , in my view probably very substantially , the herd immunity threshold ( HIT ) , beyond which an epidemic goes into retreat .
I opined , based on my modelling , that the HIT probably lay somewhere between 7 % and 24 % , and that evidence from Stockholm County suggested it was around 17 % there , and had been reached .
@ @ @ @ @ @ @ 1 ] I particularly want to highlight an important paper published on July 24th “ Herd immunity thresholds estimated from unfolding epidemics ” ( Aguas et al. )
.
[ 2 ] The author team is much the same as that of the earlier theoretical paper ( Gomes et al .
[ 3 ] ) that prompted my May 10th article .
Aguas et al .
used a SEIR compartmental epidemic model modified to allow for inhomogeneity , similar to the model I used although they also considered further variants .
They fitted @ @ @ @ @ @ @ data from four European countries for which disaggregated regional case data was also readily available .
In all cases they found a better fit from their models incorporating heterogeneity to the standard homogeneous assumption SEIR model .
They found that : Aguas et al .
estimate the HIT at between 6 % and 21 % for the countries in their analysis – very much in line with the range I suggested in May .
They also found that their HIT estimates were robust to various changes in their model specification .
By contrast @ @ @ @ @ @ @ were vaccinated randomly , the estimated HIT would have been around 65 % –80 % , in line with the classical formula , { 1 – 1/R } , where R is the epidemic ’ s basic reproduction number .
[ 4 ] Aguas et al.
’ s Figure 3 , reproduced below , shows how the HIT reduces with increasing variation either in susceptibility ( given exposure ) or in connectivity , which affects both an individual ’ s susceptibility ( via altering exposure to infection ) and infectivity .
The coloured @ @ @ @ @ @ @ position of each of the four countries they analysed in each of these ( separately modelled ) cases .
Aguas et al .
Fig .
3 Herd immunity threshold with gamma-distributed susceptibility ( top ) or connectivity related exposure to infection ( bottom ) .
Curves generated with the SEIR model ( Equation 1-4 ) assuming values of R0 estimated for the study countries assuming gamma-distributed : susceptibility [ top ] ; connectivity ( and hence exposure to infection ) [ bottom ] .
Herd immunity thresholds ( solid curves ) are calculated @ @ @ @ @ @ @ 1/R ) 1/ ( 1 + CV^2 ) for heterogeneous susceptibility and 1 − ( 1/R ) 1/ ( 1 + 2 CV^2 ) for heterogeneous connectivity .
Final sizes of the corresponding unmitigated epidemics are also shown ( dashed ) .
As Aguas et al .
say in their Abstract : The underlying reason for the classical formula being inapplicable is , as they say : The Imperial College COVID-19 model ( Ferguson et al .
[ 5 ] ) is a prime example of one that does not adequately account for @ @ @ @ @ @ @ Aguas et al .
point out that consideration of heterogeneity in the transmission of respiratory infections has traditionally focused on variation in exposure summarized into age-structured contact matrices .
They showed that , besides this approach typically ignoring differences in susceptibility given virus exposure , the aggregation of individuals into age groups leads to much lower variability than that they found from fitting the data .
The resulting models appeared to differ only moderately from homogeneous approximations .
A key reason for variability in susceptibility to COVID-19 given exposure to the SARS-CoV-2 virus @ @ @ @ @ @ @ a substantial proportion ( 35 % to 80 % ) of unexposed individuals have T-cells , circulating antibodies or other components that are cross-reactive to SARS-CoV-2 and can be expected to provide substantial resistance to it .
[ 6 ] [ 7 ] [ 8 ] [ 9 ] Such components likely arise from past exposure to common cold or other coronaviruses , or to influenza .
[ 10 ] Not being specific to SARS-CoV-2 , and typically not being antibodies , such immune system components are not normally detected in seroprevalence or @ @ @ @ @ @ @ I will end with a follow up to my June 28th article focusing on Sweden .
In it , I concluded that it was likely the HIT had been surpassed in the three largest Swedish regions , and in the country as a whole , by the end of April notwithstanding that COVID-19-specific antibodies had only been detected in 6.3 % of the population .
[ 11 ] I also projected , based on their declining trend , that total COVID-19 deaths would likely only be about 6,400 .
Subsequent developments support those @ @ @ @ @ @ @ to decline , notwithstanding a return to more travel and less social distancing , and are now down to 10 to 15 a day .
According to the latest Financial Times analysis , [ 12 ] excess mortality in Sweden over 2020 to date was 5,500 , or 24 % .
That is only about half the excess mortality percentage for the UK ( 45 % ) , Italy ( 44 % ) and Spain ( 56 % ) , and is also lower than for France ( 31 % ) , the @ @ @ @ @ @ @ ( 26 % ) , despite Sweden not having imposed a lockdown or shut primary schools .
Moreover , total mortality in Sweden over the last 24 months is now lower than over the previous 24 months , despite an upward trend in the old age population .
Another important paper has now been published on the role of inhomogeneity within a population in the social-connectivity related susceptibility and infectivity of individuals and in biological susceptibility : “ Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19 ” ( Tkachenko et al.
@ @ @ @ @ @ @ ’ s mathematical/statistical analysis is excellent .
[ 14 ] Their method of estimating the role of population inhomogeneity in lowering the herd immunity threshold seems reasonable in principle .
[ 15 ] However , they estimated the effect of inhomogeneity during lockdowns , and assumed that the effect is the same in other circumstances .
But a key effect of social distancing measures , including public events bans , bar and restaurant closures , etc .
as well as full lockdowns , is to heavily reduce the number of contacts by the @ @ @ @ @ @ @ transmitting infection .
For people with few social connections , such measures will have a proportionately much smaller effect .
So the effect in more normal circumstances of population inhomogeneity in social-connectivity , which appears to be more important than inhomogeneity in biological susceptibility , is bound to be underestimated , quite possibly substantially , by their approach .
Nevertheless , their best fit to New York City COVID-19 data during lockdown gives an estimate of an inhomogeneity factor [ 16 ] λ of 4.5 .
[ 17 ] An alternative estimation method @ @ @ @ @ @ @ States gives a λ estimate of 5.3 .
[ 18 ] A middle of the range λ value of 4.9 implies a HIT of 20 % if R = 3.0 ( 16.4 % if R = 2.4 ; 24.6 % if R = 4 ; 28.0 % if R = 5 ) .
It also equates , if all the inhomogeneity is social-connectivity related , to a coefficient of variation ( CV ) [ 19 ] of 1.4 – which is the geometrical mean of the two CV values ( 1 and 2 @ @ @ @ @ @ @ article .
Estimating λ from fits to the NYC or Chicago data prior to lockdown implies much higher CV estimates , in the range 2.4 to 2.9 if all inhomogeneneity is social-connectivity related , in non-lockdown circumstances .
The corresponding estimates for nine of the worst hit US States range from 1.9 to 3.4 .
[ 20 ] [ 1 ] One example , further supporting my superspreader-based evidence of variability in social connectivity , is Miller et al : Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel @ @ @ @ @ @ @ This paper shows that 1-10 % of infected individuals caused 80 % of infections .
That points to variability in social connectivity related susceptibility and infectivity quite likely being higher than I modelled .
[ 3 ] Gomes , M. G. M. , et al .
: Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold .
medRxiv 2 May 2020. https : //www.medrxiv.org/content/10.1101/2020.04.27.20081893v1 [ 4 ] The basic reproduction number of an epidemic , R , measures how many people , on average , each infected individual infects @ @ @ @ @ @ @ If R exceeds one , the epidemic will grow , exponentially at first .
But , assuming recovered individuals are immune , the pool of susceptible individuals shrinks over time and the current reproduction number falls .
The proportion of the population that have been infected at the point where the current reproduction number falls to one is the ‘ herd immunity threshold ’ ( HIT ) .
Beyond that point the epidemic is under control , and shrinks .
[ 5 ] Neil M Ferguson et al .
: Impact of non-pharmaceutical @ @ @ @ @ @ @ mortality and healthcare demand .
Imperial College COVID-19 Response Team Report 9 , 16 March 2020 , https : //spiral.imperial.ac.uk:8443/handle/10044/1/77482 [ 6 ] Grifoni , A.et al .
: Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals .
Cell 11420 , 2020 https : //doi.org/10.1016/j.cell.2020.05.015 [ 7 ] Braun , J. , et al .
: Presence of SARS-CoV-2 reactive T cells in COVID-19 patients and healthy donors .
medRxiv 22 April 2020 https : //www.medrxiv.org/content/10.1101/2020.04.17.20061440v1 .
[ 8 ] Le Bert , N. et @ @ @ @ @ @ @ SARS-COV-2 specific T cell immunity in SARS-recovered and uninfected individuals .
bioRxiv 27 May 2020. https : //doi.org/10.1101/2020.05.26.115832 [ 10 ] Lee , C. , Koohy , H. , et al .
: CD8+ T cell cross-reactivity against SARS-CoV-2 conferred by other coronavirus strains and influenza virus .
bioRxiv 20 May 2020. https : //doi.org/10.1101/2020.05.20.107292 .
[ 11 ] Such seroprevalence is likely to significantly understate the proportion of the population who have had COVID-19 , since asymptomatic or mild disease often results in undetectably low antibody levels ( Long , Q. X.
@ @ @ @ @ @ @ assessment of asymptomatic SARS-CoV-2 infections .
Nat Med .
18 June 2020 https : //doi.org/10.1038/s41591-020-0965-6 .
Such patients will nevertheless be immune to reinfection ( Sekine , K. et al .
: Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19 .
bioRxiv 29 June 2020 https : //doi.org/10.1101/2020.06.29.174888 ) .965-6 [ 13 ] 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 [ 14 ] I think its title gives a slightly @ @ @ @ @ @ @ not central to their paper .
It is in fact “ persistent ” heterogeneity that causes “ short-term ” overdispersion , albeit that over a short period random variability will have a significant influence .
I ’ m unconvinced by their argument that estimating social-connectivity related susceptibility and infectivity from overdispersion in transmission statistics is likely to lead to significant bias , provided that estimation is based on large-scale transmission and not just a few superspreader events .
[ 15 ] Doing so involves dependency on an estimate of the infection fatality rate @ @ @ @ @ @ @ New York City population that had been infected by early June 2020 looks reasonable , based on the NYS survey suggesting 22.7 % of NYC residents had been infected by late March and the ratio of cumulative COVID-19 deaths 23 days later .
[ 16 ] They term their λ an “ immunity factor ” , but it is only partly related to biological immunity .
If the causative inhomogeneity is related to biological immunity , λ = 1 + CV2 , whereas if it is related to social connectivity ( which affects @ @ @ @ @ @ @ = 1 + 2CV2 .
[ 17 ] They also obtian a similar estimate for Chicago , but based on a much narrower range of data .
[ 18 ] Or λ = 4.7 for a selected subset of States .
[ 19 ] The ratio of the standard deviation to the mean of a random variable .
[ 20 ] I have excluded NY State data , as their curve for that State shows abnormal behaviour , quite likely due to the early epidemic data being strongly dominated by NY City Originally @ @ @ @ @ @ @ is also available

By Nic Lewis
I showed in my May 10th article Why herd immunity to COVID-19 is reached much earlier than thought that inhomogeneity within a population in the susceptibility and in the social-connectivity related infectivity of individuals would reduce, in my view probably very substantially, the herd immunity threshold (HIT), beyond which an epidemic goes into retreat. I opined, based on my modelling, that the HIT probably lay somewhere between 7% and 24%, and that evidence from Stockholm County suggested it was around 17% there, and had been reached. Mounting evidence supports my reasoning.[1]
I particularly want to highlight an important paper published on July 24th “Herd immunity thresholds estimated from unfolding epidemics” (Aguas et al.).[2] The author team is much the same as that of the earlier theoretical paper (Gomes et al.[3]) that prompted my May 10th article.
Aguas et al. used a SEIR compartmental epidemic model modified to allow for inhomogeneity, similar to the model I used although they also considered further variants. They fitted their models to scaled daily new cases data from four European countries for which disaggregated regional case data was also readily available. In all cases they found a better fit from their models incorporating heterogeneity to the standard homogeneous assumption SEIR model. They found that:
Homogeneous models systematically fail to fit the maintenance of low numbers of cases after the relaxation of social distancing measures in many countries and regions.
Aguas et al. estimate the HIT at between 6% and 21% for the countries in their analysis – very much in line with the range I suggested in May. They also found that their HIT estimates were robust to various changes in their model specification. By contrast, if the population were homogeneous or were vaccinated randomly, the estimated HIT would have been around 65% –80%, in line with the classical formula, {1 – 1/R 0 }, where R 0 is the epidemic’s basic reproduction number.[4]
Aguas et al.’s Figure 3, reproduced below, shows how the HIT reduces with increasing variation either in susceptibility (given exposure) or in connectivity, which affects both an individual’s susceptibility (via altering exposure to infection) and infectivity. The coloured dots and vertical lines show the inferred position of each of the four countries they analysed in each of these (separately modelled) cases.
Aguas et al. Fig. 3 Herd immunity threshold with gamma-distributed susceptibility (top) or connectivity related exposure to infection (bottom). Curves generated with the SEIR model (Equation 1-4) assuming values of R0 estimated for the study countries assuming gamma-distributed: susceptibility [top]; connectivity (and hence exposure to infection) [bottom]. Herd immunity thresholds (solid curves) are calculated according to the formula 1 − (1/R 0 )1/(1 + CV^2) for heterogeneous susceptibility and 1 − (1/R 0 )1/(1 + 2 CV^2) for heterogeneous connectivity. Final sizes of the corresponding unmitigated epidemics are also shown (dashed).
As Aguas et al. say in their Abstract:
These findings have profound consequences for the governance of the current pandemic given that some populations may be close to achieving herd immunity despite being under more or less strict social distancing measures.
The underlying reason for the classical formula being inapplicable is, as they say:
More susceptible and more connected individuals have a higher propensity to be infected and thus are likely to become immune earlier. Due to this selective immunization by natural infection, heterogeneous populations require less infections to cross their herd immunity threshold than suggested by models that do not fully account for variation.
The Imperial College COVID-19 model (Ferguson et al.[5]) is a prime example of one that does not adequately account for variation in individual susceptibility and connectivity.
Aguas et al. point out that consideration of heterogeneity in the transmission of respiratory infections has traditionally focused on variation in exposure summarized into age-structured contact matrices. They showed that, besides this approach typically ignoring differences in susceptibility given virus exposure, the aggregation of individuals into age groups leads to much lower variability than that they found from fitting the data. The resulting models appeared to differ only moderately from homogeneous approximations.
A key reason for variability in susceptibility to COVID-19 given exposure to the SARS-CoV-2 virus causing is that the immune systems of a substantial proportion (35% to 80%) of unexposed individuals have T-cells, circulating antibodies or other components that are cross-reactive to SARS-CoV-2 and can be expected to provide substantial resistance to it.[6] [7] [8] [9] Such components likely arise from past exposure to common cold or other coronaviruses, or to influenza.[10] Not being specific to SARS-CoV-2, and typically not being antibodies, such immune system components are not normally detected in seroprevalence or other tests for immunity to SARS-CoV-2.
I will end with a follow up to my June 28th article focusing on Sweden. In it, I concluded that it was likely the HIT had been surpassed in the three largest Swedish regions, and in the country as a whole, by the end of April notwithstanding that COVID-19-specific antibodies had only been detected in 6.3% of the population.[11] I also projected, based on their declining trend, that total COVID-19 deaths would likely only be about 6,400. Subsequent developments support those conclusions. Swedish COVID-19 deaths have continued to decline, notwithstanding a return to more travel and less social distancing, and are now down to 10 to 15 a day. According to the latest Financial Times analysis,[12] excess mortality in Sweden over 2020 to date was 5,500, or 24%. That is only about half the excess mortality percentage for the UK (45%), Italy (44%) and Spain (56%), and is also lower than for France (31%), the Netherlands (27%) and Switzerland (26%), despite Sweden not having imposed a lockdown or shut primary schools. Moreover, total mortality in Sweden over the last 24 months is now lower than over the previous 24 months, despite an upward trend in the old age population.
Nicholas Lewis 27 July 2020
Further update 31 July 2020
Another important paper has now been published on the role of inhomogeneity within a population in the social-connectivity related susceptibility and infectivity of individuals and in biological susceptibility: “Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19” (Tkachenko et al.)[13]. The paper’s mathematical/statistical analysis is excellent.[14] Their method of estimating the role of population inhomogeneity in lowering the herd immunity threshold seems reasonable in principle.[15]
However, they estimated the effect of inhomogeneity during lockdowns, and assumed that the effect is the same in other circumstances. But a key effect of social distancing measures, including public events bans, bar and restaurant closures, etc. as well as full lockdowns, is to heavily reduce the number of contacts by the most connected people that are capable of transmitting infection. For people with few social connections, such measures will have a proportionately much smaller effect. So the effect in more normal circumstances of population inhomogeneity in social-connectivity, which appears to be more important than inhomogeneity in biological susceptibility, is bound to be underestimated, quite possibly substantially, by their approach.
Nevertheless, their best fit to New York City COVID-19 data during lockdown gives an estimate of an inhomogeneity factor[16] λ of 4.5.[17] An alternative estimation method based on a cross-sectional regression across US States gives a λ estimate of 5.3.[18]
A middle of the range λ value of 4.9 implies a HIT of 20% if R 0 = 3.0 (16.4% if R 0 = 2.4; 24.6% if R 0 = 4; 28.0% if R 0 = 5). It also equates, if all the inhomogeneity is social-connectivity related, to a coefficient of variation (CV)[19] of 1.4 – which is the geometrical mean of the two CV values (1 and 2) that I used in my original article.
Estimating λ from fits to the NYC or Chicago data prior to lockdown implies much higher CV estimates, in the range 2.4 to 2.9 if all inhomogeneneity is social-connectivity related, in non-lockdown circumstances. The corresponding estimates for nine of the worst hit US States range from 1.9 to 3.4.[20]
[1] One example, further supporting my superspreader-based evidence of variability in social connectivity, is Miller et al: Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel medRxiv 22 May 2020 https://doi.org/10.1101/2020.05.21.20104521 This paper shows that 1-10% of infected individuals caused 80% of infections. That points to variability in social connectivity related susceptibility and infectivity quite likely being higher than I modelled .
[2] Aguas, R. and co-authors: Herd immunity thresholds estimated from unfolding epidemics” medRxiv 24 July 2020 https://doi.org/10.1101/2020.07.23.20160762
[3] Gomes, M. G. M., et al.: Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold. medRxiv 2 May 2020. https://www.medrxiv.org/content/10.1101/2020.04.27.20081893v1
[4] The basic reproduction number of an epidemic, R 0 , measures how many people, on average, each infected individual infects at the start of the epidemic. If R 0 exceeds one, the epidemic will grow, exponentially at first. But, assuming recovered individuals are immune, the pool of susceptible individuals shrinks over time and the current reproduction number falls. The proportion of the population that have been infected at the point where the current reproduction number falls to one is the ‘herd immunity threshold’ (HIT). Beyond that point the epidemic is under control, and shrinks.
[5] Neil M Ferguson et al.: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College COVID-19 Response Team Report 9, 16 March 2020, https://spiral.imperial.ac.uk:8443/handle/10044/1/77482
[6] Grifoni, A.et al.: Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals. Cell 11420, 2020 https://doi.org/10.1016/j.cell.2020.05.015
[7] Braun, J., et al.: Presence of SARS-CoV-2 reactive T cells in COVID-19 patients and healthy donors. medRxiv 22 April 2020 https://www.medrxiv.org/content/10.1101/2020.04.17.20061440v1.
[8] Le Bert, N. et al.: Different pattern of pre-existing SARS-COV-2 specific T cell immunity in SARS-recovered and uninfected individuals. bioRxiv 27 May 2020. https://doi.org/10.1101/2020.05.26.115832
[9] Nelde, A. et al.: SARS-CoV-2 T-cell epitopes define heterologous and COVID-19-induced T-cell recognition. ResearchSquare 16 June 2020. https://www.researchsquare.com/article/rs-35331/v1
[10] Lee, C., Koohy, H., et al.: CD8+ T cell cross-reactivity against SARS-CoV-2 conferred by other coronavirus strains and influenza virus. bioRxiv 20 May 2020. https://doi.org/10.1101/2020.05.20.107292.
[11] Such seroprevalence is likely to significantly understate the proportion of the population who have had COVID-19, since asymptomatic or mild disease often results in undetectably low antibody levels (Long, Q. X. et al.: Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat Med. 18 June 2020 https://doi.org/10.1038/s41591-020-0965-6 . Such patients will nevertheless be immune to reinfection (Sekine, K. et al.: Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19. bioRxiv 29 June 2020 https://doi.org/10.1101/2020.06.29.174888).965-6
[12] https://www.ft.com/content/a26fbf7e-48f8-11ea-aeb3-955839e06441. Data updated to 13 July
[13] 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
[14] I think its title gives a slightly misleading impression, although that issue is not central to their paper. It is in fact “persistent” heterogeneity that causes “short-term” overdispersion, albeit that over a short period random variability will have a significant influence. I’m unconvinced by their argument that estimating social-connectivity related susceptibility and infectivity from overdispersion in transmission statistics is likely to lead to significant bias, provided that estimation is based on large-scale transmission and not just a few superspreader events.
[15] Doing so involves dependency on an estimate of the infection fatality rate, but their IFR-inferred proportion of the New York City population that had been infected by early June 2020 looks reasonable, based on the NYS survey suggesting 22.7% of NYC residents had been infected by late March and the ratio of cumulative COVID-19 deaths 23 days later.
[16] They term their λ an “immunity factor”, but it is only partly related to biological immunity. If the causative inhomogeneity is related to biological immunity, λ = 1 + CV2, whereas if it is related to social connectivity (which affects infectivity as well as susceptibility) λ = 1 + 2CV2.
[17] They also obtian a similar estimate for Chicago, but based on a much narrower range of data.
[18] Or λ = 4.7 for a selected subset of States.
[19] The ratio of the standard deviation to the mean of a random variable.
[20] I have excluded NY State data, as their curve for that State shows abnormal behaviour, quite likely due to the early epidemic data being strongly dominated by NY City
Originally posted here, where a pdf copy is also available
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climateetc--2020-07-25--Apocalypse Never and False Alarm

Apocalypse Never and False Alarm

Two important new books to discuss .
Apocalypse Never : Why Environmental Alarm Hurts Us All , by Michael Schellenberger [ amazon ] ‘ Best Seller ’ Schellenberger ’ s op-ed : On Behalf of Environmentalists I Apologize For the Climate Scare [ link ] originally published at Forbes , but then they pulled it .
Review in Daily Mail : The eco-warrior who ’ s made greens see red [ link ] Schellenberger was banned for awhile from Facebook for his ‘ heretical ’ posts about the book [ link ] The KIDZ at Climate Feedback don ’ t @ @ @ @ @ @ @ content free ‘ fact check ’ [ link ] RealClimate : Michael Tobis makes a more serious effort to critique the book , but lands few punches .
[ link ] False Alarm : How Climate Change Panic Costs Us Trillions , Hurts the Poor , and Fails to Fix the Planet , by Bjorn Lomborg [ amazon ] Read the first 25 pages here [ link ] New York Times thinks this is a dangerous book [ link ] Alex Trembath has a good review of both books : Alternatives to Climate @ @ @ @ @ @ @ , well worth reading .
Refreshing and complementary perspectives .
Seems like Schellenberger is taking more ‘ heat ’ from the usual consensus enforcers ; apparently they figure that that they ’ ve already sufficiently dismissed Lomborg as a ‘ denier.
’ Not sure how much bandwidth there is for climate change at the moment , but it seems the sales especially for Schellenberger ’ s book are really taking off .

by Judith Curry
Two important new books to discuss.
Apocalypse Never: Why Environmental Alarm Hurts Us All, by Michael Schellenberger [amazon] ‘Best Seller’
Schellenberger’s op-ed: On Behalf of Environmentalists I Apologize For the Climate Scare [link] originally published at Forbes, but then they pulled it.
Schellenberger’s slide deck for the book [link]
Review in Daily Mail: The eco-warrior who’s made greens see red [link]
Review from Environmental Progress [link]
Schellenberger was banned for awhile from Facebook for his ‘heretical’ posts about the book [link]
The KIDZ at Climate Feedback don’t like the book; snooty, mostly content free ‘fact check’ [link]
RealClimate: Michael Tobis makes a more serious effort to critique the book, but lands few punches. [link]
False Alarm: How Climate Change Panic Costs Us Trillions, Hurts the Poor, and Fails to Fix the Planet, by Bjorn Lomborg [amazon]
Read the first 25 pages here [link]
New York Times thinks this is a dangerous book [link]
Michael Schermer’s interview with Lomborg [link]
——
Alex Trembath has a good review of both books: Alternatives to Climate Alarmism
I’ve read both books, well worth reading. Refreshing and complementary perspectives.
Seems like Schellenberger is taking more ‘heat’ from the usual consensus enforcers; apparently they figure that that they’ve already sufficiently dismissed Lomborg as a ‘denier.’
Not sure how much bandwidth there is for climate change at the moment, but it seems the sales especially for Schellenberger’s book are really taking off.
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climateetc--2020-06-28--The progress of the COVID-19 epidemic in Sweden: an analysis

The course of the COVID-19 pandemic in Sweden is of great interest , as it is one of very few advanced nations where no lockdown order that heavily restricted people ’ s movements and other basic freedoms was imposed .
As there has been much comment , some of it ill-informed , on how the COVID-19 epidemic has developed in Sweden , but relatively little detailed analysis published in English , it is worth exploring what their excellent publicly-available data reveal .
I present here plots of weekly new cases and deaths , with accompanying comments .
I have been @ @ @ @ @ @ @ 2 April on .
[ 1 ] Overall development of the epidemic Figure 1 shows the overall picture for confirmed weekly total new COVID-19 cases , intensive care admissions and deaths in Sweden .
The dashed line normalises new cases by the dividing by the number of tests carried out each week , relative to those for the week to 10 April .
Both actual and normalised weekly new cases have been divided by 10 in order to make their scale comparable to that for ICU admissions and deaths .
• Recorded new @ @ @ @ @ @ @ after gently declining from then until late May have doubled since then .
All or most of that increase appears to have been be due to a decision on 5 June to expand testing .
By the end of June testing was at about double the rate in May , which in turn averaged approximately 25 % higher than in April .
• Allowing for the increase in testing over time , the incidence of COVID-19 appears to have declined by about 30 % from mid-April until the late May and has since @ @ @ @ @ @ @ in mid-April and have fallen by two-thirds since then .
• It follows that the infection fatality rate has fallen substantially since early in the epidemic .
• Deaths have been remarkably high in relation to confirmed cases .
To a substantial extent this reflects Swedish policy of focussing testing in hospital , where the disease is severe , prior to June .
It also reflects the spread of infections to care homes , where death rates are very high , relatively early in the epidemic .
• Far more people have died @ @ @ @ @ @ @ care unit ( ICU ) .
This likely reflects a combination of deaths outside hospital and an apparent Swedish policy of not generally putting people aged 80+ into intensive care .
[ 2 ] For admissions before late April , 49 % of 70+ year old patients ( who only comprised 15 % of total ICU patients ) died , and most of those were people in their seventies , for whom the prognosis is much better than for older patients .
The changing age composition of new cases over time is shown @ @ @ @ @ @ @ cases among people aged 70+ has been declining since mid-April .
Until the end of May this more than countered gently rising infections in age groups younger than 60 , with infections among 60-69 year olds broadly stable .
But since then confirmed infections in people under 60 have rocketed , and there has also been a noticeable increase in infections among people aged 60-69 .
It is likely that the bulk of these increases simply reflect the widening of testing in June .
The total number of tests in the latest week @ @ @ @ @ @ @ The Swedish Public Health Agency estimates that the current reproduction number , R , in Sweden during mid- June was approximately 0.9 , implying a decline of about 10 % a week in the true number of new infections .
However , comparing Figures 1 and 2 suggests that while infections in the oldest age groups are reducing steadily , this is being counterbalanced by an increase in infections among young people , resulting in little overall trend in the total number of infections .
Weekly COVID-19 recorded deaths have declined very substantially @ @ @ @ @ @ @ ( Figure 3 ) .
The data show the number of people with confirmed COVID-19 who died , regardless of the cause of death .
Figure 4 shows weekly deaths by age group as a proportion of new cases two weeks previously .
Since most cases are only confirmed after hospitalisation , the average delay from a case being confirmed to death occurring is around two weeks .
[ 3 ] This ratio therefore should provide a reasonable estimate of the hospital case fatality ratio ( CFR ) .
It has declined substantially @ @ @ @ @ @ @ less for ages 80+ , where it remains very high , although not unusually so .
The estimated hospital CFR for ages 50–69 has declined by an order of magnitude , while that for ages 70–79 has declined by a factor of three .
The reasons for this extremely welcome trend are not entirely clear .
Much of the reduction over the last few weeks is very likely due to a widening of the testing regime to include milder cases of disease , with more cases now being found outside hospitals .
[ @ @ @ @ @ @ @ of the decline is likely real : most countries have seen a decline in the hospital CFR over time .
Fig .
4 Weekly COVID-19 deaths by age group in Sweden as fraction of new cases 2 weeks before The decline in total deaths ( Figure 1 ) is to a fair extent due to the changing age composition of new cases , as well as a moderate reduction in total infections , with an increasing proportion of cases among younger age groups and a substantial decrease at ages 80+ , which account @ @ @ @ @ @ @ of the key older age groups , which account for the vast bulk of deaths , the number of deaths ( Figure 3 ) appears to have dropped in relation to the number of cases as adjusted for the widening of testing – certainly for ages 60-69 .
The Swedish Public Health Agency estimated the infection fatality rate ( IFR ) , based on a selection of confirmed cases that have been tested in Stockholm County and who fell ill on March 21-30 , 2020 , after adjusting by the estimated proportion of @ @ @ @ @ @ @ [ 5 ] Of all people who they estimated to be infected with COVID-19 around the second half of March , 0.58 % died .
[ 6 ] In the group At age 70 and over , mortality was 4.3 % ( 1.9 % for ages 70-79 ) , while it was 0.09 % for persons younger than 70 years .
[ 7 ] [ 8 ] The overall deaths were pushed up by the spread of infections to care homes , residents of which accounted at that point for 40 % –50 @ @ @ @ @ @ @ of magnitude decline in COVID-19 CFR at ages below 70 since mid-April implies that , even assuming that all the change in confirmed cases between early April and mid June is due to the three-fold expansion of testing , the IFR below age 70 is now much lower than the previously estimated 0.09 % .
The data indicate a current level of circa 0.015 % .
Even for ages 60-69 , where the IFR for infection in mid-March was estimated at 0.45 % , the implied IFR is now little over 0.1 % @ @ @ @ @ @ @ caused such a large reduction in IFRs for COVID-19 at ages below 70 years .
Sweden is not the only country in which the IFR appears to have declined .
There is little evidence of the SARS-CoV-2 virus having mutated and become less damaging .
However , treatment of serious cases has no doubt improved ; less use of invasive ventilation could be one factor here .
Seasonal factors may also play a role .
The body ’ s ability to fight infections and heal damage appears to be affected by sunlight exposure @ @ @ @ @ @ @ pathways perhaps both being involved .
Another important factor in the decline in the IFR over time could be population heterogeneity in susceptibility to COVID-19 infection , in particular variability arising from biological rather than social connectivity factors .
More susceptible individuals may not only be more likely to succumb to COVID-19 , but more likely to die of the disease once infected .
Since more susceptible individuals will tend to become infected earlier in the epidemic than less susceptible individuals , a correlation between susceptibility to infection and risk of death if @ @ @ @ @ @ @ declining over the course of the epidemic .
[ 9 ] I turn now to regional analysis .
Figure 5 shows weekly confirmed new cases for each of the 21 regions in Sweden .
Although widening of testing varied between regions , it is evident that Västra Götaland dominates the overall increase in cases over time .
However , the increase in testing during the first half of June appears to have been much larger in Västra Götaland than elsewhere , so the true increase in disease incidence is likely far smaller than @ @ @ @ @ @ @ Cases in Jönköping also rose sharply in June , albeit from a much lower level .
Cases in the Stockholm region remain high .
Tracking serious cases only , so as to adjust for the widening of the test regime , comparison of the latest analysed week ( 24 – week ending19 June ) with the average of the previous three weeks shows an increasing number of cases in Västernorrland , decreasing numbers in Jönköping , Stockholm , Sörmland , Uppsala , Västra Götaland and Östergötland , and an unchanged number for other @ @ @ @ @ @ @ varying populations , so confirmed cases per 100,000 head of population give a better picture of relative disease incidence ( Figure 6 ) .
Fig .
6 Weekly COVID-19 confirmed cases per 100,000 head of population by region in Sweden Deaths by region are shown in Figure 7 .
They are dominated by deaths in Stockholm region , which continue to be higher than elsewhere , although since mid-May deaths in Västra Götaland have become a much more substantial proportion of the total .
The very large and almost monotonic decrease in deaths @ @ @ @ @ @ @ less steep decline in cases from mid-April to the end of May ( which occurred despite some increase in testing ) is genuine .
As already indicated , the sharp increase in confirmed cases in June is an artefact produced by the widening of testing ; serious cases decline in Stockholm county .
Analysis of samples collected at week 21 ( late May ) shows that antibodies were detected at 6.3 % of the studied population .
Seroprevalence remains lowest among adults 65-95 years ( 3.9 % ) , compared to adults 20-64 @ @ @ @ @ @ @ 0-19 years ( 7.5 % ) .
The Stockholm region had the highest proportion of antibody positive ( 10 % ) followed by Skåne ( 4.5 % ) and Västra Götaland ( 2.7 % ) .
The number of collected samples for other regions were less than 100 and therefore can not provide reliable results .
[ 11 ] Cumulative confirmed cases as a proportion of population up to the end of week 19 ( 15 May ) , who would be expected to develop antibodies by early in week 21 , were @ @ @ @ @ @ @ , 0.47 % for Stockholm region , 0.11 % for Skane and 0.29 % for Västra Götaland .
So , up to mid-May , if the foregoing prevalence estimates are correct then confirmed cases represented about 1 in 20 of people who had developed antibodies to the SARS-CoV-2 virus , but with an inter-region range of at least 1 in 40 to 1 in 10 .
However , previous results showed higher prevalence , and prevalence can not decrease over time .
A fairly large sample study that sought response from a random @ @ @ @ @ @ @ obtained 446 valid results from tests on an average date of 11 April ( week 16 ) , reflecting infections up to late March .
It found 10 % prevalence .
[ 12 ] And a study based on blood donors showed about 11 % had developed antibodies in mid-April , although the sample size was only 100 .
Moreover , the antibody tests used appear to have a sensitivity of only 70-80 percent , so 20-30 % will test negative even though they have been infected .
But the tests have 100 @ @ @ @ @ @ @ false positively .
[ 13 ] There is no indication that any of these seroprevalence results have been adjusted for the relatively low sensitivity of the test used .
So it seems likely that in all cases the true prevalence was 25-43 % higher than that reported .
Many people with mild COVID-19 symptoms or asymptomatic infection probably do not develop antibodies in their blood , either developing antibodies only in their mucus or not at all .
Antibodies are only one component of the adaptive immune system , and immunity conveyed by @ @ @ @ @ @ @ be more important ; it is likely to be longer lasting .
Having regard to all these factors , it seems entirely possible that 20 % rather than 10 % would be a better estimate of the proportion of the population of Stockholm region that had been infected by late May .
It seems clear from the trend in cases since then that , as I intimated previously [ 14 ] , by mid- April the herd immunity threshold had been reached in Stockholm county .
Moreover , both there and in Västra @ @ @ @ @ @ @ dominated deaths to date , deaths have been declining since mid-May .
Regression of the logarithmic change in deaths over the last seven weekly data points gives similar results in both regions .
The fits are good [ 15 ] and the slope estimates , averaging them , imply that each week ’ s death figure is only 81 % to 86 % ( at ± 1 standard error ) of the previous week ’ s figure .
Skåne , the next largest region by population , also has a downward trend in @ @ @ @ @ @ @ less good and the slope estimate is not significant ( p = 0.1 ) .
Those three regions make up 53 % of Sweden ’ s population .
For Sweden as a whole the fit is as good as for Stockholm country and Västra Götaland , with a slightly slower rate of decline : each week ’ s estimate deaths are 84 % to 88 % of the previous week ’ s .
This range is slightly below the R value in mid-June estimated by the Swedish Public Health Agency , which has @ @ @ @ @ @ @ likely to reflect the declining proportion of new cases made up by older age groups ( Figure2 ) and the declining infection fatality rate for ages under 70 .
If the estimated logarithmic rate of decline in weekly COVID-19 deaths in Sweden seen over the last six weeks continues , only about 1,100 further deaths would occur .
That would bring total deaths up to approximately 6,400 , or 0.06 % of the total population .
Of the first 4,500 deaths , some 40 % involved people living in care homes , [ @ @ @ @ @ @ @ earlier in the epidemic .
To date , the average age at death was 82 , and in only 6 % of cases did a death not involve a co-morbidity ( other health condition ) .
Notwithstanding that a month ago antibodies were only detected in 6.3 % of the Swedish population , the declining death rate since mid-May strongly suggests that the herd immunity threshold had been surpassed in the three largest regions , and in Sweden as a whole , by the end of April .
In the absence of a @ @ @ @ @ @ @ that the epidemic will peter out after a thousand or so more deaths , implying an overall infection fatality rate of 0.06 % of the population ( 0.04 % excluding COVID-19 deaths of people in care homes ) .
This is broadly comparable to excess deaths from influenza infections over two successive above-average seasons , such as 2016–17 plus 2017–18 .
[ 17 ] The absence of a lockdown order , with the government largely trusting people to make their own individual decisions regarding their behaviour , informed by their particular circumstances , @ @ @ @ @ @ @ disruption and reduction of people ’ s autonomy in Sweden than in most other western European countries .
While this has also meant that COVID-19 deaths to date have been higher than in some ( but not all ) other countries in which a lockdown was imposed , the wider spread of the epidemic in Sweden means that the future COVID-19 outlook there is better .
The herd immunity threshold is likely lower at present than it would be if people were behaving completely normally ; it may also be seasonally lower .
@ @ @ @ @ @ @ since the peak of the epidemic , particularly among young people , should provide some margin of safety against its resurging when behaviour returns closer to normal and summer ends .
That is , there is less risk of a second wave of the epidemic next winter .
And if a second wave occurs , fewer measures should be needed to control it than in other countries .
Originally posted here , where a pdf copy is also available [ 1 ] On 2 April 2020 the cumulative number of cases was only @ @ @ @ @ @ @ 5 % of the deaths recorded to date had occurred .
Updated data for 19 , 20 and 21 June were not prepared .
In order to avoid these days and plot as many weeks as possible , I use data for Thursday 2 April 2020 and each 7 days thereafter , ending 25 June .
For consistency between the breakdowns by age and region , I use the data as originally reported on each date , not the final adjusted daily figures ( which are not reported by age group ) .
@ @ @ @ @ @ @ particularly for deaths .
[ 2 ] Such a policy would be understandable , as the prognosis for 80+ year-olds given invasive ventilation in ICUs is extremely poor , with many of those who do survive then having a poor quality of life .
[ 3 ] The average delay between a case being confirmed and death occurring is 12 days .
( https : //www.folkhalsomyndigheten.se/publicerat-material/publikationsarkiv/t/the-infection-fatality-rate-of-covid-19-in-stockholm-technical-report/ ) However , there is a significant delay between sickness arising and cases being confirmed .
On average , it takes 11 days between sickness and the @ @ @ @ @ @ @ ] The earlier increase in testing , from mid-April to mid-May , appears to have largely been targeted at , and detected more cases in , health and care staff .
In week 20 ( mid-May ) the proportion of confirmed cases comprised of healthcare and care staff was as high as 74 % in the Kronoberg region .
https : //www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-20-final.pdf [ 5 ] They assumed that the estimated infections were spread evenly over the population of the Stockholm region , that is an age-independent atack rate .
[ 6 ] Measuring @ @ @ @ @ @ @ in the relevant period over the estimated average number suggests a slightly higher overall mortality rate of 0.7 % .
However , mortality earlier in the 2019-20 winter period was lower than average , as it also was in the 2018-19 winter , so there were probably more than usual very old people likely to die from infection when COVID-19 struck .
By week 23 ( week ending 5 June 2020 ) the difference between the two measures was only about 10 % ( 4,811 vs 5,353 ) .
( https : //www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-25-final.pdf @ @ @ @ @ @ @ the variability involved arose from whether and how many cross-reactive T-cells – such as arise from previous exposure to common cold coronaviruses – an individual possessed , the much smaller decline in IFR for 70+ age groups might perhaps be due to the T-cell senescence that occurs in old people .
[ 15 ] The R-squared is close to 0.90 in both cases .

By Nic Lewis
The course of the COVID-19 pandemic in Sweden is of great interest, as it is one of very few advanced nations where no lockdown order that heavily restricted people’s movements and other basic freedoms was imposed. As there has been much comment, some of it ill-informed, on how the COVID-19 epidemic has developed in Sweden, but relatively little detailed analysis published in English, it is worth exploring what their excellent publicly-available data reveal.
I present here plots of weekly new cases and deaths, with accompanying comments. I have been able to access detailed daily data from 2 April on.[1]
Overall development of the epidemic
Figure 1 shows the overall picture for confirmed weekly total new COVID-19 cases, intensive care admissions and deaths in Sweden. The dashed line normalises new cases by the dividing by the number of tests carried out each week, relative to those for the week to 10 April. Both actual and normalised weekly new cases have been divided by 10 in order to make their scale comparable to that for ICU admissions and deaths.
Fig. 1 Total weekly COVID-19 confirmed cases, intensive care admissions and deaths in Sweden
Several points are noteworthy:
Recorded new cases continued rising until mid-April, and after gently declining from then until late May have doubled since then. All or most of that increase appears to have been be due to a decision on 5 June to expand testing. By the end of June testing was at about double the rate in May, which in turn averaged approximately 25% higher than in April.
Allowing for the increase in testing over time, the incidence of COVID-19 appears to have declined by about 30% from mid-April until the late May and has since remained broadly stable.
Deaths peaked in mid-April and have fallen by two-thirds since then.
It follows that the infection fatality rate has fallen substantially since early in the epidemic.
Deaths have been remarkably high in relation to confirmed cases. To a substantial extent this reflects Swedish policy of focussing testing in hospital, where the disease is severe, prior to June. It also reflects the spread of infections to care homes, where death rates are very high, relatively early in the epidemic.
Far more people have died than have been put into an intensive care unit (ICU). This likely reflects a combination of deaths outside hospital and an apparent Swedish policy of not generally putting people aged 80+ into intensive care.[2] For admissions before late April, 49% of 70+ year old patients (who only comprised 15% of total ICU patients) died, and most of those were people in their seventies, for whom the prognosis is much better than for older patients.
Analysis by age group
The changing age composition of new cases over time is shown in Figure 2. The number of cases among people aged 70+ has been declining since mid-April. Until the end of May this more than countered gently rising infections in age groups younger than 60, with infections among 60-69 year olds broadly stable. But since then confirmed infections in people under 60 have rocketed, and there has also been a noticeable increase in infections among people aged 60-69.
It is likely that the bulk of these increases simply reflect the widening of testing in June. The total number of tests in the latest week was double that four weeks previously. The Swedish Public Health Agency estimates that the current reproduction number, R t , in Sweden during mid- June was approximately 0.9, implying a decline of about 10% a week in the true number of new infections. However, comparing Figures 1 and 2 suggests that while infections in the oldest age groups are reducing steadily, this is being counterbalanced by an increase in infections among young people, resulting in little overall trend in the total number of infections.
Fig. 2 Weekly COVID-19 confirmed cases by age group in Sweden
Weekly COVID-19 recorded deaths have declined very substantially since mid-April, across all age groups (Figure 3). The data show the number of people with confirmed COVID-19 who died, regardless of the cause of death.
Fig. 3 Weekly COVID-19 recorded deaths by age group in Sweden
Figure 4 shows weekly deaths by age group as a proportion of new cases two weeks previously. Since most cases are only confirmed after hospitalisation, the average delay from a case being confirmed to death occurring is around two weeks.[3] This ratio therefore should provide a reasonable estimate of the hospital case fatality ratio (CFR). It has declined substantially for all age groups, albeit proportionately less for ages 80+, where it remains very high, although not unusually so.
The estimated hospital CFR for ages 50–69 has declined by an order of magnitude, while that for ages 70–79 has declined by a factor of three. The reasons for this extremely welcome trend are not entirely clear. Much of the reduction over the last few weeks is very likely due to a widening of the testing regime to include milder cases of disease, with more cases now being found outside hospitals.[4] Nevertheless, a good part of the decline is likely real: most countries have seen a decline in the hospital CFR over time.
Fig. 4 Weekly COVID-19 deaths by age group in Sweden as fraction of new cases 2 weeks before
Infection fatality rate over time
The decline in total deaths (Figure 1) is to a fair extent due to the changing age composition of new cases, as well as a moderate reduction in total infections, with an increasing proportion of cases among younger age groups and a substantial decrease at ages 80+, which account for most deaths. But within some of the key older age groups, which account for the vast bulk of deaths, the number of deaths (Figure 3) appears to have dropped in relation to the number of cases as adjusted for the widening of testing – certainly for ages 60-69.
The Swedish Public Health Agency estimated the infection fatality rate (IFR), based on a selection of confirmed cases that have been tested in Stockholm County and who fell ill on March 21-30, 2020, after adjusting by the estimated proportion of infections that resulted in confirmed cases.[5] Of all people who they estimated to be infected with COVID-19 around the second half of March, 0.58% died.[6] In the group At age 70 and over, mortality was 4.3% (1.9% for ages 70-79) , while it was 0.09% for persons younger than 70 years.[7] [8] The overall deaths were pushed up by the spread of infections to care homes, residents of which accounted at that point for 40%–50% of total deaths.
The order of magnitude decline in COVID-19 CFR at ages below 70 since mid-April implies that, even assuming that all the change in confirmed cases between early April and mid June is due to the three-fold expansion of testing, the IFR below age 70 is now much lower than the previously estimated 0.09%. The data indicate a current level of circa 0.015%. Even for ages 60-69, where the IFR for infection in mid-March was estimated at 0.45%, the implied IFR is now little over 0.1%.
It is not clear what has caused such a large reduction in IFRs for COVID-19 at ages below 70 years. Sweden is not the only country in which the IFR appears to have declined. There is little evidence of the SARS-CoV-2 virus having mutated and become less damaging. However, treatment of serious cases has no doubt improved; less use of invasive ventilation could be one factor here. Seasonal factors may also play a role. The body’s ability to fight infections and heal damage appears to be affected by sunlight exposure, with vitamin D and nitric oxide pathways perhaps both being involved.
Another important factor in the decline in the IFR over time could be population heterogeneity in susceptibility to COVID-19 infection, in particular variability arising from biological rather than social connectivity factors. More susceptible individuals may not only be more likely to succumb to COVID-19, but more likely to die of the disease once infected. Since more susceptible individuals will tend to become infected earlier in the epidemic than less susceptible individuals, a correlation between susceptibility to infection and risk of death if infected would automatically lead to the IFR declining over the course of the epidemic.[9]
Regional analysis
I turn now to regional analysis. Figure 5 shows weekly confirmed new cases for each of the 21 regions in Sweden. Although widening of testing varied between regions, it is evident that Västra Götaland dominates the overall increase in cases over time. However, the increase in testing during the first half of June appears to have been much larger in Västra Götaland than elsewhere, so the true increase in disease incidence is likely far smaller than the increase in confirmed cases suggests. Cases in Jönköping also rose sharply in June, albeit from a much lower level. Cases in the Stockholm region remain high.
Tracking serious cases only, so as to adjust for the widening of the test regime, comparison of the latest analysed week (24 – week ending19 June) with the average of the previous three weeks shows an increasing number of cases in Västernorrland, decreasing numbers in Jönköping, Stockholm, Sörmland, Uppsala, Västra Götaland and Östergötland, and an unchanged number for other regions.[10]
Fig. 5 Weekly COVID-19 confirmed cases by region in Sweden
Regions have varying populations, so confirmed cases per 100,000 head of population give a better picture of relative disease incidence (Figure 6).
Fig. 6 Weekly COVID-19 confirmed cases per 100,000 head of population by region in Sweden
Deaths by region are shown in Figure 7. They are dominated by deaths in Stockholm region, which continue to be higher than elsewhere, although since mid-May deaths in Västra Götaland have become a much more substantial proportion of the total.
Fig. 7 Weekly COVID-19 recorded deaths by region in Sweden
The very large and almost monotonic decrease in deaths in Stockholm region, confirms that the less steep decline in cases from mid-April to the end of May (which occurred despite some increase in testing) is genuine. As already indicated, the sharp increase in confirmed cases in June is an artefact produced by the widening of testing; serious cases decline in Stockholm county.
Prevalence of COVID-19 infections
Analysis of samples collected at week 21 (late May) shows that antibodies were detected at 6.3% of the studied population. Seroprevalence remains lowest among adults 65-95 years (3.9%), compared to adults 20-64 years (7.6%) and children 0-19 years (7.5%). The Stockholm region had the highest proportion of antibody positive (10%) followed by Skåne (4.5%) and Västra Götaland (2.7%). The number of collected samples for other regions were less than 100 and therefore cannot provide reliable results.[11]
Cumulative confirmed cases as a proportion of population up to the end of week 19 (15 May), who would be expected to develop antibodies by early in week 21, were 0.32% for the whole of Sweden, 0.47% for Stockholm region, 0.11% for Skane and 0.29% for Västra Götaland. So, up to mid-May, if the foregoing prevalence estimates are correct then confirmed cases represented about 1 in 20 of people who had developed antibodies to the SARS-CoV-2 virus, but with an inter-region range of at least 1 in 40 to 1 in 10.
However, previous results showed higher prevalence, and prevalence cannot decrease over time. A fairly large sample study that sought response from a random sample of households in the Stockholm area obtained 446 valid results from tests on an average date of 11 April (week 16), reflecting infections up to late March. It found 10% prevalence.[12] And a study based on blood donors showed about 11% had developed antibodies in mid-April, although the sample size was only 100.
Moreover, the antibody tests used appear to have a sensitivity of only 70-80 percent, so 20-30% will test negative even though they have been infected. But the tests have 100% specificity – no one will test false positively.[13] There is no indication that any of these seroprevalence results have been adjusted for the relatively low sensitivity of the test used. So it seems likely that in all cases the true prevalence was 25-43% higher than that reported.
Many people with mild COVID-19 symptoms or asymptomatic infection probably do not develop antibodies in their blood, either developing antibodies only in their mucus or not at all. Antibodies are only one component of the adaptive immune system, and immunity conveyed by the development of SARS-CoV-2 specific T-cells may be more important; it is likely to be longer lasting.
Having regard to all these factors, it seems entirely possible that 20% rather than 10% would be a better estimate of the proportion of the population of Stockholm region that had been infected by late May.
Future outlook
It seems clear from the trend in cases since then that, as I intimated previously[14], by mid- April the herd immunity threshold had been reached in Stockholm county. Moreover, both there and in Västra Götaland, the two regions that have dominated deaths to date, deaths have been declining since mid-May. Regression of the logarithmic change in deaths over the last seven weekly data points gives similar results in both regions. The fits are good[15] and the slope estimates, averaging them, imply that each week’s death figure is only 81% to 86% ( at ± 1 standard error) of the previous week’s figure. Skåne, the next largest region by population, also has a downward trend in deaths, but the regression fit is less good and the slope estimate is not significant (p = 0.1). Those three regions make up 53% of Sweden’s population. For Sweden as a whole the fit is as good as for Stockholm country and Västra Götaland , with a slightly slower rate of decline: each week’s estimate deaths are 84% to 88% of the previous week’s. This range is slightly below the R value in mid-June estimated by the Swedish Public Health Agency, which has fluctuated around 0.9. The difference is likely to reflect the declining proportion of new cases made up by older age groups (Figure2) and the declining infection fatality rate for ages under 70.
If the estimated logarithmic rate of decline in weekly COVID-19 deaths in Sweden seen over the last six weeks continues, only about 1,100 further deaths would occur. That would bring total deaths up to approximately 6,400, or 0.06% of the total population. Of the first 4,500 deaths, some 40% involved people living in care homes,[16] a slightly lower proportion than earlier in the epidemic. To date, the average age at death was 82, and in only 6% of cases did a death not involve a co-morbidity (other health condition).
Conclusions
Notwithstanding that a month ago antibodies were only detected in 6.3% of the Swedish population, the declining death rate since mid-May strongly suggests that the herd immunity threshold had been surpassed in the three largest regions, and in Sweden as a whole, by the end of April.
In the absence of a change in trends, it seems likely that the epidemic will peter out after a thousand or so more deaths, implying an overall infection fatality rate of 0.06% of the population (0.04% excluding COVID-19 deaths of people in care homes). This is broadly comparable to excess deaths from influenza infections over two successive above-average seasons, such as 2016–17 plus 2017–18.[17]
The absence of a lockdown order, with the government largely trusting people to make their own individual decisions regarding their behaviour, informed by their particular circumstances, has enabled life to continue with less disruption and reduction of people’s autonomy in Sweden than in most other western European countries. While this has also meant that COVID-19 deaths to date have been higher than in some (but not all) other countries in which a lockdown was imposed, the wider spread of the epidemic in Sweden means that the future COVID-19 outlook there is better.
The herd immunity threshold is likely lower at present than it would be if people were behaving completely normally; it may also be seasonally lower. However, the continuing spread of infections since the peak of the epidemic, particularly among young people, should provide some margin of safety against its resurging when behaviour returns closer to normal and summer ends. That is, there is less risk of a second wave of the epidemic next winter. And if a second wave occurs, fewer measures should be needed to control it than in other countries.
Nicholas Lewis
Originally posted here, where a pdf copy is also available
[1] On 2 April 2020 the cumulative number of cases was only one-twelfth its current level, and only 5% of the deaths recorded to date had occurred. Updated data for 19, 20 and 21 June were not prepared. In order to avoid these days and plot as many weeks as possible, I use data for Thursday 2 April 2020 and each 7 days thereafter, ending 25 June. For consistency between the breakdowns by age and region, I use the data as originally reported on each date, not the final adjusted daily figures (which are not reported by age group). There is some lag in reporting, particularly for deaths.
[2] Such a policy would be understandable, as the prognosis for 80+ year-olds given invasive ventilation in ICUs is extremely poor, with many of those who do survive then having a poor quality of life.
[3] The average delay between a case being confirmed and death occurring is 12 days. (https://www.folkhalsomyndigheten.se/publicerat-material/publikationsarkiv/t/the-infection-fatality-rate-of-covid-19-in-stockholm-technical-report/) However, there is a significant delay between sickness arising and cases being confirmed. On average, it takes 11 days between sickness and the start of intensive care.
[4] The earlier increase in testing, from mid-April to mid-May, appears to have largely been targeted at, and detected more cases in, health and care staff. In week 20 (mid-May) the proportion of confirmed cases comprised of healthcare and care staff was as high as 74% in the Kronoberg region. https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-20-final.pdf
[5] They assumed that the estimated infections were spread evenly over the population of the Stockholm region, that is an age-independent atack rate.
[6] Measuring instead by the number of excess deaths in the relevant period over the estimated average number suggests a slightly higher overall mortality rate of 0.7%. However, mortality earlier in the 2019-20 winter period was lower than average, as it also was in the 2018-19 winter, so there were probably more than usual very old people likely to die from infection when COVID-19 struck. By week 23 (week ending 5 June 2020) the difference between the two measures was only about 10% (4,811 vs 5,353). (https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-25-final.pdf)
[7] https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-24_final.pdf
[8] https://www.folkhalsomyndigheten.se/contentassets/53c0dc391be54f5d959ead9131edb771/infection-fatality-rate-covid-19-stockholm-technical-report.pdf
[9] If much of the variability involved arose from whether and how many cross-reactive T-cells – such as arise from previous exposure to common cold coronaviruses – an individual possessed, the much smaller decline in IFR for 70+ age groups might perhaps be due to the T-cell senescence that occurs in old people.
[10] https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-25-final.pdf
[11] https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-24_final.pdf
[12] https://www.kth.se/aktuellt/nyheter/10-procent-av-stockholmarna-smittade-1.980727
[13] https://www.svt.se/nyheter/inrikes/11-procent-av-stockholmarna-har-antikroppar-mot-covid-19
[14] https://judithcurry.com/2020/05/10/why-herd-immunity-to-covid-19-is-reached-much-earlier-than-thought/
[15] The R-squared is close to 0.90 in both cases.
[16] https://www.folkhalsomyndigheten.se/globalassets/statistik-uppfoljning/smittsamma-sjukdomar/veckorapporter-covid-19/2020/covid-19-veckorapport-vecka-22-final.pdf
[17] https://www.folkhalsomyndigheten.se/contentassets/4b4dd8c7e15d48d2be744248794d1438/rapport-overdodlighet-vecka-23-2020.pdf
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climateetc--2020-06-25--Mass spectrometry and climate science. Part II

New technologies in mass spectrometry are advancing research in climate science This is the second of a two-part posting based on a presentation prepared for the American Chemical Society ’ s National Meeting in March 2020 .
The meeting was cancelled , but the key points in that presentation ( plus a couple of added examples based on new publications of relevant research ) are in these two posts .
The first example in this part on the application of mass spectrometry to understanding recent climate and weather will look at a method used to obtain hourly to daily resolution @ @ @ @ @ @ @ past .
There are a variety of ways of reconstructing past climate changes , but most of them do not have sufficient time resolution to identify short term weather events , such as extreme storms or heat or cold periods ( “ weather-timescale extreme events ( WEEs … ) ” ] ) .
Yet the frequency and impact of such events could help developing better knowledge of past climate and its influencers .
A new study analyzed a marine bivalve in the South China Sea elemental ratios by NanoSIMS ( Nano Secondary Ion @ @ @ @ @ @ @ hourly resolution proxy records and confirm them using known daily weather for the location ( Figure 7 ) .
The technique could be applied to fossil shells from the distant past to obtain similar weather information .
The authors studied giant clam shells using the NanoSIMS technology to determine the iron/calcium ( Fe/Ca ) and strontium/calcium ( Sr/Ca ) ratios in different layers of the shells and were able to confirm the presence of nearby tropical cyclones ( Figure 8 ) and also the impact of cold weather surges on the shells ( @ @ @ @ @ @ @ Figure 7 : D is the layers of the shell by autofluorescence ; E and F are the NanoSIMS elemental ratios for indicated shell segments Figure 8 : Nearby tropical cyclones recorded in hourly Fe/Ca of Giant Clam shell obtained by NanoSIMS Figure 9 : Winter cold surges recorded in daily growth rate ( green ) and hourly Fe/Ca of Giant Clam shell ( red ) Understanding the makeup of different parts of the atmosphere and how they change is a major aspect of climate science , in addition to the study of @ @ @ @ @ @ @ spectrometry plays a key role in this area ( Figure 10 ) .
[ ii ] Why are aerosols important for understanding climate ?
Couds reflect incoming solar radiation back into space and also absorb outgoing radiation , keeping that energy inside the Earth ’ s atmosphere .
Aerosols are a key part of cloud formation , and the chemistry of aerosol particles influences the type of cloud they form .
Thus aerosols have a major influence on the greenhouse effect , as well as on other aspects of weather and climate .
@ @ @ @ @ @ @ aerosol particles and to follow their behavior in the atmosphere .
This essay is focused on mass spectrometry ( MS ) , and several recently-published examples will showcase how the different types of MS can address specific aspects of aerosol chemistry : • LC-EESI-TOF MS/MS to identify organic compound functional groups in aerosols ( TOF means Time Of Flight ) • TOF Chemical Ionization MS to follow reactions of sulfur-containing compounds in the atmosphere • Aerosol time-of-flight mass spectrometry ( ATOFMS ) and chemical ionization mass spectrometry ( CIMS ) to study formation @ @ @ @ @ @ @ salt aerosol particles These are selected from many publications in this area to show the impact of mass spectrometry on the study of aerosols , but are not intended to cover all aspects of the field !
Also , while this essay is focused on mass spectrometric technologies and their applications in climate science , I hope that the limited details about these technologies do not get in the way of understanding their significance for climate research .
Metals are often found in aerosols , but are hard to measure accurately when the @ @ @ @ @ @ @ laboratory .
A new approach using Extractive Electrospray Ionization ( EESI ) with a portable Time-of-Flight MS system has enabled field measurements .
[ iv ] Figure 12 : ( a ) Experimental mass spectrum of a mixture of zinc acetate , cadmium chloride hydrate , zinc acetate , cerium ( III ) acetate tetrahydrate , and barium acetate obtained with the EESI-TOF-MS. ( b ) Time series of an external mixture of lithium hydroxide monohydrate , lead acetate trihydrate , cadmium chloride hydrate , and cerium ( III ) acetate hydrate Organic @ @ @ @ @ @ @ OAs ) can have a substantial influence on how they develop and impact the atmosphere as they travel through it .
It is difficult to characterize the many components of the OAs , especially since they are constantly changing as they travel in the atmosphere .
New mass spectrometric technology is making it possible to improve the accuracy and completeness of atmospheric OA measurements in real time .
This enables measurement of secondary organic aerosols ( SOAs ) formed in atmospheric reactions as they happen .
Extractive Electrospray Ionization combined with ultra high @ @ @ @ @ @ @ relevant concentrations and a sufficient range of mass to cover the great variety of sizes of the OAs .
[ v ] Figures 13 and 14 show examples of studies of a common OA , α-pinene .
[ vi ] Figure 13 ( left ) : Mass spectra for unoxidized α-pinene ( top ) , α-pinene oxidized at different concentrations ( 2nd and 3rd ) , and limonene oxidation products ( bottom ) Figure 14 ( right ) : SOAs produced from these two compounds ( top and middle ) and measurements of @ @ @ @ @ @ @ active sites in organic molecules , for example the aromatic ring that makes up benzene and is in the many compounds that include its ring , or the -OH functional group that is present in alcohols and sugars .
Organic compounds with various functional groups are a significant component of aerosols , sourced , for instance , in atmospheric particulate matter .
A new approach uses LC-ESI-TOF MS/MS to quantify organic aerosol functional groups in environmental samples .
The scientists used this technique to study organic functional groups in air in Atlanta , @ @ @ @ @ @ @ , New York .
An example of their results is shown in Figure 15 .
[ vii ] Figure 15 : Organic functional groups in aerosol compounds in two locations .
The upper chart for each location expands the main chart to show the low concentration functional groups more clearly .
Dimethyl sulfide ( DMS ) is produced by phytoplankton in the oceans and is quite volatile .
DMS is readily oxidized to form sulfur dioxide and sulfuric acid , among many products found in the atmosphere .
There are several mechanisms for @ @ @ @ @ @ @ for modeling formation of aerosols and their climate impacts .
A new study has used Time of Flight Chemical Ionization Mass Spectrometry to study sulfur compounds in air .
The study was carried out in NASA aircraft missions that covered the air above the open Atlantic and Pacific oceans from 80º N to 85º S. Hydroperoxylmethyl thioformate ( HPMTF ) was discovered to be a significant portion of the atmospheric sulfur and to play a major role in aerosol formation .
The authors conclude : “ Observationally constrained model results show that more @ @ @ @ @ @ @ to the atmosphere forms HPMTF .
Coincident particle measurements suggest a strong link between HPMTF concentration and new particle formation and growth .
Analyses of these observations show that HPMTF chemistry must be included in atmospheric models to improve representation of key linkages between the biogeochemistry of the ocean , marine aerosol formation and growth , and their combined effects on climate.
” [ viii ] The paper includes a chart showing how the new information fits into the role of sulfur in aerosols , and the uncertainties in HPMTF chemistry in the @ @ @ @ @ @ @ further research ( Figure 16 ) .
Figure 16 : On the left : The basic mechanism of HPMTF formation .
On the right : Concentrations observed using the NASA Atmospheric Tomography ( ATom ) mission , which used the Time of Flight Chemical Ionization Mass Spectrometry instrument Atmospheric chlorine-containing aerosol particles are continually generated by sea spray .
However , such particles are also observed far inland , especially in winter , due to use of rock salt and salt solutions to deice roads and sidewalks .
More than 20 million tons @ @ @ @ @ @ @ in a typical winter in the United States alone .
The chlorine in salt-containing aerosols leads to formation of nitryl chloride ( ClNO ) , which is a significant concern for health .
Just-published research used Aerosol Time-of-Flight Mass Spectrometry to measure the size and chemical composition of individual aerosol particles and used Chemical Ionization Mass Spectrometry to measure chemical compounds in the air in a winter period ( February 17-18 and March 7-8 ) in an inland location adjacent to a roadway and sidewalk on which road salt and brine were applied @ @ @ @ @ @ @ ] Their results are shown in Figure 17 .
The amount of ClNO is very low during the daytime as sunlight breaks the molecule into its reactive components , and increases overnight .
The reactive components affect the levels of many atmospheric compounds and particulate matter , so having better experimental information is helpful in understanding this system .
Figure 17 : On the left : Schematic diagram of the previous bulk method and the new single-particle method for measuring formation of nitryl chloride ( ClNO ) from dinitrogen pentoxide ( N O @ @ @ @ @ @ @ using the bulk model ( gray line ) and using the single particle model ( green line ) and experimental measurements of ClNO using the new method ( black line ) .
Dashed blue lines represent modeled ClNO production from road salt only .
Biosketch Roland Hirsch has served the field of analytical chemistry in a 52-year career that spans teaching , research , and leadership at Seton Hall University , and 33 years of government service at the National Institutes of Health and the U.S. Department of Energy .
Roland has been @ @ @ @ @ @ @ Analytical Chemistry , as Councilor for 25 years , as Division Secretary for 4 years , Chair-Elect , Program Chair , and Chair , and as its Web Editor for 22 years .
Roland organized the 50th-anniversary celebration of the Division and 25 years later , wrote the definitive history of the first 75 years of the Division , published in Analytical Chemistry in 2013 .
Roland has also been active in ACS Governance , including Chair of the Committee on International Activities , Secretary of the Committee on Nominations and Elections , @ @ @ @ @ @ @ , Senior Chemists Task Force , Committee on Committees , and Liaison to the ACS Committee on Professional Training .
Based on a presentation prepared for the American Chemical Society National Meeting in Philadelphia in March 2020 .
It was to have been in the Division of Analytical Chemistry ’ s session “ Advances in Mass Spectrometry ” .
The meeting was canceled , but this presentation was revised and made available on the web site for the meeting : https : //www.morressier.com/article/mass-spectrometry-essential-research-climate-science/5e735e33cde2b641284a879e If you can not access the presentation , please go @ @ @ @ @ @ @ obtain an ACS ID at no charge .
It should allow you to sign in to view the presentation .
[ i ] H. Yan , et al.
, “ Extreme weather events recorded by daily to hourly resolution biogeochemical proxies of marine giant clam shells ” , PNAS ( 2020 ) , www.pnas.org/cgi/doi/10.1073/pnas.1916784117 and https : //www.pnas.org/content/pnas/suppl/2020/03/10/1916784117.DCSupplemental/pnas.1916784117.sapp.pdf [ iv ] S. Giannoukos , et al.
, “ Real-Time Detection of Aerosol Metals Using Online Extractive Electrospray Ionization Mass Spectrometry ” , Anal .
Chem .
( 2020 ) 92 , 1316−1325 https @ @ @ @ @ @ @ , T. Joo , J.H .
Slade , P.B .
Shepson , N.L .
Ng , and D.R .
Gentner , “ Nontargeted Tandem Mass Spectrometry Analysis Reveals Diversity and Variability in Aerosol Functional Groups across Multiple Sites , Seasons , and Times of Day ” , Environ .
Sci .
Technol .
Lett .
2020 , 7 , 2 , 60-69 https : //pubs.acs.org/doi/10.1021/acs.estlett.9b00702 [ viii ] P.R .
Veres , et al.
, “ Global airborne sampling reveals a previously unobserved dimethyl sulfide oxidation Mechanism in the marine atmosphere ” , @ @ @ @ @ @ @ 9 ) 4505-4510 https : //www.pnas.org/content/117/9/4505

by Roland Hirsch
New technologies in mass spectrometry are advancing research in climate science
This is the second of a two-part posting based on a presentation prepared for the American Chemical Society’s National Meeting in March 2020. The meeting was cancelled, but the key points in that presentation (plus a couple of added examples based on new publications of relevant research) are in these two posts.
Reconstructing impacts of short term weather events
The first example in this part on the application of mass spectrometry to understanding recent climate and weather will look at a method used to obtain hourly to daily resolution short term weather events in the near past. There are a variety of ways of reconstructing past climate changes, but most of them do not have sufficient time resolution to identify short term weather events, such as extreme storms or heat or cold periods (“weather-timescale extreme events (WEEs …)”]). Yet the frequency and impact of such events could help developing better knowledge of past climate and its influencers.
A new study analyzed a marine bivalve in the South China Sea elemental ratios by NanoSIMS (Nano Secondary Ion Mass Spectrometry) to obtain daily to hourly resolution proxy records and confirm them using known daily weather for the location (Figure 7). The technique could be applied to fossil shells from the distant past to obtain similar weather information. The authors studied giant clam shells using the NanoSIMS technology to determine the iron/calcium (Fe/Ca) and strontium/calcium (Sr/Ca) ratios in different layers of the shells and were able to confirm the presence of nearby tropical cyclones (Figure 8) and also the impact of cold weather surges on the shells (Figure 9). [i]
Figure 7: D is the layers of the shell by autofluorescence; E and F are the NanoSIMS elemental ratios for indicated shell segments
Figure 8: Nearby tropical cyclones recorded in hourly Fe/Ca of Giant Clam shell obtained by NanoSIMS
Figure 9: Winter cold surges recorded in daily growth rate (green) and hourly Fe/Ca of Giant Clam shell (red)
Studying how aerosols impact the atmosphere
Understanding the makeup of different parts of the atmosphere and how they change is a major aspect of climate science, in addition to the study of air pollution and health impacts. Mass spectrometry plays a key role in this area (Figure 10).[ii]
Figure 10: Schematic of atmospheric chemistry analytical methods
Why are aerosols important for understanding climate?Couds reflect incoming solar radiation back into space and also absorb outgoing radiation, keeping that energy inside the Earth’s atmosphere. Aerosols are a key part of cloud formation, and the chemistry of aerosol particles influences the type of cloud they form. Thus aerosols have a major influence on the greenhouse effect, as well as on other aspects of weather and climate.
Many analytical techniques are used to analyze aerosol particles and to follow their behavior in the atmosphere. This essay is focused on mass spectrometry (MS), and several recently-published examples will showcase how the different types of MS can address specific aspects of aerosol chemistry:
Extractive Electrospray Ionization (EESI) to study metals in aerosols
in aerosols EESI — Orbitrap MS to study organic compounds in aerosols
in aerosols LC-EESI-TOF MS/MS to identify organic compound functional groups in aerosols (TOF means Time Of Flight)
in aerosols (TOF means Time Of Flight) TOF Chemical Ionization MS to follow reactions of sulfur-containing compounds in the atmosphere
in the atmosphere Aerosol time-of-flight mass spectrometry (ATOFMS) and chemical ionization mass spectrometry (CIMS) to study formation of atmospheric chlorine-containing compounds due to rock salt aerosol particles
These are selected from many publications in this area to show the impact of mass spectrometry on the study of aerosols, but are not intended to cover all aspects of the field!
Also, while this essay is focused on mass spectrometric technologies and their applications in climate science, I hope that the limited details about these technologies do not get in the way of understanding their significance for climate research.
* * * * *
Metals are often found in aerosols, but are hard to measure accurately when the aerosols have to be transported to a laboratory. A new approach using Extractive Electrospray Ionization (EESI) with a portable Time-of-Flight MS system has enabled field measurements.[iv]
Figure 12: (a) Experimental mass spectrum of a mixture of zinc acetate, cadmium chloride hydrate, zinc acetate, cerium(III) acetate tetrahydrate, and barium acetate obtained with the EESI-TOF-MS. (b) Time series of an external mixture of lithium hydroxide monohydrate, lead acetate trihydrate, cadmium chloride hydrate, and cerium(III) acetate hydrate
* * * * *
Organic compounds in aerosols (organic aerosols or OAs) can have a substantial influence on how they develop and impact the atmosphere as they travel through it. It is difficult to characterize the many components of the OAs, especially since they are constantly changing as they travel in the atmosphere.
New mass spectrometric technology is making it possible to improve the accuracy and completeness of atmospheric OA measurements in real time. This enables measurement of secondary organic aerosols (SOAs) formed in atmospheric reactions as they happen. Extractive Electrospray Ionization combined with ultra high resolution Orbitrap enables measurements of OAs at relevant concentrations and a sufficient range of mass to cover the great variety of sizes of the OAs. [v] Figures 13 and 14 show examples of studies of a common OA, α-pinene.[vi]
Figure 13 (left): Mass spectra for unoxidized α-pinene (top), α-pinene oxidized at different concentrations (2nd and 3rd), and limonene oxidation products (bottom)
Figure 14 (right): SOAs produced from these two compounds (top and middle) and measurements of ambient air in summer
* * * * *
Functional groups are active sites in organic molecules, for example the aromatic ring that makes up benzene and is in the many compounds that include its ring, or the -OH functional group that is present in alcohols and sugars. Organic compounds with various functional groups are a significant component of aerosols, sourced, for instance, in atmospheric particulate matter. A new approach uses LC-ESI-TOF MS/MS to quantify organic aerosol functional groups in environmental samples. The scientists used this technique to study organic functional groups in air in Atlanta, Georgia, and the Long Island Sound, New York. An example of their results is shown in Figure 15.[vii]
Figure 15: Organic functional groups in aerosol compounds in two locations. The upper chart for each location expands the main chart to show the low concentration functional groups more clearly.
* * * * *
Dimethyl sulfide (DMS) is produced by phytoplankton in the oceans and is quite volatile. DMS is readily oxidized to form sulfur dioxide and sulfuric acid, among many products found in the atmosphere. There are several mechanisms for the oxidation and understanding them is important for modeling formation of aerosols and their climate impacts. A new study has used Time of Flight Chemical Ionization Mass Spectrometry to study sulfur compounds in air. The study was carried out in NASA aircraft missions that covered the air above the open Atlantic and Pacific oceans from 80º N to 85º S.
Hydroperoxylmethyl thioformate (HPMTF) was discovered to be a significant portion of the atmospheric sulfur and to play a major role in aerosol formation. The authors conclude: “Observationally constrained model results show that more than 30% of oceanic DMS emitted to the atmosphere forms HPMTF. Coincident particle measurements suggest a strong link between HPMTF concentration and new particle formation and growth. Analyses of these observations show that HPMTF chemistry must be included in atmospheric models to improve representation of key linkages between the biogeochemistry of the ocean, marine aerosol formation and growth, and their combined effects on climate.” [viii]
The paper includes a chart showing how the new information fits into the role of sulfur in aerosols, and the uncertainties in HPMTF chemistry in the air that need to be addressed in further research (Figure 16).
Figure 16: On the left: The basic mechanism of HPMTF formation. On the right: Concentrations observed using the NASA Atmospheric Tomography (ATom) mission, which used the Time of Flight Chemical Ionization Mass Spectrometry instrument
* * * * *
Atmospheric chlorine-containing aerosol particles are continually generated by sea spray. However, such particles are also observed far inland, especially in winter, due to use of rock salt and salt solutions to deice roads and sidewalks. More than 20 million tons of salt are used for this purpose in a typical winter in the United States alone. The chlorine in salt-containing aerosols leads to formation of nitryl chloride (ClNO 2 ), which is a significant concern for health.
Just-published research used Aerosol Time-of-Flight Mass Spectrometry to measure the size and chemical composition of individual aerosol particles and used Chemical Ionization Mass Spectrometry to measure chemical compounds in the air in a winter period (February 17-18 and March 7-8) in an inland location adjacent to a roadway and sidewalk on which road salt and brine were applied during the study period.[ix] Their results are shown in Figure 17. The amount of ClNO 2 is very low during the daytime as sunlight breaks the molecule into its reactive components, and increases overnight. The reactive components affect the levels of many atmospheric compounds and particulate matter, so having better experimental information is helpful in understanding this system.
Figure 17: On the left: Schematic diagram of the previous bulk method and the new single-particle method for measuring formation of nitryl chloride (ClNO 2 ) from dinitrogen pentoxide (N 2 O 5 ). On the right: Estimates using the bulk model (gray line) and using the single particle model (green line) and experimental measurements of ClNO 2 using the new method (black line). Dashed blue lines represent modeled ClNO 2 production from road salt only.
* * * * *
Biosketch Roland Hirsch has served the field of analytical chemistry in a 52-year career that spans teaching, research, and leadership at Seton Hall University, and 33 years of government service at the National Institutes of Health and the U.S. Department of Energy. Roland has been a leader of the ACS Division of Analytical Chemistry, as Councilor for 25 years, as Division Secretary for 4 years, Chair-Elect, Program Chair, and Chair, and as its Web Editor for 22 years. Roland organized the 50th-anniversary celebration of the Division and 25 years later, wrote the definitive history of the first 75 years of the Division, published in Analytical Chemistry in 2013. Roland has also been active in ACS Governance, including Chair of the Committee on International Activities, Secretary of the Committee on Nominations and Elections, Member of the Committee on Divisional Activities, Senior Chemists Task Force, Committee on Committees, and Liaison to the ACS Committee on Professional Training.
Based on a presentation prepared for the American Chemical Society National Meeting in Philadelphia in March 2020. It was to have been in the Division of Analytical Chemistry’s session “Advances in Mass Spectrometry”. The meeting was canceled, but this presentation was revised and made available on the web site for the meeting: https://www.morressier.com/article/mass-spectrometry-essential-research-climate-science/5e735e33cde2b641284a879e
If you cannot access the presentation, please go to https://account.acs.org/ssoamweb/account/doSignUp where you can obtain an ACS ID at no charge. It should allow you to sign in to view the presentation.
Endnotes:
[i] H. Yan, et al., “Extreme weather events recorded by daily to hourly resolution biogeochemical proxies of marine giant clam shells”, PNAS (2020), www.pnas.org/cgi/doi/10.1073/pnas.1916784117 and https://www.pnas.org/content/pnas/suppl/2020/03/10/1916784117.DCSupplemental/pnas.1916784117.sapp.pdf
[ii] P. Forbes, “Atmospheric Chemistry Analysis: A Review”, Anal. Chem. (2020), 92, 455-472. https://pubs.acs.org/doi/10.1021/acs.analchem.9b04623
[iii] http://www.dnrec.delaware.gov/ClimateChange/Pages/Greenhouse%20Effect.aspx
[iv] S. Giannoukos, et al., “Real-Time Detection of Aerosol Metals Using Online Extractive Electrospray Ionization Mass Spectrometry”, Anal. Chem. (2020) 92, 1316−1325 https://pubs.acs.org/doi/10.1021/acs.analchem.9b04480
[v] C.P. Lee, et al., “Online aerosol chemical characterization by extractive electrospray ionization – ultrahigh-resolution mass spectrometry (EESI-Orbitrap)” Envir. Sci. Technol., (2020), 54, 3871-3880 https://pubs.acs.org/doi/pdf/10.1021/acs.est.9b07090
[vi] https://pubchem.ncbi.nlm.nih.gov/compound/1S_-_-_-alpha-Pinene
[vii] J.C. Ditto, T. Joo, J.H. Slade, P.B. Shepson, N.L. Ng, and D.R. Gentner, “Nontargeted Tandem Mass Spectrometry Analysis Reveals Diversity and Variability in Aerosol Functional Groups across Multiple Sites, Seasons, and Times of Day”, Environ. Sci. Technol. Lett. 2020, 7, 2, 60-69 https://pubs.acs.org/doi/10.1021/acs.estlett.9b00702
[viii] P.R. Veres, et al., “Global airborne sampling reveals a previously unobserved dimethyl sulfide oxidation Mechanism in the marine atmosphere”, PNAS (2020), 117 (9) 4505-4510 https://www.pnas.org/content/117/9/4505
[ix] S.M. McNamara, et al., “Observation of Road Salt Aerosol Driving Inland Wintertime Atmospheric Chlorine Chemistry”. ACS Central Science (2020), 6, 684-694. https://pubs.acs.org/doi/pdf/10.1021/acscentsci.9b00994
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