The great topmost sheet of the mass, that where hardly a light had twinkled or moved, becomes now a sparkling field of rhythmic flashing points with trains of traveling sparks hurrying hither and thither. The brain is waking and with it the mind is returning. It is as if the Milky Way entered upon some cosmic dance. Swiftly the head mass becomes an enchanted loom where millions of flashing shuttles weave a dissolving pattern, always a meaningful pattern though never an abiding one; a shifting harmony of subpatterns.

Few scientists write like that anymore. Science is the poorer for it. Sherrington was able to dash that off when the most complicated device to provide an analogy was a 1801 Jacquard loom capable of weaving complicated patterns on the basis of punched cards. So, Sherrington thought of a loom, Freud of a hydraulic system of pipes, Pavlov of a telephone exchange, and more recently everyone and their uncle think of the brain as a computer.

As we progress, the capacity to image the brain, and image it in motion, improves rapidly. Now we can look at fMRI outputs, and deduce the fine detail of connectivity in the brain. A problem arises which the authors of a recent paper identify as a source of bias: even at rest, these outputs differ according to race.

Here is an interesting study which looks at the patterns detectable in brain activity, and which identifies significant racial differences.

Evidence For Bias Of Genetic Ancestry In Resting State Functional MRI. April 2019
DOI: 10.1109/ISBI.2019.8759284
Conference: IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 8-11 April 2019, Venice, Italy Volume: 16. Andre Altmann and Janaina Mourão-Miranda

https://drive.google.com/file/d/1Zj4mEsFdq-Hsze2Oy6mda8AdaDRVQ1Tl/view?usp=sharing

Their abstract:

Resting state functional magnetic resonance imaging (rs-fMRI) is a popular imaging modality for mapping the functional connectivity of the brain. Rs-fMRI is, just like other neuro-imaging modalities, subject to a series of technical and subject level biases that change the inferred connectivity pat-tern. In this work we predicted genetic ancestry from rs-fMRI connectivity data at very high performance (area under the ROC curve of 0.93). Thereby, we demonstrated that genetic ancestry is encoded in the functional connectivity pattern of the brain at rest. Consequently, genetic ancestry constitutes a bias that should be accounted for in the analysis of rs-fMRI data.

Resting state is what the subject does when lying in a scanner without any task to attend to. Of course, resting state can be very busy: thinking of nothing is impossible, so subjects may be worrying about claustrophobia, enumerating their daily tasks and duties, mulling over a recent problem, or idly considering the sensual advantages of polymorphous perversity. I digress, and no digression is ever fully restful.

Although age and sex and health status can affect functional MRI readings “there is one additional subject level characteristic that is known to affect head and brain morphology but is rarely considered as a confound in rs-fMRI studies or brain imaging studies in general: genetic ancestry.”

They took 1003 subjects from the Human Connectome Project with the relevant fMRI data, and for 950 of those for they also had genomic data. They ended up with 764 European, 138 African, and 39 Asian subjects. To increase discriminative power they compared the Europeans against the rest (using various cut-off assumptions).

They use signal detection theory, which entered psychology in the 1960s, and made “Receiver Operating Characteristics” and “Areas Under the ROC curve” familiar to researchers.

It is a tiny matter, but the same analysis of functional MRI which classifies race at 93% accuracy classifies sex at 98% accuracy. Both these categories can be detected on brain waves in the form of minute blood flows.

So, the functional connectivity of the brain differs among racial groups defined by their genomes, and this difference can be picked up with a high level of accuracy (93%).

They caution:

The exact origin of these apparent connectivity differences between continental ancestries remains elusive at the moment. However, we hypothesize that the observed differences are not based on true neuronal differences but that they originate from differences in head and brain morphology as reported in [8, 9]. These morphological differences may be carried forward through the standard rs-fMRI processing pipeline and affect the inferred functional connectivity. In addition, rs-fMRI connectivity is based on correlations between blood-oxygen-level dependent (BOLD) signal time series at rest. Thus, it is conceivable that genetic differences contributing to blood circulation, perfusion and elasticity of the vascular system may modify BOLD dynamics. This is exemplified by reports identifying ethnicity as independent risk factors for cardio blood oxygenation level dependent vascular disease[14] and intracranial artery tortuosity[15]. In addition, brain hemodynamic responses are known to be heritable traits[16].

So, the authors think that it is racial difference in skull and brain shape which may account for these differences, which make it easy to detect the subject’s race from the connectivity of the brain, rather than any neuronal differences.

Perhaps they are keeping this for later work, but subjects in the Human Connectome Project have completed intelligence tests, and will also have a measure of MRI derived brain size.

Previous 2015 work on the Human Connectome Project shows that brain networks are associated with intelligence.

https://www.unz.com/jthompson/connectivity-matrix-predicts-fluid/

Previous 2017 work on the Human Connectome Project shows that brain networks are associated with intelligence.

https://www.unz.com/jthompson/iq-brain-map/

Previous 2018 work on Human Connectome Project likewise.

https://www.unz.com/jthompson/the-well-tempered-clavichord/

So, it would be possible to see if the differences in connectivity related in any way to intelligence in the three groups studied: Europeans, Africans and Asians. If there were no differences this would tend to disprove assumptions about brain size and brain organization as a source of racial difference in intelligence.

This paper has a very large sample, employs standard measures and has appropriate statistics, cautiously interpreted. All these aspects are reassuring. It is a niggle, but the authors use the notion of “bias” in a particular way, because they have identified human differences which are nothing to do with bias. These differences were detected by very precise measures of brain activity, thus revealing something, not obscuring it. By analogy, their mapping of the sea floor has shown previously unrecognized under-water contours and ships can now navigate more safely.

Like any other study, this needs to be replicated, but this is a significant finding which stands until refuted by a study of similar sample size and uniformly applied measures.

If you are a racist, then you are asking for a fight. But science is my ally, not yours, and your fight is not just with me, but with reality.

This book, shortly to be published in the US, is written by a geneticist. Racism is a topic of contemporary interest, and there are certainly different conceptions of reality. The book is diminished by its title, which proclaims it an aggressive polemic, looking for a fight. “How to argue about race” would advertise a better book, on which new readers might choose to rely, finding in it an evaluation of opposing arguments. On the contrary, this author says his book is a weapon. Not a sextant, microscope, thermometer, weighing machine, assay kit or surveyor’s theodolite. He wants arguments to be weaponized. His aim is combat, not an even-handed evaluation of a fascinating science.

If you jump to the References then the one-sided nature of the material is clear to see. Papers critical of race differences in intelligence are presented without the rejoinders.

https://www.unz.com/jthompson/james-watson-nobel-laureate-and-unperson/?highlight=Wicherts

Despite a publication date of December 2019 much recent work on the genetics of intelligence, including a major study in July 2018 which in passing looks at race differences is simply omitted.

https://www.unz.com/jthompson/journey-of-1-1-million-miles/

Not only that, but the references are very sparse. Some 40 references cannot do justice to the scope of the argument, and the engaging style cannot compensate for sweeping generalizations and omissions. Potential readers might wish to proceed no further.

However, I believe that there is reason to read on, if only because the title will attract readers and get supportive reviews, but also because the book admits as much as it denies, conceding that many apparent disputes are about nomenclature (“population” “ancestry” “lineage” are described as “more technical”, but although said to be incoherent in scientific taxonomy, the author admits “race” will do). It admits that “racism” will be applied only to Western and European cultures, because they “started it” in the Enlightenment; and also concedes that racial differences are rooted in biology.

Our quintessential nature as wanderers, hunters, farmers, and social creatures meant that, over the last few thousand years, Earth has become smaller, and peoples from around the world have met, traded, mated, fought, conquered and a whole lot more. In these interactions, we engage with people who are different from each other. These differences are rooted in biology, in DNA, and also in our behaviour as social animals – in our dress, our speech, our religions and our interests. In the pursuit of power and wealth, the fetishisation of these differences has been the source of the cruellest acts in our short history.

So, racial differences are rooted in biology, in DNA. The problem does not lie in these differences, but in the fetishization of these differences. It is a matter of degree. All this by page 4. Readers inclined to genetic determinism may be tempted to stop reading: Rutherford has given them enough to feel that they are in agreement with him, and the “fetishisms” are details of minor significance.

Contrary to his claim, cruel acts in our short history have arisen for many reasons, not only race. Contrary ideas have often split families and races sufficiently to make them fight each other in civil wars, despite genetic similarities. Germany and Russia jointly did cruel things to Poland in 1939 because they shared a common political purpose. A detail perhaps. Russia did cruel things to its own citizens, China likewise, and Cambodia as well, because of ideas about social class. All sorts of ideas can lead to unfairness and cruelty.

Rutherford says he will provide:

a scientific description of real human similarities and differences that will provide a foundation to contest racism that appears to be grounded in science. Here, I am focusing on four key areas where we often slip up by adhering to stereotypes and assumptions; I am outlining what we can and cannot know according to contemporary science on the subjects of skin colour, ancestral purity, sports, and intelligence.

I agree with Rutherford that skin colour is part of racial differences, but hardly all or even the most important part of it. It is simply one facet of differences which together make up evident racial groupings: skull shape; body shape and size; muscle and fat distribution; bone density; early motor development; vulnerability to illnesses and others.

Equally, ancestral purity is hardly much of an issue. Some groups have lived together for sufficient generations to have developed characteristics in common. The fact that they are purely that group does not mean that they are better. People who have always lived for generations in Norfolk are purely Norfolk. Whether that is a good or bad thing can be determined by other means.

Rutherford mounts an argument about the genetic isopoint (pg 76):

Everyone alive today is descended from all of the global population in the fourteenth century bce. Irrespective of how plausible that sounds, or how contrary it seems to our own experiences of family and family trees, it is true – the isopoint is a mathematical and genetic certainty. It is likely that the proportion of a person’s ancestors at the isopoint are not equally distributed around the world: a Chinese woman or man will have far fewer southern African ancestors than East Asian, and vice versa. But they will have some, and each of those ancestors has an equal relationship with their living descendants regardless of where on Earth they lived and died.

At times this seems to be intended to lead to the conclusion that we are all one “race” but the explanation concedes that the proportion will vary. However, the conclusion he comes to is:

No nation is static, no people are pure.
Racial purity is a pure fantasy. For humans, there are no purebloods, only mongrels enriched by the blood of multitudes.

However, he accepts there will be “proportions”, and those different proportions develop characteristics which are rooted in biology.

How does the author define racism?

Racism has many definitions; a simple version is that racism is a prejudice concerning ancestral descent that can result in discriminatory action. It is the coupling of a prejudice against biological traits that are inalterable with unfair behaviour predicated on those judgements, and can operate at a personal, institutional or structural level. Pg 20.

I prefer Hazlitt (1830) because it applies to everything and everyone:

Prejudice is prejudging any question without having sufficiently examined it, and adhering to our opinion upon it through ignorance, malice or perversity, in spite of every evidence to the contrary.

Although Rutherford does not spell it out, he implies he would accept valid judgments about ancestral descent, and actions based on good evidence. In the text the accusation of racism is frequent and broadly applied. Rutherford agrees that race exists, but goes on to ask:

Are there essential biological (that is, genetic) differences between populations that account for socially important similarities or divisions within or between those populations? Pg 21

Certainly, to deny the importance of genetics in influencing our behaviours is folly. Pg 23.

America is in turmoil because of a video in which a Policeman seems very highly likely to have caused the death of an arrested man. He must face trial. Also, the techniques used in making arrests must be put on trial. Kneeling down on the back of the neck of a hand-cuffed man is not remotely a safe procedure.

A few years ago, Europe was in shock at the picture of a refugee child lying dead on a Greek beach, leading to a chain of events which culminated in the German Chancellor, Angela Merkel, inviting into Germany one million refugees. The picture was more powerful than a thousand words. A thousand times more powerful.

A dreadful image, a horrifying video, can have enormous impact. It brings us face to face with death, from which we are now spared in everyday life, since death has been outsourced to professionals, whereas before it was part of family experience to attend to the dying, and to sit by a visible body in a protracted wake. Equally, the videos of riots, beatings, and shootings cannot help but arouse emotions, of fear, pity, anger and hatred. It is easy to be horrified, repelled by human behaviour, outraged at violence on the part of others, and to wish for prompt and savage retaliation.

Bertrand Russell observed that individual cases had an enormous impact in popular argument. When an individual case is made flesh in the virtual reality of a video, it takes on an incontrovertible status: we saw it happen, as if we were there, and no one can contradict us, other than a liar. We regard ourselves as a witness to a broader reality, and are reluctant to accept that we have witnessed a rare and unrepresentative event. Traumatised victims of crime are apt to over-generalise, both about the perpetrators, the circumstances and place of the event, and about the prevalence of crime. And why not? Their lives were mostly good until that point, and then one or two miscreants put them in fear of their lives, on a street somewhere, in a way they will forever remember. Avoiding those types of people (including their race) and those types of circumstances (talking on a phone in a street) and that particular location seems prudent. The victim has learned from experience, and much therapeutic time may be needed to help them out into public again.

In the face of these emotional realities, from which few are exempt, the search for context may seem a betrayal: the cop-out of the feeble, the excuse of the bystander. Quoting the available statistics may seem perverse, as if it were an attempt to deny that a citizen was killed, as depicted.

Would it be better to show videos of other citizens being killed, in the same or similar manner, to reveal that it happens to men of all races? Bizarrely, this might be a good corrective to over-generalization. Say, the last 20 to 30 cases of death of arrested men, each shown in painful personal detail, might show in virtual reality what comes out of the dry government statistics, that in recent years 3 black men per 10,000 arrests have ended up dead, as compared with 4 white men per 10,000 arrests. @LeonydusJohnson

Will these figures have any impact? I doubt it. We have not seen their faces, heard their pleas, witnessed their agony. It is the difference between a great tragedy onstage and the dry recitation of a telephone directory in a side alley. Statistics are of interest to 0.1% of the population.

Of course, the context should be even broader, which is to look at all racial aspects of crime. These data are not available on an annual basis, and tend to be given as bald numbers, requiring readers to do extra work in order to interpret them. A good starting point is to go through the logic before looking at any numbers.

For each group in society, in this case racial groups, we need to know the crime rate per head of population for all arrestable crimes. This gives us a first approximation, subject to reporting errors, which of course could include differential arrest rates because of racial bias. Where possible, we could compare those arrest rates with victim and witness descriptions of perpetrators. Victims very often see their assailants very close up, and can give racial characterisations, if not descriptions which are detailed enough to secure convictions. Witnesses likewise can generally give racial groupings of perpetrators. Interestingly, self-reports are also available from crime surveys, and these serve as another comparison against which to evaluate arrest rates.

To evaluate the argument that all these figures are shot through with specific racial biases, it would be good to look at speed camera infractions by race.

Crime surveys in which citizens recall crimes (which they may not have taken to the Police) and also give racial descriptions of the perpetrators would be another test case. Interestingly, the argument that the Police and justice systems are biased would have to be extended to all victims, arguing that out of personal racial prejudice the victims mis-identify the actual perpetrator so that the police search for the wrong racial group. This seems unlikely.

Government statistics on race and crime have been published, debated and publicised countless times, yet seem to have no effect at all on the nature of the national discussion. They seem to exist only as a footnote, which should not disturb the main narrative. To mention them is to interrupt an action movie with obscure comments on the possibly misleading effects of selective camera angles.

One reason for confusion is that mainstream media (the BBC is a prime example) give a selective presentation of the findings. This week they were showing graphs (as part of “reality-checking”) of fatal police shootings by ethnicity. What could be wrong with that? What could be wrong with prison population by ethnicity?

https://www.bbc.co.uk/news/world-us-canada-52877678

The error lies in assuming that racial groups are equally law-abiding. That possibility is not included in the BBC reality checking.

What is to be done?

The statistics don’t matter, and statistics are the distillation of innumerable biographies.

Uruguay is a small country on the eastern coast of South America between Argentina and Brazil. Mostly European in demographics, it was long considered the Switzerland of South America because, fearful of the usual local tendency towards dictatorship, it shared power in a plural executive, was early in separating Church and State, in giving votes to women, allowing divorce, and generally being sensible. It has low corruption, high press freedom, a democratic tradition which survived a 70s lapse into military dictatorship, and no terrorism, a good standard of living, and an excellent standard of beaches. In fact, think of a 400-mile beach with an excellent farm attached. In truth, it also has a fine capital city of Montevideo where half the population lives, which gives it a high population density; and Punta del Este, the continent’s premier beach resort, which helps draw in 4 million tourists a year. Agriculture and tourism are major sources of income.

The interest lies in how this country, with only 3.45 million citizens in an area larger than England, has coped with coronavirus, given that it knows with reasonable certainty that the entry point was Montevideo airport on or about 6 March, and is a society in which most people know each other, or at least can trace connections pretty easily. Three arrivals at the airport spread the disease. Two took a 6-hour bus ride north on 8 March, and one already infected lady on 7 March attended a society wedding with 500 guests, and kissed, conversed and danced with enough of them to infect a reported 44 people.

Half of Uruguay’s cases can be traced to that party. One of those infected, a young woman, went to work and infected a male colleague in her office, and that young man passed it on to his mother, a friend of mine, who later recounted her illness in detail. It was over 3 weeks before she felt fully recovered.

The socialite who flew in denied having any knowledge of her infected status, though she had been in Milan in January and fallen ill afterwards, but she was not screened at the airport. She was later reported for having received family visitors while in quarantine. Folklore songs have been composed about her, none of them flattering. Contact tracing has been done on the wedding event but detailed reports are not yet available.

What has happened since?

Coincidentally, on first of March the ruling Left-wing coalition gave way after 15 years to a Centre Right coalition of traditional parties, the more rurally-inclined one celebrating by getting many of its farmers to attend the inauguration on horseback in full gaucho regalia, a spontaneous innovation which was a joy to behold. With only a week’s notice, the newly installed government took advice, and soon went into lockdown. (This had been started progressively the moment cases were announced, with universities, schools, public theatres and cinemas introducing various distancing measures immediately, though the airport was not finally closed till 20 March). Kids at public schools already had a personal computer, so distant learning was already established. Health officials traced contacts, and used widespread testing. Uruguay had had the wit to ensure that the Pasteur Institute, looking for a South American location for a new centre, chose Uruguay (because an ex-president remembered that Uruguay had excused France a post-war bill for meat, and deserved something in return), so test systems were on hand.

http://pasteur.uy/en/home/

The government designed an app so that information could be disseminated by WhatsApp and a system for border control, so that if any flights came in they had advance notice of names and health status.

http://socialdigital.iadb.org/en/covid-19/regional-response/4679

Currently, Uruguay has had 737 cases and 20 deaths. Here are some comparative rates (rates per million, not percentages).

Brazil death rate 79
Argentina death rate of 8.5
Uruguay death rate of 5.8

Uruguay has been 13 times safer than Brazil, where the President is against lockdowns though many state leaders are for it; and a bit safer than Argentina, which also went into lockdown. Brazilian contacts tell me that compliance has been variable, and much lower among the hard-pressed poor, despite welfare payments. Argentine contacts say that compliance was mostly good, and it remains to be seen if it holds up.

By the way, Uruguay is not a culture in which deaths could be covered up. At most, there seem to be 3 degrees of separation. News travels fast, and family connections are strong, so a body on the street would soon have a crowd at a distance sending social media announcements.

How come Uruguay has few cases and very few deaths? As discussed, Uruguay does not have many airline connections with the countries of origin of the infection, and by March the big holiday season influx of tourists was over. The health service is good, and there are strong professional medical links with the US and also with Europe. The new government maintained progress on transition tasks but concentrated heavily on the threat. Social distancing was observed, and the minority of infractions related to walks on the beach or in parks, which is less dangerous. Given that the transfer of power between sharply contrasting political coalitions (ex-terrorist Tupamaros leaving power, landed gentry and business owners taking power) had been peaceful and even convivial, there was a sense of national unity.

Uruguay even took time out to help some cruise ships with Covid patients on board, who had been refused docking in other countries, treating them and then letting the whole lot get into buses to take flights out. To the great surprise of the cruise passengers, who had been refused a berth at other countries, Uruguayans came out at night to clap and cheer them on their way home.

In the last few weeks, before agreeing to lift lockdown, it started up the construction business under strict distancing protocols, and monitored the workers in a large sample of workplaces. Not a single one tested positive. Equally, they let rural primary schools open, finding that they could do so without further outbreaks. They have worked carefully, testing each step of the way.

They have also been conducting research, some of which is already published. A local research group has done some backward tracking on the first cases encountered world-wide, showing it was active earlier than publicly stated.

It is too early to make a final judgment about which countries have done best. The debate will be about which of them got the biggest dose to begin with: airport connectivity and entrepot status. Then some debate about population density, and the age structure. Is Africa doing well because of previous experience with Aids and Ebola, or is it just that it is a very young continent? Did Uruguay benefit from being far away in airline terms from China, and having completed most of its summer tourism by mid-February?

Last night the UK Prime Minister said that those who could not work from home, like those in construction and manufacturing should go to work today, maintaining social distancing, and avoiding public transport if possible. Primary schools may begin reopening in June, as may some shops, and some of the hospitality industry may reopen in July. The advice was conditional: if the reproductive rate goes above 1 (it is estimated to be between 0.5 and 0.9) then these relaxations may have to be reversed.

Bizarrely, in my view, he added: “it will soon be the time… to impose quarantine on people coming into this country by air”. If that had been implemented in January the UK would not have had an epidemic. For some reason the argument he gave was that, because good progress had been made in reducing R by shutting down society, it was now appropriate to deal with the supply of the virus and in due course, without any particular urgency, turn off the tap that set the whole country afire.

So, it is not an end to lockdown, nor the beginning of the end, but possibly the end of the beginning.

Lots of ideas are rattling around the country, and it is salutary to note some of them, since we are getting so much history now. Decades when nothing happens, and then days when decades happen.

There is considerable scepticism about the Imperial College model, which is seen as poorly documented, poorly configured in computing terms (bad architecture). There is plain scepticism about the accuracy of any of Prof Ferguson’s predictions, because he has a history of wildly exaggerated death rates. Or that is the claim against him. More likely, each of his simulations involved several predictions: best case, most likely case, worst case and both he and his critics can pick those which best suit their story. What is Prof Ferguson’s Brier score (mean squared difference between predictions and actual outcomes)? How many predictions does he make per simulation? Would he get to super-forecaster levels? At the moment it does not seem likely. Interesting, however, that his predictions are mutative: even if wrong, they have had a big influence on policy, and so alter the outcomes which serve as the test of predictive accuracy.

He did not do well with the foot and mouth outbreak. Long before I took up blogging I wrote to the relevant Ministry to ask for their justification for their slaughter of cow herds, and was fobbed off for months until on the third attempt they sent me what they claimed was their justification: not the work of Ferguson but a calculation by another professor. I contacted him and he replied that his note to the ministry was a back of an envelope calculation, and no basis for any policy at all. Foot and mouth disease does not lead to appreciable drops in animal fitness, and the meat is still fit to eat, so the whole episode remains something of a mystery. I digress.

What should one require of all model simulations on which public policy may be based? At a minimum, it seems essential to have an introductory page giving all the assumptions in plain language, and in a standard agreed format. Then, a second page explaining the basic structure of the computer program. Then, publication of the actual code for inspection and expert testing.

It is extraordinary that one of the most important events in current economic and social history was based on the unexamined workings of a computer model. It appears that politicians believed the numbers because they were printed out by a computer. An accountant friend was once asked to audit a very smart perfume shop in Mayfair, London. It was a palace of marble and chrome, every saleswoman a beauty, and the accounts beautifully printed out by computer, which was novel at that time. He asked himself: If I owned this shop and was trying to steal money, how would I do it? Suspicious, but at a loss where to begin, he spent some days adding up all the entries with his own calculator. The computer totals had been falsified, but very neatly, and they were skimming money out of the business.

Was lockdown a waste of time, because even if it prevented prompt deaths, herd immunity is lowered, leaving us open to a much more dangerous second wave this winter? This is an unpleasant Is it 66% or a far lower figure? If most super-spreaders are already immune, very likely because they were most likely to catch the virus in the first place, then the herd could be safe at a lower threshold. In the UK at least, the picture is still unclear.

On a far brighter note, there is now more clarity about how the infection spreads. As a result of a series of tweets (a chorus of tweets?) Dr Muge Cevik of St Andrews University deserves two medals: one for not having a computer model; the other for looking in detail at 14 studies of close contacts of Covid cases, showing how many infected people go on to infect how many others, and how those rates differ between sustained indoor settings and more casual outdoor ones. Apart from confirming the age gradient in vulnerability, she shows very clearly that sustained exposure in an enclosed space is the greatest vector of infection (houses, offices, public transport). Casual interactions outdoors are far less risky. Looks like droplets, not aerosols, are the main vector. The advice would be: stand apart, wear masks, wash hands, and reconfigure public indoor settings to reduce all respiratory disorders in future.

While the infectious inoculum required for infection is unknown, these studies indicate that close & prolonged contact is required for #COVID19 transmission. The risk is highest in enclosed environments; household, long-term care facilities and public transport.

High infection rates seen in household, friend & family gatherings, transport suggest that closed contacts in congregation is likely the key driver of productive transmission. Casual, short interactions are not the main driver of the epidemic though keep social distancing!

Increased rates of infection seen in enclosed & connected environments is in keeping with high infection rates seen in megacities, deprived areas, shelters. A recent preprint demonstrates that #COVID19 epidemic intensity is strongly shaped by crowding

Although limited, these studies so far indicate that susceptibility to infection increases with age (highest >60y) and growing evidence suggests children are less susceptible, are infrequently responsible for household transmission, are not the main drivers of this epidemic.

Finally, these studies indicate that most transmission is caused by close contact with a symptomatic case, highest risk within first 5d of symptoms. To note: this preprint suggests that most infections are not asymptomatic during infection

In conclusion, contact tracing data is crucial to understand real transmission dynamics. Cautionary note: This data & interpretation is based on the available evidence as of May 4th. Our understanding might change based on community testing/lifting lockdown measures.

Addendum: While we have limited data, similar high risk transmission pattern could be seen in other crowded & connected indoor environments such as crowded office spaces, other workplace environment, packed restaurants/cafes, cramped apartment buildings etc.

Conclusion 2: (a) we need to redesign our living/working spaces & rethink how to provide better, ventilated living/working environment for those who live in deprived & cramped areas; (b) avoid close, sustained contact indoors & in public transport, & maintain personal hygiene.