❗️Did you know that *study after study* finds most people don't seem to transmit COVID at all? That a small percent is responsible for almost all infections? That R is not that informative? My new piece on why this may be key to controlling the pandemic.
Good text on #COVID19 overdispersion and cluster tracing in . The evidence is accumulating. Backward cluster tracing is the way to go. The hardest part is for diagnosed persons to name potential source clusters. Start your cluster diary today!
Look, the science doesn't fit into the lockdown/anti-lockdown fights. It's not how much we restrict, but whether we target the right things. Overdispersion means the right measures take us a long way, but also that we can't relax: It can quickly flip back.
It's k, the dispersion factor, not R. 's ★ synthesis of the opportunity to get ahead of the virus by taking advantage of the 80/20 rule for how it spreads, the avoidance of superspreading (not overlooked by me; a compelling case here)
I'm nominating this article for the Rosen Prize in explanatory writing. Rules for awarding the prize combine three factors: clarity in explanation, yes, but also underlying complexity of the thing being explained, AND urgency of the subject to the public.
I got the chance to talk with one of my science heroes, Zeynep Tufekci (), about how complex systems like #COVID19 break our brains & challenge public health approaches in the US/Europe. Everyone should read her far-reaching & insightful new piece.
Instead of offering a pithy summary of this excellent ⁦⁦⁩ piece, I’m just gonna say you really should read it.
In today's superspreader/overdispersion piece by (the best, comprehensive one on this topic to date), she also reviews how important these cheap, frequent, rapid turnaround tests can prevent such spread
If you read one thing to understand the pandemic and how to deal with it, read this: It introduces k It explains how the infection spreads in clusters It looks at the implications for testing and contact tracing And it does all this with exemplary clarity
This piece on Covid and overdispersion by is fabulously informative
Folks asking me if this skew—that a few account for most infections while most don't transmit—is due to the people & what it means. That's all in the article.😃 It's not just a mathematical curiosity; it has big practical consequences. Also in the article.
I was initially sceptical of the extent of overdispersion in #SARSCoV2 transmission, due to evidence mainly coming from countries with low rates of transmission and studies with small numbers This explains it all and has changed my mind for good 1/3
When said this was "probably the best article on Covid I've read," I had to read it immediately. It's really good, as everything from seems to be.
To stop transmission, maybe we need to stop narrowly thinking about the what & how (aerosols/droplets/fomites), since we already have agreed on NPIs. We should think about the when & why instead: overdispersion. Really fantastic piece by .
K - or why nothing makes sense until you read this: HT
👇READ THIS!! Rarely has a piece given me such "A-HA!" moments, brilliantly puts the puzzle pieces together on COVID-19 transmission & the way forward:
⁦⁦fuck this article by ⁦⁩ is absolutely gold to understand the “mechanics” of COVID and, above all, to have a better answer than “Japanese people don’t hug” when asked about why Japan has been fairly good at containing the virus 💡
This article by is extraordinary, and well worth your time. First time since March I've felt a small measure of hope about life pre-vaccine.
Absolute must read
There’s an old joke about St Louis weather, it’s not the mean, it’s the variance that will get you. The same is true of COVID-19 transmission. Most cases infect no one, a few infect many. Important implication for public health control
More great #pandemic journalism from at  helps explain why #R0 & #flu-conditioned #epidemiology isn't adequate for understanding #COVID19. SFI's weighs in on what other measures matter more, and why: #superspreader #pareto
Have we been thinking about the pandemic all wrong? The virus’s ability to seed explosive spreading events may outweigh the influence of its average contagiousness, R0. via
On the way coronavirus spreads in an imbalanced way, this piece by is so clear and illuminating Most infected ppl DON'T transmit COVID. A small % of superspreaders is responsible for almost all infections, ie the Daegu woman who gave it to 5,000
An article in The Atlantic by changed how I think about COVID-19. The disease is likely spread by fewer, super-spreading events, and Japan might have had the optimal response to the disease.
Thoughts on this new article suggesting focusing on cluster busting and backwards contact tracing. The author was very early on pushing masks and ventilation.
Why did Japan and Korea do so well? And what really happened in Sweden? This is the best explanation I have seen to date
Great article by with comments from The #COVID19 pandemic is overdispersed, meaning a relatively small number of people spread it in clusters. R0 doesn't capture this behavior.
“A growing number of studies estimate that a majority of infected people may not infect a single other person... [A]bout 19 percent of cases were responsible for 80 percent of transmission, while 69 percent of cases did not infect another person.”
There’s also this great piece by in the Atlantic, which does a good job of explaining the concept of overdispersion
Fascinating. Break clusters. Time, ventilation and people density - key issues. K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
How to promote k, overdispersion: 1. Don't wear a 😷 2. Rely on a test w/ >30% false negative rate and reassure people that if they test - don't need a mask 3. Bring a big crowd together, no physical distancing For more on this, 's got it covered
This is a potentially very important article. Suggests that quite a bit of what we think we know about Covid transmission and spread is misleading and that parts of the British approach have been wrong.
K: The Overlooked Variable That's Driving the Pandemic - The Atlantic k, the measure of its dispersion
K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
Superb article on the importance of transmission heterogeneity in the trajectory of covid outbreaks
This article by ⁦⁩ is one of the most useful and insightful of the whole pandemic. The key variable is dispersion, and the best way to measure it is backwards tracing. We and others could have helped if there was any will.
““prolonged contact, poor ventilation, [a] highly infectious person, [and] crowding” as the key elements for a super-spreader event. [it]can also occur indoors beyond the 6feet, [virus] can travel through the air & accumulate, esp. if ventilation is poor.”
Important analysis by . This pandemic is nonlinear and stochastic. It’s about clusters and “super-spreading”—extreme events that make averages misleading.
Also, if you suspect that I went to the trouble of writing ~5000 words on overdispersion so I could type "the mean is not the message", I admit nothing.
This is completely fascinating and so smart, by . Also... wow: "Just one woman, dubbed Patient 31, generated more than 5,000 known cases in a megachurch cluster."
It's easy to think the more restrictive our pandemic response, the more effective. But as so aptly explains, restrictiveness gets us nowhere when we're restricting the wrong things.
Such an important piece by , on mounting evidence that coronavirus is heavily driven by superspreader events. The implication: focus efforts on limiting superspreader events and lighten up on costly & cumbersome efforts vs more routine transmission.
This joint on the science of overdispersion and super-spreading events is not only a magnificent piece of analysis, it's also eerily prophetic. We posted it ... Wednesday.
This, from the great ⁦⁦⁦⁦⁩, explains so much of what’s unfolding before our eyes: The Overlooked Variable That's Driving the Pandemic. Via ⁦
This is probably the most insightful thing I've read about COVID in quite a while Understanding the stochastic nature of transmission explains the explosive early growth in epidemics, until it shifts to a more deterministic spread as saturation is reached
A dense read, but incredibly interesting.
🙄Anyone proposing Sweden as a contrarian example has *no idea* what they're talking about. Sweden had a more strict & early indoor gathering limit than most of Europe, never lifted it when others did & everyone 16+ is in virtual school. Japan! Try Japan!
בשלב הזה כולנו מכירים את מקדם ההדבקה R: כמה אנשים בממוצע מדביק כל נשא של הנגיף. אבל מעבר לR יש עוד אות חשובה: k, מקדם ״פיזור-היתר״ או overdispersion. שרשור בעקבות כתבה מצויינת של (שאני ממליץ לעקוב אחריה בכללי). שרשור --> 1/14
Possibly the best thing I've read on the pandemic since it all started. By , naturally.
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
In complex systems with network effects and long-tail distributions, *averages* like R_0 are often misleading.
I don't agree with all of 's piece but it is exactly what supposedly serious outlets like SHOULD be offering - a non-hysterical take that tracing "super-spreader events" is crucial to controlling #Covid, with ideas about ways to do so.
Great article by explaining the importance of superspreaders in driving the Covid pandemic, with important implications for how to fight it.
This is as informative and clear as everyone says. Essential reading - for those in charge of policy, and those at risk of infection (that's all of us)
Really fascinating look at how and why #covid19 may spread the way it does. K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
This is what great scientific journalism looks like: finding striking examples to explain what's going on without resorting to specialist jargon. Great work from
See the forest and commit to cluster-busting. Chase those pandemic butterflies! Backward tracing may save us from this stochastic virus. HT
After 9 months of collecting epidemiological data, we know that this is an overdispersed pathogen, meaning that it tends to spread in clusters, but this knowledge has not yet fully entered our way of thinking about the pandemic—or our preventive practices.
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
Containing #covid19 spread: use "backward tracing" to limit super spreader event conditions-follow #Japan's strategy. Outstanding contribution from the Atlantic
If you haven't read it yet, the most eye opening thing I've read on Covid-19 in recent weeks. Came out day before Trump diagnosis. I imagine , who has seen around all the corners for months, would write some paragraphs differently 10 days later.
This Overlooked Variable Is the Key to the Pandemic It’s not R. -RT'ing again as it's that good 🙏
. has been a consistently superb commentator on COVID19. In this long, fascinating piece she explains the critical importance of identifying "super-spreader" events, which much tracking & tracing fails to do.
Extraordinary explanatory essay from
The key to better control of the #covid19 pandemic may be in identifying & preventing #superspreading, through backward tracing to identify clusters. Need to rethink our approach. Good article by explaining the rationale.
Fascinating article from on the importance of heterogeneity in SARS-CoV2 transmission. NB for ecologists k is the dispersion parameter from a negative binomial distribution.
Japan’s focus on cluster-busting and the 3C’s (crowds, closed spaces, close contact) seems to be working for them.
K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
This is a great read. Dispersion, back tracing and cluster busting You’ll love it ⁦⁩ K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
C19 is not the flu! “an early run of bad luck with a few super-spreading events, or clusters, can produce dramatically different outcomes even for otherwise similar countries” Need rapid testing smartly rolled out; backwards tracing and cluster busting...
I found this very convincing and informative. K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
“The reason for backward tracing’s importance is similar to what the sociologist Scott L. Feld called the friendship paradox: Your friends are, on average, going to have more friends than you.”
This by is great. I read about the the k/overdispersion stuff when the early research came out but didn’t think it through to the backward tracing and friendship paradox effects. Hope this hits policy people.
Move over R: let's talk about k. Coronavirus seems to have a high k, meaning it often spreads in clusters. The policy implications of this are fascinating: I particularly enjoyed the section on backward contact tracing.
This Overlooked Variable Is the Key to the #COVID19 #Coronavirus #Pandemic: That variable is the dispersion parameter k. Preventing super-spreading events can massively reduce infections!
Zeynep Tufekci's new article in the Atlantic has insights on how COVID-19 spreads and can be managed. It covers many less familiar concepts: overdispersion, super-spreading characteristics, k vs. Rt, backward tracing, and test sensitivity vs. selectivity.
Finally read the Atlantic piece about COVID and overdispersion. If it is correct, UK policy is almost exactly wrong: wrecking everyday social contact while facilitating superspreading events in bars and restaurants .....
More on fat-tail distributions being *key* in understanding the pandemics, beyond Gaussian-like averages. We're not profiting enough from the best chance ever of getting the population to embrace knowledge about power-laws here. #SciComm #ComplexSystems👇
It’s exhausting watching the world react politically in response to COVID instead of following the science. The fact that economics are divorced from the realities of the biosphere makes it worse.
A very cool story on overdispersion of COVID-19 transmission--interesting in its own right, and great if you're teaching negative binomial models!
Long read but very interesting and informative👇👇
must read, least because it cites Feld's friendship paradox
#COVID19 occurence is highly variable because it is spread by #SuperSpreaders. R is unless! In South Korea, just one woman, Patient 31, generated 5,000 cases in a megachurch cluster. We need to focus on finding these highly contagious people now ☣️
“Once an infected person is identified, we try to find out with whom they interacted afterward... But that’s not the only way to trace contacts... Instead, in many cases, we should try to work backwards to see who first infected the subject.”
Vahva lukusuositus - selkeytti omaakin ajattelua: Miksi korona leviää eri tavalla kuin influenssa, miten supertartuttajat luovat sattumalle ison roolin, ja kuinka suuri käytännön merkitys on supertartuntojen ehkäisyllä ja jäljityksellä. Lyhyt ketju
Hard to overstate how important these findings are. Overwhelming majority of infected people will spread covid to ZERO others. Ending the epidemic is about stopping super-spreader events, likely with rapid, cheap, less-accurate testing on a massive scale.
There is a reason why everyone in your feed is recommending this article. A brilliant piece by ! "By now many people have heard about R0. But to really understand the mysteries of the pandemic we need to think about a different variable: k."
"10 to 20 percent of infected people may be responsible for as much as 80 to 90 percent of transmission and that many people barely transmit it."
K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
“Overdispersion makes it harder for us to absorb lessons from the world, because it interferes with how we ordinarily think about cause and effect.” -⁦⁩ K: The Overlooked Variable That's Driving the Pandemic - The Atlantic
As a steward of literature, I’m drawn to this section: “...SARS-CoV ... that caused the 2003 SARS outbreak, was also overdispersed in this way... MERS, another coronavirus cousin of SARS, also appears overdispersed...”
1/13 - “To fight a super-spreading disease effectively, policy makers need to figure out why super-spreading happens, and they need to understand how it affects everything, including our contact-tracing methods and our testing regimes.” Via
An extremely insightful piece of #SciComm and helpful for thinking like an #epidemiologist about #COVID19 transmission and spread
This is an important article. When I once worked on a viral marketing platform, I learned that even though you could figure out viral coefficients, another thing to notice was that on the web most people barely spread things but a few are super spreaders.
I endorse for president
We gotta talk more about #covid19 clusters, reminds us in this excellent piece
The Overlooked Variable That's Driving the #Coronavirus Pandemic: K, the measure of dispersion, by ⁦⁩ via ⁦⁩ #Covid19
this from is absolutely on point
As usual, is required reading. And I'm not just saying that because she let me be a Hitoshi Oshitani fanboy in the pages of
"....averages aren’t always useful for understanding the distribution of a phenomenon, especially if it has widely varying behavior. an early run of bad luck with a few super-spreading events, or clusters, can produce dramatically different outcomes...."
This is impressionistic, but looking at what seems to be an explosion of Covid in the White House, Zeynep Tufecki's () piece in the Atlantic seems on target.... It's not R, it's k. Also this earlier piece.
reading about the underrated application of 80/20 to COVID spread. “This highly skewed, imbalanced distribution means an early run of bad luck...can produce dramatically different outcomes even for otherwise similar countries.”
This is a must-read from on how COVID-19 doesn't spread like the flu, and how that matters for what steps we can take to stop it.
. on superspreaders & cluster busting as key to #COVID19. Evidence that superspreader events are key has been out there since early March - baffling why so many health authorities (like the UK's) stuck to ineffective flu-based modelling so long
Very interesting piece, making the point that preparedness and capacity for "cluster busting" (using standard communicable disease management tools) is the key to manage the Covid-19 pandemic well.
Good long read on the relevance of dispersion for the pandemic
Interesting suggestion that the constant, k, which describes the degree of clustering, explains a lot of oddities of this pandemic...
"In an overdispersed regime, identifying transmission events (someone infected someone else) is more important than identifying infected individuals."
Here's the most interestintg #COVID19 article I've read in quite some time. Leads to good sources; very apposite, intruguing, suggestive
“...k... is... simply a way of asking whether a virus spreads in a steady manner or in big bursts, whereby one person infects many, all at once.” Great explanation of why k is a more illuminating measure then R for COVID-19. (h/t )
We’re still trying to figure out “k” when it comes to Coronavirus. It’s tricky, but the long and the short of it: a yuge “k,” just a really big “k,” means lots of minimal transmission along with big bursts of superspreader transmissions.
Interesting network properties of COVID-19. Overdispersion (high variance/mean) points to the need for backward testing (trace forward contacts of the infecting person rather than the infected person's forward contacts) and for using cheap rapid tests.
A must read, esp. for #COVID modeling, contact tracing, or counterfactual comparisons. Thank you ! TLDR: 1) Averages & Rt don’t work well w/ skewed distributions. 2) Ponder backward tracing. 3) Luck plays a big role. #causalinference #epitwitter
Why do border guards think the average person travels more than they do? The answer may explain the mysteries of the pandemic, and, writes in a brilliant article, why the variable k (for super-spreading) is more important than the average R0.
Absolutely brilliant ⁦⁩ piece that shifts the lens through which we understand SARS-CoV-2 spreading, and how to think about control strategies, including which diagnostic tests to use when. Bottomline: not one size fits all approach
The Atlantic has some of the best science journalism out there. This article on why we should be focusing on the overdispersion of COVID19 transmission is fantastic.
Who yer gonna call? Cluster-busters! How to slow this pandemic ⁦⁩ h/t ⁦
The best-written, most insightful piece I've read on how COVID spreads and how to contain it. From ⁩, who's fast establishing.herself as one the most important intellectuals/communicators of our time. I hope Biden's team is reading up on this.
K: The Overlooked Variable That's Driving the Pandemic
"we see that super-spreading clusters of COVID-19 almost overwhelmingly occur in poorly ventilated, indoor environments where many people congregate over time….especially when there is loud talking or singing without masks”, #Covid19
This overlooked variable is the key to #coronavirus pandemic: It's not R via
2020 year of stochastic disease & stochastic terrorism
RIVM: 'Tweede golf uit Spanje en Zuid-Frankrijk naar Nederland gekomen' via Wordt hier niet wat over het hoofd gezien?
I can just about cope with my nephew asking me about R, but if hear people talking about k down the pub I’ll lose it! Still, this is a great article.
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
At this point of the pandemic, unless it is breaking science/medicine, I don't learn much new about the pandemic. But this article, if not entirely new to me, really helped me think about the pandemic's spread (whether or not I think about "k")
RT : K: The Overlooked Variable That's Driving the Pandemic - The Atlantic k, the measure of its dispersion
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
In this brilliant piece, introduces the world to the dispersion parameter
Dispersion — The Overlooked Variable Driving the Pandemic - The Atlantic #COVID19
Interesting piece (to my amateur eyes) that, if correct, would speak in favour of privileging backward tracing and cheap, comfortable, rapid, low-sensitivity tests
Voor zover ik kan inschatten is dat een uitstekende strategie. Zie dit grondige stuk uit The Atlantic dat ik eerder deelde: En dit uitstekende interview met Hitoshi Oshitani in vandaag. /2
But 87% of cases not infecting anyone else is very much in line with what we know about superspreading/clustering of transmission! Multiple studies have suggested "10 to 20% of infected people may be responsible for as much as 80 to 90% of transmission."
Take a look at my article. Almost every country got multiple introductions, just like the United States, and some even had bigger outbreaks than we did early on and did not have draconian shutdowns, just appropriate response to the threat, and it worked.
Revelatory analysis from Zeynep which might explain some interesting outliers eg Sweden and Japan
super-spreading clusters of COVID-19 almost overwhelmingly occur in poorly ventilated, indoor environments where many people congregate over time especially when there is loud talking or singing without masks.
Useful examination of heterogeneity in Covid transmission. Cluster busting contact tracing sensible
good read re: how Covid spreads
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
One of the most insightful pieces on the pandemic I've read in a while. Is 'overdispersion' the key variable rather than R0? Averages can be misleading. As they say, a statistician can drown while crossing a river that is 'on average' 30 cm deep. /1
Fascinating article.
Important role of 'k' - individual variation in infectiousness - in explaining highly sporadic nature of epidemics, not captured by R0 alone - terrific overview here
K: The Overlooked Variable That's Driving the Pandemic - The Atlantic #COVID19ireland ⁦
K: the overlooked variable that's driving the pandemic. Read and share.
Interesting (longish) piece #COVID19 #superSpreading
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k. .
This Overlooked Variable Is the Key to the Pandemic, It’s not R., by Zeynep Tufecki ⁦⁩ / ⁦
I had a Twitter discussion a few weeks ago about the potential stochastic nature of coronavirus. Here is an excellent article by that goes in depth on the topic.
Are we missing something by focusing on the R number.... To really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
By now many people have heard about R0. But to really understand the mysteries of the pandemic, writes, we need to think about a different variable: k.
Fascinating article by : By now many people have heard about R0. But to really understand the mysteries of the pandemic we need to think about a different variable
(6) "Multiple comparisons fallacy" aka "look-elsewhere effect" case of "Texas sharpshooter fallacy": attempts to explain why some very comparable cities/groups have large outbreak and others don't, while this pandemic is known to exhibit large dispersion.
Currently reading, obvious reasons