Biostatistics Seminar - 6.2.2020

June 08, 2020

Nicholas Christakis, Sterling Professor of Social and Natural Science, Internal Medicine & Biomedical Engineering

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00:00- Hi everyone, welcome to the sixth seminar
00:04of our seminar series on COVID-19
00:06organized by the Department of Biostatistics
00:08at Yale University.
00:10I'm very pleased to have here today Nicholas Christakis.
00:14He's a senior professor of social and medical science
00:19at Yale university.
00:21He's very well know for his research on social networks
00:26and his recent work focuses on how essentially human biology
00:31and health affect and are affected
00:36by social interaction, social networks.
00:39So today, he's gonna talk a little bit
00:42about the epidemiology of COVID-19.
00:45He's gonna give us an overview and updates
00:48and then he's gonna talk about his recent paper
00:52just published in Nature
00:54on how to use mobility data and population overflow
00:59from Wuhan to predict the spread of the COVID-19
01:03in all the areas of China.
01:05And then finally he's gonna talk about the new shining app
01:08called Hunala which is gonna use network science
01:14to essentially develop network sensors
01:18to four epidemic forecasting.
01:23So Nicholas is gonna take question anytime.
01:26So you're welcome to write questions in the chat box.
01:30And I will try to monitor it and read them to him.
01:34Or you can just unmute yourself and ask questions any time.
01:39- Well raise your hands,
01:41I can monitor the participant list
01:43for raised hands electronically raised.
01:46That's easy for me.
01:47- All right.
01:48So Nicholas, thank you for participating
01:52and why don't you take it from here.
01:55- Thank you Laura.
01:56Thank you so much.
01:57I see many names that I recognize
01:59on this big panel in front of me.
02:02I'm gonna talk without slides
02:03because I find it very stressful
02:05and weird to use slides on Zoom.
02:08I find Zoom like probably many of you
02:10do pretty weird already.
02:12It's so disembodied and taxing in some ways.
02:15So I'm just gonna tell you a little bit
02:17about the epidemiology of Coronavirus
02:19as it has come to be known by many people around the world
02:22in the last few months since the epidemic started.
02:26And some of these things may be very simple or known to you,
02:30others will not be perhaps known to you,
02:32I hope I will tell you some things you don't know.
02:34And I'm happy to take questions at any time.
02:36And then towards the end,
02:37I'm gonna tell you a little bit about some of the projects
02:40in my lab that have raised a number,
02:42are raising a number of difficult statistical questions
02:45that we are absolutely eager
02:46to collaborate with people about.
02:49And Laura has been interacting with us now
02:52for quite a number of years
02:53as I've certain others of you
02:55that I can see on this list.
02:57So we are experiencing something very unusual
03:01in our species that happens from time to time,
03:04which is the introduction of a new pathogen.
03:07We happen to be alive at a moment
03:09when a new germ is entering our species
03:12and having what is known as an ecological release.
03:16It's just like when the rats were first introduced
03:18to New Zealand, and they found you know, Terra Incognita,
03:22and could just take over and do whatever they wanted.
03:25This virus spent decades evolving in barks,
03:28probably spent some time in pangolins
03:30that's still being worked out.
03:32And then in an unseen way,
03:34almost surely in October or November in Wuhan China,
03:37leapt into human beings and gradually spread among them
03:41and then spread around the world.
03:44This pathogen SARS-CoV-2
03:47bears a strong similarity to other pathogens
03:50that have been long circulating in bats.
03:52And it's the seventh such Coronavirus species
03:57that afflicts us.
03:58There are four species of Coronavirus
04:01that just cause the common cold,
04:03they cause about 20 or 30% of the common cold
04:06that people get.
04:07The other viruses that cause the common cold
04:09are other species of viruses.
04:11And two of these Corona viruses also came from bats.
04:15In addition, there are two prior Coronavirus,
04:18a serious Corona viruses that have afflicted us,
04:22what is the so called SARS-1 that was pandemic in 2003.
04:27It was a kind of limited pandemic, which I think,
04:30on the one hand, gave certain Asian countries
04:33a taste of what could happen so they prepared well.
04:37But on the other hand,
04:39because the pandemic petered out,
04:40it kind of lulled the rest of the world
04:42into a false sense of security.
04:44And then the seventh, before the current SARS-CoV-2,
04:49the seventh Corona virus
04:50is something called Middle Eastern Respiratory Syndrome,
04:53or MERS, which is a virus that has a R naught
04:57which we'll talk about in a moment
04:58of less than one we think.
05:00Each infection yields about .9 new infections.
05:05So that epidemic self extinguishes,
05:07which is one reason
05:08that MERS has not become as serious as SARS-2 has become.
05:14Anyway this SARS-CoV-2 leapt into humans
05:17sometime in November, started causing cases in December.
05:22And by the middle of January,
05:23the Chinese knew that it was extremely serious.
05:28I was contacted by some colleagues in China and Hong Kong
05:31on January 23 or 24th,
05:33about the possibility of collaborating to do some work.
05:36We had been working for a long time,
05:38using phone data from China to look at the impact of things
05:42like earthquakes on shaping people's social interactions,
05:46or the building of high speed rail lines.
05:49So we had a well established collaboration
05:51and well established procedures for handling data.
05:54And they contacted me
05:55and we decided to study the impact
05:58or something to do with phone data
06:00and the pandemic.
06:02And so we began working in earnest on the 24th of January,
06:07and all the one 58,
06:08it reminded me of when I was a graduate student,
06:10because we worked non stop for three weeks,
06:13it was very exciting.
06:14And of course, because they were on the other side
06:16of the world, you know,
06:17I would work during the day and then hand it to them,
06:19and then they wake up
06:20and they work during their day while I slept.
06:22And then it would come back to me.
06:24When we submitted the paper on February the 18th,
06:26and it was ultimately published two months later
06:29in the middle of April.
06:31That's the paper that Laura mentioned.
06:32And in this paper, what we did is is we had phone data
06:35on 11 and a half million transits through Wuhan.
06:39We could track people as they transited through Wuhan
06:42and spread out around the country.
06:46And we had the misfortune as a species,
06:49that this epidemic left to us at a moment in time
06:53and in a place where the one of the largest I think,
06:57annual human migration takes place.
06:59During that annual Harvest Moon Festival in China,
07:04the new year festival.
07:06So there are 3 billion translocations of people
07:11that take place in China in the run up to this holiday,
07:15which was on January 24th or 25th this year.
07:19So the virus steps into us at a time
07:22when people are spreading out,
07:23and millions of Chinese moved throughout the country,
07:26including transiting through Wuhan.
07:28And unbeknownst to them, they carry the virus with them.
07:31And what we were able to do
07:32is using simply the movement of people,
07:35track with phone data, the aggregate number of people
07:38that left Wuhan between January 1st and January 24th,
07:42and spread out to the other 296 prefectures of China.
07:46By tracking the number of people who left
07:49and carry the germ with them,
07:50we were able to build a model that allowed us
07:52to predict the timing, intensity
07:55and location of the epidemic up through late February.
08:00And this model we believe,
08:02and I'll return to a little bit later,
08:03this model we believe could be useful
08:06in other sorts of situations
08:08in which there is a risk source or risk sources
08:11that one is trying to assess in terms of its impact
08:16on this spreading of an epidemic,
08:18especially if there's data available.
08:20And I'll come back to that later.
08:24So of course, these people in China in Wuhan,
08:26then of course spread out throughout the world.
08:28The Chinese were criticized for closing
08:31their internal borders by January the 25th,
08:36the Chinese had imposed stay at home orders
08:39on prefectures in China,
08:41that encompassed past 930 million people.
08:46So beginning on January 25, nearly a billion people
08:50were under some form of home isolation.
08:52And this really got my attention,
08:54because the Chinese had judged
08:56that in order to combat this pathogen,
08:58that the enemy that they were facing
09:00and the virus required them to basically detonate
09:04a social nuclear weapon.
09:06This was how strong they rightly in my view
09:09felt that the epidemic was.
09:12So they close down their own country,
09:15but they lagged a little in closing down travel
09:17and leaving Wuhan.
09:19Some people have have said
09:20that there was some conspiracy to do that.
09:22I see no evidence of that.
09:24I just think they were scrambling to cope with a pandemic,
09:26they closed internal travel
09:28but didn't close external travel till a week or so later.
09:32And without of course the germ you know,
09:34spread around the world.
09:35Although it would have spread no matter what.
09:38It's in the nature of these pathogens
09:40once they take root.
09:42There's really no stopping them
09:45as I alluded to earlier.
09:49So the Chinese quarantined Wuhan
09:52and then Hubei province surrounded home
09:54to 58 million people on January the 24th.
09:59Now, the first paper about this pathogen,
10:02regarding the first 41 cases
10:04appeared in The Lancet on the same date
10:06around January the 24th.
10:09And that very first paper noted the extreme likelihood
10:12of interpersonal spread and the severity of the infection.
10:15So the nature of what we were confronting
10:18was well understood by scientists early in January.
10:23I don't think we can claim that we had no idea
10:27there was interpersonal spread,
10:28or that it was serious.
10:30And the virus we now know from genetic studies
10:34arrived in Seattle already by the middle of January.
10:37And this is one of the reasons that border closures
10:39are so ineffective as other scientists
10:42including Neil Ferguson's group, and Mark Lipschitz's group
10:46have also looked at
10:48is that by the time you're aware of what's happening,
10:51and you try to close the borders, it's too late.
10:54The pathogen has spread, you know, surreptitiously
10:57and cross the borders.
10:59And in fact, it had arrived in Seattle
11:01by the middle of January,
11:02and via Italy, in New York City by the middle of February.
11:07And by then, after that point,
11:10most of the cases throughout the rest of the United States
11:13actually were seated from internal cases.
11:17And eventually, community transmission took over
11:20at importation, whether from abroad or from other states
11:23became a progressively tinier fraction of the size
11:26of operates at any particular location.
11:29This is, again, typical of what happens with epidemics.
11:33You know, some cases move in, epidemic starts,
11:36and then it just takes off
11:37and it's not doesn't matter
11:39how many more people come in to a location.
11:44And of course, it's spread into other countries
11:46around the world as well.
11:48Now right from the beginning,
11:49there was a lot of effort
11:51to estimate key epidemiological parameters
11:54about this pathogen.
11:55And I suspect everyone in this group knows about this,
11:58but I'll just review quickly what's known,
12:00and then highlight one other interesting parameter
12:02that not as many people pay attention on
12:05but I know this group will be interested in.
12:08So the so called R naught, the R0
12:11is the number of new cases
12:13in a fully susceptible non-immune
12:16normally interacting, typical population.
12:20That's an attempt to measure something intrinsic
12:22about the virus in a kind of typical human population
12:26where no one is immune,
12:27the virus is brand new to us,
12:30people are interacting normally,
12:31we haven't yet taken any protective action.
12:34So this is known as the R naught.
12:37And for this germ, for SARS-CoV-2,
12:39it's probably around two and a half,
12:42and it could be as high as three.
12:45This is high, actually.
12:47The seasonal flu has an R naught of about 1.3 to 1.6.
12:53Chickenpox has an R naught of 3.5 to 6.
12:56Ebola has an R naught of 1.5 to 1.9.
13:01And of course the champion pathogen is measles,
13:04which has an R naught of 18,
13:06which is why vaccination rates for measles
13:09have to be so high,
13:11because the pathogen is so infectious,
13:13there's a relationship between the amount of the pathogen
13:16and the required vaccination rate to stop it,
13:19before you could get herd immunity for example.
13:23Now, the so called Re, R sub e
13:26is the effective reproductive rate.
13:28This is the number of new cases as the epidemic proceeds,
13:32and as immunity rises, or as people take action.
13:36And this number can fall and change.
13:38So for example, if we all of a sudden became hermits,
13:42nobody interacted with anyone else,
13:44the Re would fall below one.
13:46This is actually what happened to China.
13:48They were able to track the Re
13:50and find that whereas it started
13:52at around 3 in Wuhan in January.
13:54After their national lockdowns,
13:56it fell to about 0.3,
13:58each new case only created a third of a new case.
14:02So that's, you know, when the epidemic extinguishes.
14:06So this Re is very sensitive to the natural rise
14:10of immune people in the population,
14:12and also the human behaviors
14:13or ultimately for lucky vaccination that we might implement.
14:18But there's another very important parameter
14:20that I think will interest this group
14:22and that many that perhaps not all of you have heard about,
14:25which is the variance in the R naught,
14:27or the variance in the Re.
14:30And there was a landmark paper
14:31that was published by Lloyd Smith
14:33and his colleagues in Nature in 2005,
14:36that quantify this using a dispersion parameter
14:39they called Kappa,
14:40which seeks to quantify the interindividual variation
14:44in the R, in the reproductive rate of the pathogen.
14:48So imagine a situation where,
14:52for everyone, every single person in the population,
14:55the R is two, each person infects two other people,
14:59and another situation in which it is zero
15:02for many people, but let's say 50 for one person,
15:05imagine the population, a small population.
15:08The average R in these two situations could be the same.
15:13But the ability of the epidemic to establish itself
15:17actually could be quite different,
15:19and would be much easier in the former case.
15:22In the latter case, you have more super spreading events.
15:26There's a variance in the R
15:28so you've got that right tail distribution,
15:30some situations where one person might infect 50
15:33or 100 people, but also most of the cases
15:36are dead ends, most people infect no one.
15:39So in a population of such people infected
15:41with such a germ,
15:42if one person leaves and goes somewhere else,
15:45most of the time they won't be able to establish an epidemic
15:48in the new location.
15:50So the random movement of people from one population
15:52to another, from a risks source to another place
15:55won't be able to establish an epidemic.
15:58So this dispersion parameter is actually quite important
16:02for what might happen in these types of a situation.
16:05And it turns out that the dispersion parameter for SARS-2,
16:10what we're currently facing
16:11is smaller, the variance is smaller
16:14than the variance was for SARS-1.
16:17And this actually is one of the things
16:19that's making SARS-2 worse for us.
16:21Even though there are super spreading events now,
16:24they are fewer than they were for the previous pathogen.
16:28And more often now, a move of a person
16:31from one place to another starts the epidemic
16:34and can result in it taking off.
16:38Now, superspreading depends not only on the pathogen,
16:40but also of course on the host,
16:42attributes of the host that are immunity to the pathogen,
16:46how irritable like some people,
16:48let's say my cough more than other people,
16:50so that might make me more likely
16:52to be a super spreader than you.
16:55Super-spreading events also have to do with the environment.
16:57This is why a pact conferences of people
17:01are more likely to cause super spreading events,
17:04then open air concerts and so forth.
17:08So people try quickly to get a sense
17:11of the reproductive rate of this pathogen
17:13and they were successful.
17:15There have been like dozens of studies now quantifying this
17:17and the summary statistic
17:19is around two and a half that I told you.
17:21And also the dispersion parameter
17:23they tried to quantify.
17:25Distinctly, people tried to quantify the case fatality rate
17:28or the infection fatality rate of this parameter.
17:31And there's still ongoing of this pathogen
17:34and there's still ongoing debate about the CFR
17:37and the IFR.
17:39The CFR is the fraction of people who die conditional
17:44on their coming to medical attention,
17:46or a little bit better definition,
17:49conditional on their developing symptoms,
17:52something which is called the S-CFR,
17:54the symptomatic case fatality ratio.
17:57And we think this number is about between 0.5 and 1% still.
18:01It could be as low as 0.3%.
18:05But I doubt that it's any lower.
18:07And notice that the case fatality rate
18:09is very sensitive to people's behavior.
18:12You know, do people seek medical care?
18:14You know, if they have mild symptoms from the disease,
18:16they might never tell anybody.
18:19Or it's sensitive to the ability of the healthcare system
18:21to save their lives.
18:23So this is not something that's sort of written in stone,
18:26but it's something that attempts
18:27to quantify how lethal a pathogen is it
18:30that we have on our hands.
18:32The case fatality rate for the seasonal flu is about 0.1%.
18:37So on average, about one out of 1000 people
18:39who get seasonal flu will die,
18:41and SARS-2, the current pathogen we're facing
18:45is less deadly than SARS-1.
18:47The case fatality rate for SARS-1 was about 10%.
18:51Yeah, was about 10%.
18:53So it's about about 10 times as deadly
18:56as the current pathogen.
18:58And similarly the case fatality rate
19:00for the 1918 flu pandemic,
19:03which was very bad, was about 4 to 5%.
19:07Now, one of the things that's interesting about this,
19:09as many of you may know
19:10is that actually a less fatal disease is more difficult
19:15to treat, to stop,
19:20because when the disease kills us rapidly
19:22like Ebola, the victim dies
19:25before they can transmit the disease.
19:28But if the disease's less deadly,
19:30and the person is walking around
19:32for a longer period of time,
19:33while sick, they can infect more people.
19:37So this this difference between SARS-2,
19:39what we're facing and SARS-2 in 2003,
19:43I should have mentioned that the SARS-1 petered out
19:45there were only eight and a half thousand cases worldwide.
19:48You know, it was a trivial pandemic
19:51compared to what we're facing now.
19:54So and I forgot how many deaths but it was in,
19:57you know, I think 500 or six 700 deaths
20:00from the SARS-1 pandemic.
20:03So the lower the fatality of this pandemic,
20:06ironically makes it more dangerous,
20:09lower the fatality on a per case basis,
20:11because it can spread farther
20:12and ultimately cause many deaths.
20:15And in general, it's an evolutionary biology principle
20:18that the pathogens don't want to kill us.
20:21That is to say, pathogens do better
20:24when they're not as deadly because they can spread.
20:27And also variants of the pathogen
20:30that don't kill us or don't kill us fast,
20:33typically outstrip variants that do kill us fast.
20:38So that's one of the reasons in general
20:40we tend to see the evolution of pathogens
20:43to be less severe as time goes by.
20:46And I'll come back to this point as well in just a moment.
20:52And then the infection fatality rate
20:55as distinct from the case fatality rate,
20:57is the fraction of people who get infected and die.
21:02Not the ones that come to medical attention.
21:04So we think that about 50% or develop symptoms,
21:08we think that about 50% of people
21:10who get SARS-2 are asymptomatic.
21:13And so this means that in this case,
21:15because of that 50% number,
21:18it means that the IFR is about half the CFR in this case.
21:22So, half the people that get infected,
21:26don't get any symptoms at all.
21:28And so this makes the IFR lower
21:30by a factor of two than the CFR.
21:34Now you can take these two parameters,
21:36the reproductive rate and the case fatality rate,
21:39and you can put them on a little graph
21:41and then you can plot all of the pandemics
21:44that have occurred, let's say in the last hundred years,
21:47this is a typical exercise that epidemiologists engage in.
21:51And if you do that, you find very distressingly
21:55that the SARS-2 pandemic
21:57falls between the 1957 influenza A pandemic,
22:02which was the second deadliest pandemic
22:04we've had in the last hundred years.
22:07And the 1918 pandemic, which is the deadliest.
22:10So, this is a serious pathogen SARS-CoV-2.
22:14It's right there in between the upper right corner is 1918,
22:17it's not as bad as that,
22:19but it's worse than 1957
22:22when you look at these two numerical parameters.
22:26And in fact, it was clear to many people in certainly
22:30by February, that without action,
22:32many people would die.
22:34I think hundreds of thousands of Americans would have died,
22:37had we done nothing.
22:38And unfortunately, I still think
22:40that hundreds of thousands will die.
22:42We've already had 100,000 deaths,
22:44I think we're gonna very likely have at least another couple
22:47of hundred thousand deaths
22:49before the epidemic ultimately winds down
22:52in two or three years.
22:55And this partly relates to the fact
22:56that we're gonna have more waves,
22:58which is a point I'll come back to.
23:00The disease itself has very wide range of presentations,
23:04from asymptomatic to mild to critical
23:07and can affect many organ systems,
23:09not just the upper airway or the lungs,
23:11but also the heart and the kidneys
23:16and the intestinal system and so forth.
23:18And the symptomatology is very protein as well.
23:21People manifest a great variety of symptoms.
23:22There are three clusters of symptoms,
23:25most are respiratory cough, shortness of breath, fever.
23:28Some are the musculoskeletal system, fatigue,
23:32muscle pains, joint pains.
23:34And some are intera, diarrhea, vomiting,
23:37nausea, and again, maybe a fever.
23:42The case fatality rate for this respiratory,
23:45for respiratory diseases in general
23:47typically varies with age.
23:50And most most of the respiratory pandemics
23:54of the last century have had a U-shaped function.
23:57So the very young and the very old
24:00are at the greatest risk of death.
24:03Famously, in 1918, there was a W-shaped function.
24:07There's some interesting theories
24:08if we have time and you're interested,
24:10I can tell you what some other scientists
24:12have speculated as to why it was a W, the very young,
24:15very old were killed and middle aged,
24:18sort of working age young adults, 20s and 30s were killed.
24:24And then finally, there's an L-shaped
24:25or backward L-shaped curve.
24:28So polio has a regular L-shape curve.
24:31So polio pandemics, they kill the young
24:33and they sort of spare the old.
24:35But Coronavirus has a backward L-shaped,
24:38it spares the young and kills the old
24:41and this is unusual,
24:43very unusual actually for a pathogen,
24:46and the fatality rate rises
24:48from about one out of 3000 people younger than 20.
24:52The case fatality rate so conditional on getting sick,
24:55one out of 3000 people will die
24:57to about one out of 100 for people
24:59in their late 50s, early 60s
25:01to about one out of five for people who are older than 80.
25:05So pretty sharp L-shaped curve.
25:10And I found it very poignant,
25:13almost biblical actually
25:15and sweet that this epidemic spared the young
25:19because, you know, the young,
25:20the leading killer of young children is infectious disease,
25:23something like 60% of kids
25:25under five worldwide die of infections.
25:30And the fact that this virus spared them
25:33was very pleasing to me.
25:35And moving actually, and as a parent,
25:39I didn't have to worry about my college age kids
25:41or we have a new child but Eric
25:43and I do that's 10 years old,
25:44and so we didn't have to worry about him, which was helpful.
25:49Now, another important part of the epidemiology
25:51of this condition is something known
25:52as the incubation period.
25:54Incubation period is the time between being infected
25:59and developing symptoms.
26:01And that is between two and 24 days,
26:04I'm sorry, two and 14 days,
26:07the incubation period varies between two and 14 days,
26:10more precise estimates of this have shown recently
26:13and people are studying this a lot, that 97.5% of people,
26:19if they're going to get symptoms,
26:21after being infected, get them by 11 and a half days.
26:26So, people are still studying the details of this,
26:28but the gist is that early on,
26:30it was established that 95% of cases got symptoms
26:33within the first 14 days of infection.
26:36And this is the origin of the 14 day quarantine
26:39that we have all been practicing.
26:42There's a different quantity known as the latency period.
26:46This is the time from infection to infectiousness,
26:48how long between when you're infected
26:50and can infect others and very sadly for us
26:54in this pathogen, unlike SARS-1 in 2003,
27:00this latency period can be a couple of days
27:02shorter than the incubation period.
27:05That means that people can spread the infection
27:08when they're asymptomatic.
27:10And many estimates from China and Italy
27:12and England suggest that the majority of cases,
27:16the bare majority maybe or sometimes the great majority,
27:20arise from this type of transmission.
27:23So most people become infected from other people
27:26who don't have symptoms let's say.
27:29The difference between these two
27:30is known as the mismatch period.
27:33Actually, in veterinary medicine,
27:34there's some veterinary scientists
27:35that call it the Omega period,
27:37which I think is kind of interesting
27:39when you think about the implications for us.
27:44In some cases, the latency period
27:47is shorter than the incubation period,
27:50for example, like in HIV.
27:53So people with HIV can be infectious for years
27:56before they have symptoms,
27:58that makes the disease difficult to control,
28:01or the latency period can be equal to
28:03or longer than the incubation period, like smallpox.
28:08You have to get smallpox vesicles
28:10on your body before you can infect other people.
28:12So we can see who's infected.
28:15And that makes quarantine so much easier
28:18and so much effective.
28:21So the fact that there is a negative mismatch period,
28:24that is to say that the latency is shorter
28:26than the intubation on average.
28:27And this condition is one of the things
28:30that makes it so nasty and difficult to treat.
28:34In terms of transmission modes,
28:36there's a lot of ongoing research on this,
28:37it's clear that the primary mode
28:40is through respiratory droplets, people coughing,
28:44or speaking loudly or singing.
28:47There have been a number of super spreading operates
28:48associated with singing or yelling.
28:52But there's also evidence
28:54that there's airborne transmission
28:55which is small little parts of droplets
28:57come out of your mouth and fall down,
28:59which is why wearing a mask is effective.
29:01Airborne droplets can stay suspended
29:04in the air and spread farther.
29:06There is airborne transmission.
29:07But it's not so bad.
29:10We don't think in this condition.
29:12Although well, I won't go into it.
29:13There's some examples.
29:15There's also spread by fomites,
29:17that's surfaces that we touch.
29:19Although this is increasingly not seen
29:21as a major vehicle for transmission,
29:24there's also fecal transmission
29:25although again, this is not a major explanation
29:28for what's happening in the epidemic.
29:31Now, how do humans respond to epidemics?
29:36Well, the broad division is pharmaceutical interventions,
29:39and so called non-pharmaceutical interventions.
29:42We don't have any pharmaceutical interventions
29:44really for this pathogen.
29:45We have no vaccines for it,
29:47although we're working on it.
29:48We have no drugs, although Remdesivir
29:51has recently been felt to have some benefit
29:53it's modest benefit and we don't have drugs,
29:56and in general viruses are very difficult
29:57to treat, antiviral medications generally
30:00are weak in their effectiveness.
30:03So, just like plague is an ancient threat to human beings,
30:09we have to respond to a familiar enemy
30:12with a familiar response,
30:15which is physical distancing.
30:17People have been physical distancing
30:19in times of plague for centuries.
30:21And unfortunately, that's what we have to do.
30:24We have to engage in a non-pharmaceutical interventions.
30:29There are two broad kinds
30:30of non-pharmaceutical interventions,
30:32individual interventions, things like hand washing,
30:36or mask wearing, or self-isolation
30:40and collective interventions
30:41that required the action of groups of people
30:44or the state, border closures, collective hygiene,
30:48you know, cleaning the subways for example,
30:50testing and tracing, bans on gatherings,
30:54school closures, and ultimately stay at home orders.
30:59And these two classes,
31:01these non-pharmaceutical interventions
31:02can be divided into individual and collective,
31:06but they can be divided in a different way
31:09in what are known as transmission reduction
31:12and contact reduction.
31:14So transmission reduction are things
31:15that try to reduce the likelihood
31:17that conditional on my interacting
31:19with you, I give you the germ.
31:20So wearing a mask or washing my hands
31:22or sanitation measures
31:24might be transmission reduction measures.
31:28But contact reduction or condom use
31:31in the case of HIV is a transmission reduction intervention.
31:35And contact reduction
31:37is when you try to reduce the amount of social mixing.
31:40So gathering bands, self isolation, school closures,
31:44or in the case of HIV reduction and partner number,
31:47those are examples of contact reduction interventions.
31:51And the point of these interventions
31:54however we taxonomise them, is to flatten the curve.
31:58We've all heard about that now,
31:59but why are we trying to flatten the curve?
32:01We're trying to spread out,
32:03we're trying to this wave
32:04is about to hit us with a new pathogen
32:06for which we have no immunity.
32:07And the force, the compressive force
32:09of the wave is gonna hit us,
32:11what we're trying to do is deaden the wave, slow it down,
32:14like build breakwaters offshore,
32:16so maybe even if the same amount of water comes ashore,
32:20it will come ashore with lower intensity.
32:23So that's what we're trying to do.
32:24We're trying to flatten the curve.
32:27And what we mean by that
32:29is that we are going to allow the healthcare system
32:31and the supply chains time to work.
32:34By flattening the curve,
32:35maybe we can save more lives
32:37by not overwhelming our healthcare system.
32:39That's one bit.
32:41The second reason we flatten the curve
32:43is that it postpones some cases
32:45and deaths into the future,
32:48at which time we might have a vaccine,
32:50that might prevent some of the deaths
32:52or we might have better knowledge
32:53of how to treat the condition.
32:55Again, reducing the total number of deaths.
32:57So flattening the curve could reduce deaths
33:00in this way as well merely by the postponement function.
33:04And finally flattening the curve
33:06may be beneficial because it postpones some of the cases
33:10to occur at a time when the pathogen might,
33:13if we're lucky, have mutated to be less deadly.
33:17Remember, we mentioned this earlier.
33:19So if the pathogen has become less deadly,
33:21people will become infected in the future,
33:23we'll get a milder variant of the disease.
33:26But to be clear, what flattening the curve does not do
33:32is eradicate the pathogen.
33:34What we are doing is stopping transmission,
33:36not killing the germ.
33:39The pathogen is still there, and it's going to come back.
33:42It's coming back in Asia,
33:44it's gonna come back in the United States,
33:47there is no escaping from this.
33:49The pathogen is now a feature of our environment
33:52with which we must cope.
33:55Now, one of the features of this pandemic
33:57I won't spend much time on it
33:59is the affliction of healthcare workers,
34:01why health care workers were at special risk.
34:04This increased risk of healthcare workers
34:07has been noted since time immemorial.
34:09Thucydides in the plague of Athens
34:11in 430 BC, talks about how doctors
34:14are dying in greater numbers
34:16and knew the reason.
34:17It's because they're having contact
34:19with sick patients.
34:20The same thing is happening in our society
34:22and happened in China and happened in Italy.
34:25And part of the reason they're at special risk
34:27is that health care workers
34:29in the course of caring for path people,
34:31especially when they have not had
34:32adequate personal protective equipment,
34:35the lack of which has enraged me, in our society.
34:40The reason is they get high viral inoculum.
34:43So they're up close working with the patient,
34:44the patient coughs in their face.
34:46So they, you might get the germ from touching something
34:50in the subway, or interacting with a colleague at work
34:54who speaks loudly and some number of particles
34:57leave that person's mouth
34:58and enter your body, by the time those viruses
35:01are able to multiply, your body might be able
35:04to mount an immune system, immune response
35:07and clamp down on the infection
35:09so you don't get a serious infection.
35:12But a healthcare worker getting a high viral load,
35:15a large inoculum actually can't do that, is overwhelmed,
35:20their body is overwhelmed.
35:22And there's a very moving website
35:23that's tracking the needs of healthcare workers
35:26around the world who have died during this pandemic
35:29and it's growing every day.
35:33Other places of outbreaks
35:34have been nursing homes, prisons, ships.
35:39You've all heard about the cruise ships
35:40and of course the aircraft carrier
35:43and meatpacking plants,
35:45which is a very interesting if you want,
35:47we can talk a little bit about the packing plants.
35:49The burden of this illness falls harder on men.
35:52Men are more likely to die to get it and to die.
35:55But equally likely to get it
35:57but they're more likely to die than women.
36:00And as is typical of infectious diseases,
36:02it's socially stratified,
36:04the poor and the marginalized and the sick
36:07are more prone to die of this condition.
36:13Now, let's turn briefly to this issue of waves
36:16of the pandemic.
36:20Because I think
36:22it's a serious problem.
36:25Every respiratory pandemic, in the last century
36:28has had multiple waves.
36:30And these typically recur in the fall.
36:33Not always, I think,
36:34because of all the protests that we've seen and the rush
36:37to even before the protest, the rush to leave the lockdowns,
36:42I think we're gonna see an earlier wave
36:44in the United States.
36:47And these waves typically come every year
36:50for two or three years
36:52until eventually the epidemic becomes endemic in us.
36:56We saw waves in 2009 with a very mild H1N1 pandemic,
37:01there was a pandemic in 2009,
37:03but that pathogen was not very deadly.
37:05So nobody noticed.
37:06It was actually less deadly than the flu.
37:08So it circulated the whole world
37:10there were waves, we can see the waves of H1N1,
37:13but we didn't care because it didn't kill very many people.
37:16The 1918 pandemic,
37:19the second wave famously came out of phase
37:22with the first wave.
37:23There's some interesting theories
37:25as to why and was much four times
37:27as deadly as the first wave.
37:29We have no way of knowing how deadly the Coronavirus
37:32the second wave will be.
37:35But I don't believe it'll be less deadly
37:38than the first wave for various reasons.
37:42Now the reason for the occurrence
37:43of these waves, it's complicated.
37:45It has to do with human behavior in part,
37:48which is the fact that people return to school
37:50and move indoors with the coming of the fall
37:53and it gets colder.
37:54It has perhaps to do with environmental factors
37:57to the extent that heat
37:58and humidity affect the spread
38:00or modify our body's resistance to the pathogen.
38:03And of course, the epidemic right now has gone
38:06to the southern hemisphere and is raging there.
38:09And Brazil is having for a number of reasons,
38:11including that it didn't make any efforts
38:13to do anything about it.
38:14You know, many, many hundreds of thousands of people
38:17are going to die in Brazil.
38:20Incidentally, I should mention.
38:23I'll just take a small digression
38:25that there's a lot of geographic variation
38:29with these respiratory pandemics.
38:30And we don't fully know the reason.
38:32For example, in the 1957 pandemic,
38:35there was a 30-fold variation
38:37in the final attack rate,
38:38the number of people that got the disease.
38:41So Chile was really hard hit and Egypt was spared in 1957.
38:46We're gonna say that see the same thing
38:48with this pandemic, some parts of the country
38:50will be very hard hit,
38:51other parts of the country will not,
38:54some countries in the world will be hard hit, some will not.
38:57Sometimes this will have to do with the temperature
39:00in the region, sometimes it'll have to do with what
39:02the nations did in response.
39:04But mostly, most of the variants will be chance,
39:07as far as we can tell from previous analyses
39:10of geographic variation in the pandemic.
39:13Anyway, Brazil is being hard hit at the moment.
39:17But the point I wanna make about this
39:19is that these waves illustrate the fundamental point.
39:23But this epidemic is going to become endemic among us.
39:27Either we will develop herd immunity,
39:31probably at around 50%, ultimately,
39:33of people will be required.
39:35And we can talk about, there's a subtle detail here.
39:37So, if you can compute the fraction of people
39:41that need to be infected
39:42before you get herd immunity naturally infected
39:45and naturally immune before you get herd immunity,
39:48with a little formula
39:49that relies on the R naught of the pathogen,
39:51as we mentioned earlier, in the case of measles,
39:54this epidemic has an R,
39:56let's say around two and a half,
39:58it means that about 60% of people need to be immune
40:01before the epidemic goes away.
40:06But actually, you can sometimes reach herd immunity
40:11at lower percentages,
40:12because of the fact that human populations
40:15are not well mixed,
40:17they have a structured, a network structure.
40:19So typically popular people are more likely to get infected
40:24and therefore more likely to get immune.
40:26And once they become immune,
40:28they're no longer pathways for the movement,
40:30they're no longer vectors for the movement of the pathogen.
40:33So in fact, if you immunize,
40:36let's say, 30% of the most popular people
40:38in a population, you could reach herd immunity
40:41at lower percentages.
40:43So, in practice,
40:44what we typically find is that herd immunity
40:46is reached at a lower percentage.
40:49This is, you know, the pre-pharmaceutical era
40:52at a lower percentage than you would predict
40:55based on the amount of the R naught of the pathogen,
40:57for example in 1957,
40:59the epidemic maxed out at around 40%,
41:02was the final attack rate,
41:03you know from retrospective serology studies
41:07that were done after the epidemic.
41:12So, in our case with this pandemic,
41:14either we will get herd immunity, or we will get a vaccine.
41:18And I've now concluded that for whatever it's worth,
41:23that it doesn't really matter which of those two we get
41:26to first because there'll be approximately at the same time,
41:29the likelihood that we will be able to invent
41:31a good vaccine, fast enough, manufactured
41:36and distributed fast enough to outstrip
41:40the inevitable herd immunity seems low to me.
41:43I do think we will get a vaccine eventually,
41:45but I'm no longer putting my hopes
41:47in that as an exit strategy for this pandemic.
41:50Maybe we'll get lucky.
41:51I hope I'm disproven,
41:54not disprove it, but I hope that doesn't prove
41:57to be the case.
41:59So the attack rate if you multiply
42:01all these quantities together
42:03that I've been telling you,
42:04in the end for this pandemic,
42:05in my view will be 40 to 50%.
42:09Maybe more probably higher if we overshoot,
42:13which is another thing, you know,
42:15the epidemic rages onward
42:17before we have a time to actually catch up with it.
42:21And this partly relates to the issue of who are,
42:24you know, like I already said, the popular people
42:26and the acquisition of immunity.
42:30Now, where do we stand with this pandemic so far?
42:33If you look at Cyril prevalence studies
42:34to date in Sweden,
42:36which has adopted a pretty mild approach
42:39to coping with it.
42:40Nationwide there are about 4% of people have had the disease
42:44and are now immune, I think in Stockholm
42:47with seven or eight or 9%
42:49in the most densely populated part of Sweden.
42:52In New York City, it's about 21%
42:54we know from a good study,
42:56and in various era prevalence studies,
42:59no one has really done a perfect study
43:00at anywhere in the United States.
43:02We've been thinking in my lab of doing such a study
43:05in the Greater New Haven area,
43:06picking a random sample of New Haveners,
43:08and then following them prospectively.
43:11Most people have done others sub-optimal.
43:14And I don't criticize them,
43:15it's difficult to get a random sample of people.
43:19But if I had to guess, in our cities,
43:23probably we are at no more than 2, 3, 4, 5%
43:27around the country.
43:28So if we're gonna get to an attack rate of 40,
43:30or 50%, we have a long way to go unfortunately.
43:36I think it's important to note
43:37that the United States response to the pandemic
43:40has been awful, has been completely incompetent frankly.
43:43And the failures in my judgment have occurred
43:46at multiple levels of government, but certainly,
43:49at the White House,
43:51has, you know,
43:52there's been an appalling lack of coordination.
43:55I think the expertise at the CDC was there,
43:58but there were men with deep expertise
44:00at the CDC and deep expertise at the National Institute
44:03of Allergic and Infectious Diseases,
44:08but it hasn't been deployed properly.
44:10But I also think it's fair to say
44:11that many of the state governments
44:13were caught flat footed.
44:14And let's also acknowledge that many European countries
44:16that didn't have the incompetence
44:18at the level of the White House
44:20also seem to have been caught flat footed.
44:22I don't understand why.
44:24The you know, it's not a mystery.
44:27I can reach over and grab a book on my shelf
44:29that's called National Strategy for Influenza Pandemic.
44:33Many, many experts knew what was happening
44:36in January and February.
44:37And the Chinese bought us time, you know,
44:41by locking down their nation.
44:43We had two months to look at what was happening
44:45in China and become concerned.
44:48Let me tell you briefly and then I'll shut up.
44:51What are some of the projects that are happening
44:53in my lab right now
44:57which we'd be welcome.
44:59Welcome collaborators.
45:00And Laura's cooperating with us on some of these things.
45:04We have ongoing work on using big data techniques.
45:08Laura mentioned this paper on human movements.
45:12Many scientists are working on this right now
45:14and there are labs around the world
45:15that are famous for this.
45:16We're trying to contribute to that in a certain way,
45:19a tracking human movements or symptom reporting,
45:24that will using various Big Data techniques,
45:26including Twitter data that would allow us to forecast
45:29the course of the epidemic, to get ahead of it,
45:31to know where it's gonna strike
45:33based on knowing what's happening.
45:34Another similar project like that,
45:36being spearheaded by another graduate student
45:38in my lab, Eric Feltham is looking at gatherings.
45:41For example, we were very interested in the gatherings
45:44to vote the primary elections.
45:45Did they, you know, people got together to vote
45:48at polling places, did that cause a spike?
45:50This is of course highly relevant to our national security,
45:54we need to somehow have a good vote, a fair
45:56and honest vote in November and for that,
45:59in my judgment, We need to have widespread absentee
46:02balloting to allow for this.
46:05Otherwise, if people stay away from the polls,
46:07because they're afraid of the pandemic,
46:09or if they go to the polls and then become infected,
46:12either one of those outcomes
46:14is a threat to our society in my view.
46:16But similarly, I believe that the recent protests
46:19that we've seen after the appalling murder
46:23that we saw in Minnesota,
46:27and the rioting that we've seen,
46:29are gonna contribute to a spike in cases
46:32and I mentioned this earlier.
46:34Finally, we had just released last week
46:37an app from my lab.
46:38That's called Hunala, hunala.yale.edu.
46:47This app relies on some old ideas of ours,
46:50involving network science
46:52that I previously discussed with Raphael
46:54and others on this call,
46:57which is that if you think about
46:59a contagion that begins stochastically in a graph,
47:03you should have the intuition,
47:04it's obvious that the contagion
47:05as it winds its way a social contagion,
47:08winds its way a biological contagion,
47:10that winds its way through the graph
47:11is gonna reach central people
47:13sooner than it reaches random people in the population.
47:17So if we could identify central people,
47:19and monitor them, they would function
47:22as a kind of canary in a coal mine
47:24forecasting the state of the epidemic at some future time.
47:28We published several papers about this 10 years ago,
47:31that showed that we could do it with each one and one,
47:34that we could track central people,
47:36and that we can monitor them.
47:39And then we would therefore be able to use them
47:42to predict the future state of the epidemic
47:44between two and six weeks in advance.
47:49So this is one of the things that our app
47:52is attempting to exploit.
47:53we're attempting to get people to report their symptoms
47:57and then we are attempting to monitor that
48:00or redraw the graph anonymously or privately.
48:05We don't inform anyone
48:06for example, if you get sick, we do not inform your friends
48:10that you are sick.
48:12But we exploit people's reports
48:13to create a kind of ways for Coronavirus.
48:17So everyone contributes a little information
48:19saying whether or not they have symptoms.
48:22And we can then by manipulating that information
48:26using some machine learning algorithms
48:28in partnership with a mean car buses group
48:30in the electrical engineering department here.
48:34We can predict what your risk of getting the epidemic
48:38is in the future.
48:39So if your friends friends friends
48:41had respiratory disease three weeks ago,
48:45that should modify your risk.
48:47Or if your friends had a fever a week ago,
48:50that should modify your risk.
48:52And so our app is attempting to collect these data
48:55and forecasts the future course of the epidemic.
48:57We expect to have, if we're lucky
49:00and if the app is widely adopted,
49:04we will have a ton
49:05of very difficult complicated data to analyze.
49:08And just because I haven't been as clear
49:09about this as I hope because I'm rushing,
49:12the app is like waves for Coronavirus.
49:14It warns you just like waves does
49:17that there's a traffic jam two miles ahead,
49:19the app can warn you that there is pathogen
49:23in your social network neighborhood.
49:25And just like in ways you might, you know, take an exit
49:28and avoid the traffic jam, with ways you might say,
49:30you know, I'm gonna stay at home now,
49:32because my risk is high.
49:34And we give people daily assessments of their risk
49:38based on where they live.
49:40And here we include lots of public source data
49:43and the reports of our users.
49:46And we give them a risk of like based on where you live,
49:49is the risk low, medium, high.
49:52Like for example, like a fire like a forest fire prediction,
49:56you know, based on the humidity today,
49:58what's the risk of a forest fire?
50:00And, we also give you a personal risk
50:03based on where you are in the network.
50:05So traffic is bad in New York City in general,
50:08but it's really bad on your block right now,
50:10waves might tell you.
50:12So our app also does that.
50:15People have reported seeing a fire,
50:17it's not just that it's dry and hot,
50:19actually other users in your area have seen a fire.
50:22So your risk now is much higher.
50:26So I'm gonna say one last thing,
50:27which is that the issue is when will this epidemic end?
50:32And I already alluded to the fact that
50:33it's gonna end when it becomes endemic among us,
50:36but it's also gonna end,
50:39or epidemics have a biological end and a social end.
50:43And basically, the epidemic is gonna end
50:45when we declare that it's over.
50:46But we have come to accept it.
50:49And so I think we're still in the early phases
50:51of the Kübler-Ross model of grief.
50:54You know, we have anger
50:58and we have sadness, you know, depression.
51:01And we have bargaining, you know,
51:03but soon we're gonna have acceptance,
51:05which is the only way out from this pandemic.
51:10Thank you.
51:12- Thank you Nicholas for this great talk.
51:15We have time for questions.
51:22- [Jose] Hello, I'm Jose.
51:24So I was curious about the app
51:27that you develop in collecting data.
51:30Are you interested in like, what network properties
51:36are you like hoping to gauge, are you looking into,
51:39like sensitivity, you know, like a core.
51:45And do you have a particular hypotheses about like,
51:50because like when you're in a network,
51:52you receive information,
51:54the more connected you are just like,
51:57forgot the term but I think you tend to get more information
52:01based on your connection,
52:03are you expecting that people in certain types of networks
52:08would be reporting
52:11like symptoms like earlier?
52:13Or I was just wondering like,
52:16what are you hoping to capture using those parameters?
52:19- So we've studied that if you want,
52:21you can look at our 2010 paper on the H1N1.
52:24Networks with low transitivity are at higher risk.
52:28So individuals that have low transitivity
52:30in their neighborhood are at higher risk
52:31for getting the flu.
52:33Unsurprisingly, people with higher degree
52:35are at higher risk,
52:36and people that have high centrality are at higher risk.
52:40So we've shown that before.
52:41And eventually we hope if we reach the appropriate scale,
52:44that these parameters will also be relevant.
52:47But it's not just the structure of the network
52:50as I alluded to earlier.
52:51It's what's happening in the network and when.
52:53So I don't know exactly yet.
52:55We don't have the data yet to know what is the real signal.
52:59is it COVID three weeks ago in your third degree alters,
53:03is it COVID 10 days ago or 12 days ago in your alters?
53:08You know, we don't know all these details yet.
53:10If we get enough data, we will know the answer.
53:13But we do know from principles that we published before
53:16and that are mathematically predicted
53:18what should matter.
53:20Excuse me, I've been dying to do a K cornice forecasting.
53:24And I've been talking to Dan Spielman,
53:26I actually had to (mumbles) about this for years.
53:28We have another project in Honduras
53:30that Laura's involved with
53:31where we're gonna look at K cornice,
53:34I don't know whether we'll be able to do with this app.
53:35It depends on how much use we get.
53:39- [Jose] Thank you.
53:44- Other questions?
53:46- Yeah, I have a question.
53:49Hi, my name's Sam Burma, graduate in genetics.
53:51I'm interested in understanding a little bit more
53:52about the dispersion you're talking about
53:55with the equivalent reproductive,
53:57the effective reproductive rate of the virus,
53:59and I think I made it just missed a little bit there.
54:02But can you just go back and explain a little bit more
54:04about how this relates to the fact that
54:07there's a lower dispersion rate actually is worse?
54:11- Yeah.
54:12It's a wonderful, beautiful paper by Lloyd Smith
54:15at all in Nature 2005 which is it's just,
54:19it's like one of those papers, you read it,
54:21and you get the point that you read and you get
54:23"There's more subtlety here," and then you read it again,
54:24"There's more subtlety here."
54:26It's a wonderful paper.
54:28The gist is, if you think about it,
54:30and also just incidentally, as an intellectual point,
54:32there's something happening in the sciences.
54:34This is an old issue in the sciences
54:36between lumpers and splitters.
54:37No Darwin talks about this, lumpers and splitters.
54:41There are scientists who are concerned about
54:42getting the sense of a thing
54:44and like the average and they're scientists
54:46who are interested in variants
54:48like what's, you know, what are exceptions?
54:50How do things spread out?
54:51And so much of statistics and social sciences
54:54in the last 50 years has been focused
54:56on measures of central tendency, why?
54:59Because we invented regression models and statistical tools
55:02that were easier for us to say that,
55:04whereas variance is also so important.
55:06And so there's a lot across the social scientists
55:08and people that are becoming much more interested
55:10in variants and in variation.
55:12And so this is another example of that,
55:14where for a long time, people were, you know,
55:16trying to estimate the R naught.
55:17And Lloyd Smith comes along at all,
55:19and says, "Wait a minute, the variance is also important."
55:23Why does it matter?
55:24So if everyone in the population
55:26has an R naught of two,
55:29then every single time one person goes from one place
55:31to another, then we'll restart the epidemic.
55:36But if there's variation and some people
55:38or most people have an R naught of zero,
55:40they cannot give it to anyone.
55:42And one out of 100 people can give it to a lot of people,
55:46the epidemic is more likely to extinguish
55:48'cause 99 out of 100 times when one person
55:51in the latter example goes somewhere,
55:53the epidemic stops.
55:54Only if that's superspreader,
55:56the person with a capacity to be a superspreader
55:59goes does it get started.
56:00And if it's something intrinsic to the germ,
56:03then in the next cycle,
56:04most of the transmissions will also be zero.
56:08And so this dispersion, which they in that paper,
56:11they quantified across pathogens
56:13seems to fit with the ability of the pathogen
56:17to get instantiated.
56:22- Thank you so much.
56:25And I also shared that paper in the chat for him.
56:26- Yeah, and also, just so you know,
56:28it's estimated that with with SARS-2
56:31what we're currently facing,
56:33is you need four importations to get one transmission,
56:38one community transmission.
56:40So in fact, in Seattle patient zero,
56:43did not infect anybody else.
56:45The first case that arrived in the middle of January,
56:48when they did contact tracing elaborately,
56:50and now genetic studies,
56:51he didn't infect anyone else.
56:52It was a dead end.
56:54You needed subsequent importations
56:55before the epidemic took root in Seattle.
56:59Same in China by the way.
57:04Other questions?
57:07It's very weird Zoom, 'cause I can't see you.
57:09I don't know if I'm boring you,
57:11I have no way of judging how I'm coming across.
57:14You know, I could be coming across as very aggressive,
57:17which of course not my intention.
57:22- I mean, if no one else has questions.
57:23I was also wondering what are the factors
57:25that influence our ability to detect the difference
57:27between the latency and the infectivity period
57:29'cause I remember at the early days in the US,
57:33CDC was still saying they didn't think
57:34that asymptomatic spread was a major factor for COVID-19.
57:39But now it seems like...
57:42- I haven't dug deep in what the CDC
57:44was saying but they knew
57:47there was a symptomatic transmission
57:48certainly by the end of January.
57:51There's some CDC announcements
57:54that I haven't traced it all the way back
57:56but for sure, by the end of January,
57:57they were already saying this
57:59and I think earlier in January too.
58:00There was some confusion in December still,
58:03but certainly by January, people knew.
58:11The genetics of human susceptibility
58:13are very interesting, by the way.
58:15I think we're gonna find that a small part
58:18of the geographic variation will relate
58:20to the genetics of the pathogen.
58:23Right now, there's no evidence that yet that some strains
58:27of the pathogen are more deadly
58:29or more infectious than other streams,
58:30a lot of interest in this topic right now.
58:33We probably will find some of that.
58:35And there's also some small evidence
58:38that human genes, that some people may be more of you
58:41than others, because of, you know,
58:43their various variants still to be described.
58:47So I think that's gonna be
58:48and I think that'll be a small part
58:50of explaining that geographic variation, not a big part.
58:53- Nicholas, Luke here in the chat box has a question.
58:57He's interested in your thoughts about transmission settings
59:01like nursing homes, meatpacking.
59:03Is it network structures susceptibility
59:06excetera driving transmission?
59:08- Well, I think the meatpacking,
59:10the story for the meat packers was aerosolization
59:13of the pathogen in a cold environment
59:15and high density and I have a long Twitter thread
59:18on this if anyone is interested on why meatpacking
59:22and it's a worldwide phenomenon,
59:24it's not just a phenomenon in the United States.
59:26So I think the explanation by the Secretary of Health,
59:31the Secretary of Health in our country,
59:33that it had to do with the living arrangements
59:35of the immigrants working in these factories,
59:38is not correct.
59:40There are other factories
59:41with other industries with similar immigrant populations
59:44and similar living conditions
59:45and they didn't have the operates.
59:46I think it's to do with the temperature in the environment
59:50where these people work.
59:51It's refrigerated, the very tight packing of the workers
59:54and up aerosolization using saws and other equipment
59:58that create aerosols
59:59that are in the turbulent wind conditions
01:00:01in these factories.
01:00:04The nursing homes is different,
01:00:05I think it's a very customary health care situation
01:00:08where you have very vulnerable elderly people
01:00:10and health care workers that are up close
01:00:12and intimate working with people.
01:00:14So once the epidemic takes root, you know,
01:00:16you get a very rapid spread.
01:00:18I think nursing homes are more like prisons, actually,
01:00:20in terms of their epidemiology,
01:00:23meatpacking is different.
01:00:27You know, ships,
01:00:28ships are close quarters as well.
01:00:30So you have young people in ships,
01:00:32you know, in the US's Theodore Roosevelt,
01:00:35you know, you have young healthy sailors,
01:00:36although one died already,
01:00:38we should acknowledge from the disease,
01:00:41but they're very tight.
01:00:42People are living in bunks,
01:00:43you know, one on top of each other in that situation.
01:00:50- Nicholas I have a question.
01:00:52So we're used to think that
01:00:53these social distancing measure, these lockdowns,
01:00:56what they're really doing
01:00:57is reducing the number of susceptible people
01:01:00in the population
01:01:01so that essentially we plug in this number,
01:01:04this lower number in our serial models
01:01:06and predict the spread.
01:01:08But, I think what in addition to reducing the density
01:01:12of the network, what they're also doing
01:01:14is reshaping the network
01:01:16because essentially the structure of the network
01:01:18because people don't go to school anymore.
01:01:20People don't gather in bars.
01:01:22So what do you think of this?
01:01:24Do you think we should make this information
01:01:26will be useful to include in our model is right?
01:01:30- Yes I do think that.
01:01:31And just if there's a little detail there,
01:01:33which was seen in China and the United States
01:01:36is, ironically household transmission,
01:01:39cases of household transmission
01:01:40typically are more severe than cases
01:01:42of community acquired transmission
01:01:44because of the viral inoculum idea.
01:01:46If I get the pathogen from my wife,
01:01:49I'm gonna get a sicker than if I get the same pathogen
01:01:53from riding in the subway,
01:01:55because I'm writing something
01:01:56I might get a low viral inoculum
01:01:58whereas if I get it from my wife,
01:01:59I'm gonna get a serious case.
01:02:00You know, I kiss my wife, for example.
01:02:04So intra-household transmission is often more severe
01:02:07and more efficacious than out-of-household transmission.
01:02:11So the dynamics will change in quite complicated ways,
01:02:14just like like you're alluding to,
01:02:16and I'm sure other groups are looking at this.
01:02:19It's not something we're actively doing.
01:02:22But again, if you are interested Laura,
01:02:23I'm always eager to work with you,
01:02:24I love working with you.
01:02:27- All right after this (laughing).
01:02:30Now, do you do you wanna take one more question?
01:02:33- I'm happy to take one more
01:02:34but pick one I wanna go so, let's do one more then stop.
01:02:38Anyone else have something?
01:02:42I'm looking around here
01:02:43and all the things I need to monitor.
01:02:50Alright, thank you all very much.
01:02:53- Thank you.
01:02:54Yeah, Luke says thank you for a very interesting point
01:02:58as we think about contact tracing.
01:03:00Okay.
01:03:02- And someone else asked about school reopening.
01:03:04That's a long topic to keep people at the last minute.
01:03:07My wife had a nice piece in The Atlantic about this,
01:03:10if you're interested,
01:03:11I think schools are gonna reopen,
01:03:13I think they need to reopen.
01:03:15They're going to reopen only 'cause we have no choice
01:03:17but it would be better from pandemic point of view
01:03:19if they did not.
01:03:21I think that if they're gonna reopen,
01:03:23a lot of procedures are gonna have to be put in place
01:03:25at Yale, at nursery schools, at elementary schools.
01:03:27It'll be different from place to place.
01:03:30And I think that there will be a second wave.
01:03:35So I think the schools will close again,
01:03:37just what I suspect is gonna happen in October, November.
01:03:43Thank you all very much.
01:03:44- Thank you Nicholas.
01:03:45Thank you all for joining.
01:03:47Bye bye.