Real-time Big Data Forecasting of the COVID-19 Outbreak

Transcript

• 00:00I would now like to
• 00:02introduce our next Speaker,
• 00:04Doctor Nicholas Christakis Doctor.
• 00:05Christakis is a sociologist,
• 00:07an physician who conducts
• 00:08research in the areas of social
• 00:10networks and biosocial science.
• 00:12His current research is
• 00:13mainly focused on two topics.
• 00:15First, the social mathematical,
• 00:17an biological rules governing,
• 00:18have social networks form,
• 00:20and 2nd the social and biological
• 00:22implications of how these networks
• 00:24operates to influence thoughts,
• 00:25feelings, and behaviors.
• 00:27Doctor Crystal Kiss.
• 00:28Thank you for being here.
• 00:31Thank you all.
• 00:32Of course I can't see any of
• 00:34you and there are 268 of you.
• 00:36I see.
• 00:36It's a very odd experience using
• 00:38zoom to do this and I lately
• 00:40have not been using slides in
• 00:42order to communicate in a way
• 00:43I think is more effective.
• 00:44So I'm going to try to cultivate
• 00:46some visual images in what I'm
• 00:48going to talk to you about.
• 00:50I'm going to start with just a
• 00:51couple of brief remarks or to set the
• 00:54stage about the coronavirus pandemic.
• 00:55I suspect everyone on this audience
• 00:57will know these numbers already.
• 00:58Then I'm going to talk a little bit
• 01:00about the issue of waves of pandemics.
• 01:02And then I'm going to tell you a little
• 01:06bit about some of the work in my lab,
• 01:09including an app that we just released
• 01:12a soft launch earlier this week
• 01:14called who nala HUNALA that we think
• 01:16can be quite helpful and it's quite
• 01:19different than all the other apps
• 01:21that are available at the moment.
• 01:23So as you all know,
• 01:25the are not for a pathogen is felt to
• 01:28be something intrinsic to the pathogen
• 01:31or as it relates to the host as well.
• 01:34Uh,
• 01:35this is the number of new cases
• 01:37that can arise from a prior case.
• 01:39In, uhm,
• 01:40you know fully susceptible population.
• 01:41That is to say,
• 01:42no individual is immune to a pathogen
• 01:44and the end host hasn't responded yet.
• 01:47We haven't taken any actions were
• 01:49not pulling apart and living in
• 01:51like Hermits for example were
• 01:52interacting normally and it is
• 01:54estimated and there was a recent
• 01:56meta analysis just released by the
• 01:58land set a couple of days ago or
• 02:00a couple of weeks ago that they
• 02:02are not for this condition is.
• 02:05Probably around 3,
• 02:05maybe 2.4 somewhere in the range
• 02:07in the high choose probably,
• 02:09and that's quite high.
• 02:11Actually that is a high are not.
• 02:13The seasonal flu has and are not of
• 02:16about 1.3 Ebola of about 1.5 to one point 9.
• 02:19And of course a chicken pox
• 02:21around 3:00 to 6:00.
• 02:22And of course measles is the
• 02:24champion which has one of the
• 02:26highest arnotts estimated of around
• 02:2818 new cases for each new case.
• 02:30This so called are not of course is
• 02:33different than what is called the RE.
• 02:36The affective reproductive rate,
• 02:37which is the number of new cases
• 02:40that arise in a kind of more
• 02:42steady state of the epidemic.
• 02:44For every old case.
• 02:45So some people are immune.
• 02:47People are beginning to
• 02:48take responsive action,
• 02:49for example, were beginning to
• 02:51engage in physical distancing,
• 02:52and this would be known as the
• 02:55affective reproductive rate and the
• 02:57ariav courses can fall as an epidemic
• 02:59proceeds because of what we do or how
• 03:02we've been affected by the pathogen.
• 03:04I just a different parameter,
• 03:06something known as the case fatality rate.
• 03:08That's the fraction of people
• 03:10who died conditional on coming
• 03:11to medical attention course.
• 03:13Whether you come to medical
• 03:14attention or not depends on what
• 03:16kind of health care system you have,
• 03:19what patients do when they get symptoms,
• 03:21and so on.
• 03:22And sometimes people estimate
• 03:23something instead known as the
• 03:25symptomatic case fatality rate.
• 03:26The SCF are,
• 03:27which is what fraction people die,
• 03:29conditional on developing a symptoms.
• 03:31We think this is still much
• 03:33more debated than they are not.
• 03:35We think this numbers between 0.5 and
• 03:371% or perhaps as low as zero point 3%.
• 03:41Now the case fatality rate for the
• 03:43seasonal flu is about zero point,
• 03:451% ignoring other features, just on average.
• 03:48About one out of 1000 people who
• 03:50get the seasonal flu will die.
• 03:53We think that this disease is between
• 03:555 and 10 times as bad as that and the
• 03:58Stars to the epidemic we're having
• 04:00right now is about a 10th as deadly
• 04:04as the SARS one epidemic from 2003.
• 04:06The case fatality rate for
• 04:08SARS of one was about 10%.
• 04:10That was a much deadlier.
• 04:12Condition and the 1918 influenza A
• 04:14pandemic had a case fatality rate.
• 04:17We think about four to 5%.
• 04:20So these two parameters there are
• 04:21not or the RE something about the
• 04:23transmissibility of the disease,
• 04:25and the fatality of the disease
• 04:27could be put on two axes,
• 04:29and you could plot every respiratory
• 04:31pandemic for the last 100 years
• 04:33on these two axes,
• 04:34and then you could see well, how?
• 04:37How does this pandemic compared
• 04:38to previous ones,
• 04:39and when you do this exercise,
• 04:41it's actually kind of alarming.
• 04:44Of the worst pandemic we've had
• 04:46in terms of how transmissible it
• 04:48was and how deadly it was is 1918
• 04:51in the upper right corner.
• 04:53The second worst we had was in 1957,
• 04:56which had sort of Intermediate
• 04:58Intermediate Lethality,
• 04:59an intermediate transmissibility,
• 05:00and this disease is probably slightly
• 05:02more transmissible and slightly
• 05:03more deadly than the 1957 pandemic,
• 05:05so it's getting up there.
• 05:07It's above the 1957 pandemic and
• 05:09not as bad as the 1918 pandemic.
• 05:12However, the point is, it's bad.
• 05:15This is bad and I think what we
• 05:17have to accept is that this moment
• 05:20in historical time.
• 05:21That we all happen to live in is
• 05:24a moment when a new species of
• 05:26pathogen has entered our species.
• 05:28There's a new germ out there that is
• 05:31is going to have its way with us.
• 05:34It's going to spread in our species
• 05:36and and affect us and it is bad and
• 05:39without action many people would
• 05:41have died even with the actions
• 05:43we have taken about.
• 05:45100,000 people have already died.
• 05:47Now as a nation and as different districts,
• 05:50we have taken different sorts of
• 05:52actions that people have been sort
• 05:54of locked down in various ways.
• 05:56We've closed our schools,
• 05:58we've pathetically done some contact
• 06:00tracing and some testing we have had
• 06:02work from home orders or stay at home
• 06:04orders in most states in the union,
• 06:06and the point of this,
• 06:08of course, was to flatten.
• 06:09The curve was to reduce the
• 06:11intensity at any given moment of
• 06:13the number of cases that we have.
• 06:15But every single respiratory pandemic.
• 06:17The last century has come in waves.
• 06:19All we have done by flattening the curve
• 06:21is we've not eradicated the pathogen.
• 06:23We just stopped the transmission.
• 06:25The germ is still out there.
• 06:26It will come back to China.
• 06:28It will come back to us.
• 06:30It's going to come back.
• 06:31All of the rest atory pandemics,
• 06:33even the mild ones of the last 100
• 06:35years have come back and typically
• 06:37they come back in the fall.
• 06:39And this has to do with a variety of things.
• 06:41It has to do,
• 06:43and they typically come back
• 06:44every fall for two or three years
• 06:46before they kind of damp down.
• 06:48Uh,
• 06:48and and eventually end and if time permits,
• 06:51we can talk a little bit
• 06:53about why pandemics end.
• 06:55And this has to do partly
• 06:57with human behavior.
• 06:58You know, when the fall comes,
• 07:00the students return to school,
• 07:02adults return to work.
• 07:04We move indoors.
• 07:05So are are dense interactions
• 07:06the proximity which we interact
• 07:08with other people increases,
• 07:10which enhances transmissibility.
• 07:11There may be some environmental factors
• 07:13which affect the pathogen heat or humidity,
• 07:16or our responsiveness to the pathogen.
• 07:18So we might do better in the sunny weather.
• 07:21Our bodies might be more able to resist
• 07:24the pathogen is not sunny weather.
• 07:27And so on.
• 07:28And ultimately,
• 07:29one of the factors of parameters
• 07:31that we could think about is it
• 07:33bikini ologists In addition to.
• 07:35Well, there are a number of parameters,
• 07:37but In addition to the transmissibility,
• 07:39the R and the lethality that
• 07:41case fatality ratio is something
• 07:42known as the attack rate,
• 07:44which is the fraction of people
• 07:46who actually get the disease in
• 07:48the end and in the end for this
• 07:50pathogen it'll probably be above 50%,
• 07:52maybe a bit higher if we overshoot
• 07:54in ways we can discuss.
• 07:56If if there's time now in the 1957 pandemic.
• 08:00Nationally,
• 08:00about 25% of people got the
• 08:02disease were infected,
• 08:03but in some hard hit areas
• 08:05it was as high as 40%.
• 08:07I got the disease now.
• 08:09Our best estimates of how many
• 08:11people have gotten it already
• 08:13in the United States is low.
• 08:15So for example,
• 08:16if you look at Sweden,
• 08:17they just released a quite good study.
• 08:20Sweden has had less severe sort of
• 08:22social or physical distancing than
• 08:24we've had about 4% of Swedes using a
• 08:26national Sero prevalence study just
• 08:28released this week have been infected.
• 08:30And they have been mixing
• 08:32more than we've been mixing.
• 08:33As you know, in New York it was about
• 08:3621% of New Yorkers in a rather good
• 08:38sort of representative sample of
• 08:40New Yorkers have become infected,
• 08:41and other studies around the United States
• 08:43of high quality that have been done
• 08:45show relatively low fractions of people
• 08:47have yet been exposed to the epidemic,
• 08:49so we have quite a way to go still.
• 08:54You know, before we before we reach the
• 08:57final attack rate that we're likely to get.
• 09:00So the disease is going to come back
• 09:03more people are going to get infected.
• 09:06Unfortunately, we're going to have
• 09:08more deaths with this condition.
• 09:10What can we do to predict the course of this?
• 09:13Oh, and I should say that I think
• 09:15that I've been flip flopping on my
• 09:17opinion as to the likelihood of
• 09:19successful development of a vaccine.
• 09:21So some days I'm optimistic some
• 09:23days I'm pessimistic.
• 09:24I'm not an expert on vaccines,
• 09:26but what I suspect is that no matter
• 09:28how fast we go on the vaccine,
• 09:30it's likely that plus or minus six months
• 09:33the vaccine will be widely available
• 09:34around the same time we otherwise
• 09:36would have gotten herd immunity anyway,
• 09:38so I don't think the vaccine
• 09:40is going to change the story.
• 09:42Very much unfortunate.
• 09:45How can we predict the course
• 09:47of this epidemic?
• 09:48Can we develop some tools that help us
• 09:50to confront how we might emerge from
• 09:52the lockdowns that we're currently
• 09:54engaged in and might anticipate
• 09:56the course of the epidemic in the
• 09:58fall when it comes back my love.
• 10:00Has been doing quite a few projects
• 10:02in this regard.
• 10:03We have a in the midst of developing
• 10:05New Haven wide sero prevalence study
• 10:07that will follow people longitudinally.
• 10:09Our work if we launch it,
• 10:11will have some different features
• 10:12than some of the other studies that
• 10:14have been done around the world.
• 10:16Some features that we think offer some
• 10:18interesting research opportunities,
• 10:19but I'm not going to talk about that today.
• 10:22Instead,
• 10:22I want to talk about two other things.
• 10:24One is a work that exploits the
• 10:26use of human movement.
• 10:28We had a paper just published in nature.
• 10:30About two weeks ago that took advantage
• 10:33of a big data that track the flow
• 10:35of people through Wuhan in China
• 10:38throughout the whole of China are
• 10:40up through sort of late February,
• 10:42so we had data on 11.5 million
• 10:44transits using phone data.
• 10:46So people paying the tower when
• 10:48they were in Wuhan,
• 10:49and then they relocated to another
• 10:51part of China and such data can be used
• 10:54to track the flow of human beings,
• 10:57even if you don't know who's
• 10:59infected or who is not.
• 11:01The movement of people, which,
• 11:03depending on data availability,
• 11:05could be tracked in basically in real time.
• 11:08Can be used.
• 11:09We showed using a certain model
• 11:11to predict the intensity,
• 11:13location and timing of the pandemic.
• 11:15So there are tools you can use that
• 11:17rely on other sorts of information.
• 11:20For example human movement,
• 11:21and it doesn't have to be
• 11:23fun data. It could be tolling
• 11:25data on highways as cars,
• 11:27a shift from place to place.
• 11:31Sort of other kinds of Geo.
• 11:34Location data, air travel data, and so forth.
• 11:38So human movement can be used.
• 11:40Another kind of thing that can be
• 11:42done is is using searches and probably
• 11:45many of you are remember the so called
• 11:48Google flu trends that was proposed.
• 11:50You know 10 or 15 years ago.
• 11:52Now the idea there was the following idea.
• 11:55So right now what the CDC does or
• 11:58other monitoring agencies do and what?
• 12:01What doctor Weinberger's talk just
• 12:03talked about as well is you wait
• 12:05in a central location for data to
• 12:08accumulate and be reported to you.
• 12:09For example,
• 12:10testing data for people doing influenza
• 12:12testing or people showing up in an
• 12:14emergency room or death counts for example.
• 12:16And what that means is that you know
• 12:19some period of time distant from
• 12:21now two to three weeks from now.
• 12:23You might know where the epidemic is today.
• 12:26Well, that's frustrating because
• 12:27you're always behind the epidemic.
• 12:29You can never get out ahead of it.
• 12:32Well, Google flu trends was an idea.
• 12:34That's it.
• 12:35Well,
• 12:35maybe we can use something about
• 12:36people's behavior today like there
• 12:38searching behavior for flu symptoms.
• 12:39For example.
• 12:40Maybe that can tell us where the
• 12:42epidemic is today and their first
• 12:43paper by Larry brilliant group
• 12:45showed that that could be affected.
• 12:46Then there was a whole literature that
• 12:48emerged that sort of debunk that and said,
• 12:50well, no,
• 12:51there it won't be effective and so on.
• 12:53But that's an illustration of a set
• 12:55of tools like the movement of data
• 12:57that I just described you from.
• 12:58The other project we had done.
• 13:00It's an illustration of a set of tools that.
• 13:03Allow you to a survey or note
• 13:05where is the epidemic today based
• 13:08on what I'm seeing today?
• 13:10But we have another idea that I'm about
• 13:12to tell you about that allows you to tell
• 13:15where the epidemic will be in the future.
• 13:18So it's not just rapid notification,
• 13:20it's advanced warning of the epidemic.
• 13:22How does this work?
• 13:24Well, imagine you're the network of people.
• 13:26There may be many of you can
• 13:28cultivate in your mind's eye,
• 13:29a kind of image of such a network.
• 13:31Since I'm doing this talk without slides,
• 13:33I have to kind of try to do that.
• 13:36Their little dots that are people
• 13:37in lines that connect the people.
• 13:39Many of you have seen these images and you
• 13:42have this sense that there's a middle of it,
• 13:44which is a very densely
• 13:45interconnected group of people.
• 13:47And then it feathers out
• 13:48to the social periphery,
• 13:49where there are people who,
• 13:51let's say,
• 13:51only have very few friends and whose
• 13:53friends have very few friends.
• 13:55So in the middle of the network you
• 13:57have people that are very popular and
• 13:59whose friends are very popular and as
• 14:01you get to the edge of the network,
• 14:02you don't have those qualities.
• 14:04That sense of centrality in the
• 14:05network can be quantified in a
• 14:07variety of mathematical ways.
• 14:08In fact, the mathematics of that lies
• 14:10at the core of how Google you know
• 14:12the billions of dollars that were
• 14:14made by the founding of Google using
• 14:15the so called page rank algorithm.
• 14:17So you can figure out what
• 14:19is the central website.
• 14:20Or you can figure out who's this
• 14:22central person in a network.
• 14:24Now imagine is such a network that
• 14:26a pathogen begins strikes someone
• 14:28at random in the population and
• 14:29then begins moving across the
• 14:31ties through the social graph.
• 14:33You should have the intuition that it
• 14:35should reach central people in the
• 14:37network sooner in the course of the epidemic,
• 14:40popular people should be more
• 14:42likely to get infected.
• 14:44And popular people should get
• 14:45infected sooner in the course of
• 14:47the epidemic than unpopular people.
• 14:49That means if we can identify this,
• 14:51incidentally,
• 14:52is the same reason that popular
• 14:53people get better stock tips
• 14:55or more information sooner.
• 14:56'cause if information flows
• 14:57through the network,
• 14:58there are more central in the network
• 15:00they can acquire this knowledge just
• 15:02like they acquired germs sooner
• 15:03in the course of the epidemic.
• 15:05Actually,
• 15:05there's a side light on some work
• 15:07we've done in the lab on the
• 15:09evolutionary biology of friendship,
• 15:11where we argue that the spread
• 15:12of germs is the price we pay
• 15:14for the spread of information.
• 15:16That's a whole other topic anyway,
• 15:18so central people can be like
• 15:20Canaries in a coal mine.
• 15:22If we can find them and monitor them,
• 15:24they will tell us those people
• 15:26should get the epidemic should
• 15:28strike them earlier in the course.
• 15:29Then it strikes a random person,
• 15:31so identifying such people and
• 15:33monitoring them gives us a tool to
• 15:35forecast the future course of the epidemic.
• 15:37My lab about 10 years ago for the H1N1
• 15:40Pandemic showed that this was possible.
• 15:42It could be done,
• 15:44and now we've developed new tools
• 15:45in combination with a mean car.
• 15:47Posse's group in the at Yale
• 15:49Electrical Engineering using
• 15:50certain machine learning tricks,
• 15:52which I'll describe in just a moment.
• 15:54That allow us to deploy these
• 15:56ideas in the form of an app that is
• 15:58sort of like ways for coronavirus,
• 16:00where everyone anonymously and
• 16:02privately contributes a little information.
• 16:03This information is aggregated
• 16:04and then fed back to the users,
• 16:06just like when you use when you drive
• 16:09on the highway and you use ways,
• 16:11you report that there's a traffic
• 16:13accident or that there's a traffic jam,
• 16:15and then this informs people that
• 16:16are behind you on the highway and
• 16:18gives them something of value.
• 16:20You get something of value and
• 16:22you share with others.
• 16:23It's like a crowdsourced way.
• 16:25Of tracking traffic,
• 16:26but we have like ways for coronavirus.
• 16:28So for example,
• 16:28when we saw all the politicians
• 16:30and celebrities that were getting
• 16:32that were in the news with getting
• 16:34sick from coronavirus early on,
• 16:36it was not just that they were
• 16:38rich and famous,
• 16:39so they were able to get tests and
• 16:41people cared what happened to them.
• 16:43They actually were getting sick more
• 16:45so Boris Johnson was out there shaking
• 16:47hands with all these other people.
• 16:49He was also spreading the germs exactly,
• 16:51which was irresponsible or Tom Hanks
• 16:53and his wife are all of these people.
• 16:55There are more connected
• 16:57so they get stricken.
• 16:58Earlier they were Canaries in a coal
• 17:00mine so our new app which were just
• 17:03released this past week on Monday
• 17:05were doing a soft launch this week.
• 17:08If you would like to use it
• 17:10you can go to who nala HUNAL,
• 17:13a.yale.edu and download it.
• 17:14We are asking that you not
• 17:16broadly advertised it.
• 17:17You can invite your friends but
• 17:19please don't broadly advertise it yet.
• 17:21We're still debugging it.
• 17:23If you find any bugs please email me or.
• 17:26Let us know and then we will.
• 17:28We're working on it and then next
• 17:30week we're going to do a kind of a
• 17:32big goof and try to get a lot of
• 17:35attention and try to get if we can.
• 17:37Hundreds of thousands of users.
• 17:38The more people that use the app,
• 17:40the better it can monitor what's happening
• 17:42in terms of the flu in your area.
• 17:44The app when you upload it,
• 17:46it only takes a on the first time you use it.
• 17:49You tell us some basic information
• 17:51about yourself and then.
• 17:53Every day you are pinned.
• 17:54If you, uh, if nothing is happening,
• 17:56you've had no symptoms.
• 17:58You haven't seen the doctor.
• 17:59You say no, no,
• 18:01you're done.
• 18:01If something is happening,
• 18:03you have some symptoms,
• 18:04or you've seen the doctor or you
• 18:06been out and about in some way.
• 18:08You might take a minute to answer
• 18:10and then you immediately get
• 18:11feedback in the form of your risk.
• 18:13Your told how much respiratory diseases
• 18:15there where you live based on a
• 18:17machine learning algorithm that takes
• 18:19advantage of lots of information not
• 18:21only from the CDC and other sources,
• 18:22but from our users.
• 18:24And then you're also told your
• 18:25individual risk based on where
• 18:27you are in the social network.
• 18:29For example, if your friends,
• 18:30friends,
• 18:30friends have the flu three weeks ago.
• 18:33This means your risk is different
• 18:35than some of his friends, friends,
• 18:37friends did not have the flu three weeks ago,
• 18:40or if your friends friends are very popular.
• 18:42Your risk is different than if my
• 18:45friends friends are not very popular.
• 18:47This information,
• 18:47which we validated mathematically.
• 18:49Another work is then combined and
• 18:51processed and fed back to you and
• 18:53you can monitor your risk everyday.
• 18:55Like I said,
• 18:56it's anonymous and private and it is
• 18:58like ways for respiratory disease.
• 19:01Now and we and and we are also
• 19:03building from this a dashboard.
• 19:05So imagine that the state police in
• 19:07a in a state wanted to know which
• 19:10parts of the highway are dangerous.
• 19:12In principle they could take a years
• 19:14worth of reports or months worth
• 19:16of reports by citizens traveling
• 19:18on the highways,
• 19:19saying where are there traffic
• 19:21accidents and they could say,
• 19:22Oh my goodness,
• 19:23this part of the highway is very dangerous.
• 19:26Maybe we should put a redesign.
• 19:28That part of the highlight.
• 19:30So this is this.
• 19:31Our app will work the same way.
• 19:33We're building a dashboard
• 19:34that could be used,
• 19:36for instance by people running a
• 19:37hospital system that want to monitor
• 19:39what's happening in their area,
• 19:41or that could be used to to detect
• 19:43where is are the hot spots,
• 19:45and to see it's coming.
• 19:46We're seeing a spike in people
• 19:48who are central in the network
• 19:50and not a spike in average.
• 19:52People that difference between those things?
• 19:54The difference between the at risk
• 19:55people and the less at risk people when
• 19:58there's divergence in those curves,
• 19:59that's a harbinger.
• 20:00But the epidemic is going to spike
• 20:03that the 2nd wave has begun,
• 20:04for example,
• 20:05or that the lockdowns are beginning
• 20:07to foster a spread of the pathogen,
• 20:09and so of course the individuals
• 20:11who get this information can act
• 20:13accordingly on their own benefit,
• 20:14but collective decision makers can
• 20:16also now have some vision into
• 20:18what's happening in the system.
• 20:21And I'll just stop.
• 20:22I have two more minutes.
• 20:24I'll shut up what I want to say is.
• 20:28That to my eye,
• 20:29there is no escape from this pathogen.
• 20:32It will become endemic among us.
• 20:34There are to our knowledge 7
• 20:37coronavirus species that that afflict
• 20:39us for that cause the common cold.
• 20:41I think that those pathogens that
• 20:44cause the common cold right now,
• 20:46the Corona viruses are probably distant
• 20:49echoes of previous introduction of
• 20:51coronavirus pandemics into our species.
• 20:52In the distant past.
• 20:54I think what happens is these pathogens
• 20:57tend to mutate to become milder.
• 20:59Remember from the point of view of the
• 21:02pathogen, it doesn't want to kill us.
• 21:04It's better if it doesn't kill us.
• 21:06So if we if we die too fast
• 21:08we don't spread it.
• 21:10So as time goes by,
• 21:11in general,
• 21:12pathogens mutate to become milder,
• 21:13but unfortunately and then there
• 21:15are two other coronaviruses,
• 21:16the SARS 1 from 2003 and Murs,
• 21:18the Middle Eastern respiratory syndrome,
• 21:19which has a very low are not,
• 21:22which is one of the reasons
• 21:23this has not become pandemic and
• 21:25then the one we're in right now,
• 21:27which unfortunately is awful for us and.
• 21:29It will spread and it will
• 21:31kill many of us I think is the
• 21:34sad truth until such time is.
• 21:36Either we invent an effective vaccine
• 21:38or we get herd immunity so it will
• 21:41become endemic and we have to use
• 21:43the best tools that we have to
• 21:45cope with its existence among us.
• 21:47Thank you.
• 21:52Thank you very much doctor Chris Nagus.