Interdisciplinary Quantitative Research on COVID-19

Transcript

• 00:00I would now like to introduce our
• 00:03next Speaker, Doctor Saad. Omer.
• 00:05Doctor Omer has conducted studies
• 00:07in the United States, Guatemala,
• 00:10Kenya, Uganda at the opia,
• 00:12India, Pakistan, Bangladesh,
• 00:13South Africa and Australia.
• 00:15Doctor Omers research profile
• 00:16includes Epidemiology,
• 00:17a respiratory viruses such as influenza,
• 00:20RSV, and more recently COVID-19.
• 00:22He is a director of the Yale Institute
• 00:25for global health and associate Dean
• 00:28of Global Health Research Doctor Omer.
• 00:30Thank you for being here.
• 00:33It's my pleasure to present
• 00:35and I will share my slides.
• 00:42I'm assuming everyone can see my slides.
• 00:45Correct Fatma Yes Yeah.
• 00:49So it's my pleasure to present
• 00:52some of our work in on this
• 00:55topic on the topic of COVID-19.
• 00:57Broadly, I will focus on our group in
• 01:00both Yell Institute for Global Health
• 01:03and my more immediate research group
• 01:06has been focusing on a bunch of things,
• 01:09but this presentation will focus
• 01:11on somewhat interdisciplinary,
• 01:12quantitative research on code 19.
• 01:15But before I start,
• 01:17I just want to step back and talk about,
• 01:20you know, when there is an
• 01:23outbreak when there is a so called
• 01:25once in a century of event.
• 01:28Educational institutions and
• 01:29research institutions should
• 01:30think about their role actively.
• 01:32We're not Jackson Labs.
• 01:34We're not scripts who have their
• 01:36own position in the ecosystem
• 01:38of discovery and deployment.
• 01:40We are not the CDC.
• 01:42We're not a health Department.
• 01:44We are an educational institution
• 01:47and educational institutions have
• 01:49to be responsive both in terms
• 01:51of their research and some of
• 01:54the other activities to emerging.
• 01:56Issue so one way of looking at what
• 01:59educational entities do is the Inter.
• 02:01Logically to say that the appropriate
• 02:03Ness of our actions and our responses
• 02:05to public health emergencies are
• 02:07judge irrespective of their impact.
• 02:09I don't think that's that's
• 02:11an important thing.
• 02:12Part of a University,
• 02:13so you know we should ask why someone
• 02:16is looking at zebrafish biology?
• 02:18Because it serves as well that if you
• 02:20don't ask those questions earlier on,
• 02:23we benefit from that kind of free
• 02:25thinking and. Go taking where going.
• 02:28Whether our curiosity takes us,
• 02:30but in a pandemic we have to
• 02:34be decidedly consequentialist.
• 02:35We have to say Watt,
• 02:37the time we are spending the things
• 02:40we're doing and the research
• 02:43questions we're going after.
• 02:45How does that contribute to
• 02:47our response to this?
• 02:49This event I would say of
• 02:51unimaginable proportions.
• 02:52It is not unimaginable. So a lot of people.
• 02:55Part of it talked about.
• 02:57It worked on it.
• 02:59So I will go through a series of questions,
• 03:03so I'll take the so called Socratic
• 03:05approach to discussing what
• 03:07we have been doing.
• 03:08So the first question is what was
• 03:10the whole country in the world shut
• 03:13down and the immediate question
• 03:15was even in March when this was
• 03:17the started in early March in most
• 03:20a lot of places,
• 03:21or actually mid March 2nd week of March,
• 03:24is when there was imposition
• 03:26of these lockdowns or social
• 03:28distancing measures and then bye.
• 03:30Date March the question was was this?
• 03:32What are these policies working?
• 03:34So we did two projects around there.
• 03:37The first of all we contacted the CEO
• 03:40of one of the apps that is out there
• 03:43and the reason why we went after this app.
• 03:47It's called the mobility index.
• 03:49It's called the city mapper app because
• 03:51it combines both the IT combines
• 03:53the the the trips taken through
• 03:55public transportation and private
• 03:57transportation and trip planning etc.
• 04:00So they have.
• 04:01Calibrated this mobility
• 04:02index of 41243 cities.
• 04:03Depending on the type of
• 04:05data that you're looking at,
• 04:07and then we got those data.
• 04:09The other one was look at cell phone
• 04:12data that tells us with some minute
• 04:14detail where people are spending
• 04:16time and there are more advanced
• 04:19users of data that we're working on.
• 04:21So the second part of the work,
• 04:24the cellphone based data, came out
• 04:26of collaborations with Eli furniture.
• 04:28I'll pause here for a second, so
• 04:30the approach the consequentialist approach.
• 04:32Tells us that you don't start
• 04:34with the discipline.
• 04:35You start with a question and work backwards,
• 04:38and so I hope these snapshots and
• 04:40examples will convey that if you
• 04:43start with a question you don't
• 04:44stay within your own research group,
• 04:47certainly,
• 04:47but also not in a single school or
• 04:50two schools or even an institution.
• 04:52Broadly speaking,
• 04:53you work with people who have the
• 04:55interest and something to offer
• 04:57and combine forces in producing
• 04:59some of these outcomes.
• 05:00So the first thing was
• 05:02actually more immediate.
• 05:03So this was blood by amine Malik.
• 05:05One of my post docs and we did a very
• 05:07quick analysis and we did statistical
• 05:09analysis as well as sort of.
• 05:11This is graphic representation
• 05:13to be went through a few models
• 05:15and we found that in April.
• 05:16So this is by cities and if you look
• 05:19at the trends that red dots are the
• 05:21ones where there wasn't an imposition.
• 05:24Of these orders,
• 05:25so the initial very early inside.
• 05:27So this was on March 28th that we did
• 05:30the first analysis and we found that
• 05:33there was actual anticipatory this
• 05:35decrease in mobility approximately
• 05:37by three point 4% based on the
• 05:40depending on the baseline compared
• 05:42to the baseline for a day.
• 05:45Even before these orders were
• 05:47implemented and it was almost
• 05:48universal across continents etc.
• 05:50With these data were available,
• 05:52however, there was an effect.
• 05:55Overall effect of mobility decrease
• 05:57after using this measure after social
• 06:01distancing policies were implemented.
• 06:04Then with this cell phone based
• 06:06data that I talked about,
• 06:08we were able to look at time spent at
• 06:11home in the US and we try it stress
• 06:14testing these data in terms of these
• 06:17trends which were apparent earlier on.
• 06:20But just what the interesting part was,
• 06:23whether we looked at New York or
• 06:26Poker Tellow, Idaho or Arkansas,
• 06:28or Atlanta or Vermont,
• 06:30this these are the counties we had
• 06:33some pretty interesting trends,
• 06:34so they worked at first of all the
• 06:37change in behavior is the increase
• 06:40in time spent in home preceded some
• 06:43of these policy actions and this
• 06:45dark line anchors this work on the
• 06:48policy action and the imposition.
• 06:51Uh,
• 06:51of these lockdowns and social
• 06:53distancing measures.
• 06:54But there was also an effect and
• 06:57impact of the number of cases
• 07:00in the community,
• 07:02but there was differential
• 07:04that was the difference between
• 07:06Metropolitan areas in rural areas.
• 07:08The rural areas where going for
• 07:11the local cases and Metropolitan
• 07:13areas where tracking the added
• 07:16effect was tracking and
• 07:18there was a bit of a crowding out effect.
• 07:21That we're tracking national cases.
• 07:25And so that's an interesting observation.
• 07:27And that's rational,
• 07:28and that that works through.
• 07:30The phenomenon of availability heuristic
• 07:33in psychology that we just the probability
• 07:36of events by how vividly they are
• 07:39covered and so availability heuristic
• 07:41was working and it's rational to say
• 07:44that Metropolitan areas were actually
• 07:46linked more than non Metropolitan area.
• 07:49So they were tracking national cases whereas.
• 07:52Rural areas and on Metropolitan areas
• 07:55were tracking local cases and that has
• 07:57implications on when we open up and long
• 08:00term strategies for social distancing.
• 08:03The second thing was that a lot of us
• 08:06working with populations and governments
• 08:08and policymakers outside the US as well
• 08:12as inside the US an the testing in the US
• 08:15has gone up substantially and will go up.
• 08:19This testing is load University
• 08:21but in low income countries.
• 08:23That those limitations are even more stark,
• 08:25and therefore the testing capacity
• 08:27is a little bit more long term,
• 08:29so you know you can always use a
• 08:31little bit of interdisciplinary
• 08:32work where you could look at,
• 08:34you know what what is happening and
• 08:36what are some of the approaches.
• 08:38But before I get to that,
• 08:40so you know, as I've been saying,
• 08:42there's a cottage industry of plans
• 08:44to open up America in the world.
• 08:46Ours was shared initially in late March,
• 08:48it came out as part of a peace in JAMA where,
• 08:52you know, we focused on.
• 08:53Extensive testing and contact tracing.
• 08:56Reduced reduction of household transmission.
• 08:58An additional treatment options will
• 09:00be said is going to help US Open up
• 09:04America safely, but now you know,
• 09:06you know this is.
• 09:08You know several program plans have since
• 09:11come out for the Rockefeller plan, the.
• 09:14Eh, I plan.
• 09:16This one is published, I think April 4th.
• 09:20So and so forth.
• 09:22So there are multiple plans
• 09:23that are out there.
• 09:24One thing that is shared is
• 09:26testing and contact tracing.
• 09:27And so how do we optimize it in one way?
• 09:30And I'm sure at Kaplan was done brilliant
• 09:33work on this in the more sort of.
• 09:35Local and domestic settings will
• 09:37talk about that in more detail.
• 09:39One strategy that we figured
• 09:41out would be too for government,
• 09:44specially in low income countries,
• 09:46is pool testing and so this was
• 09:48a little bit of massive working
• 09:51with an economist at the awesome
• 09:54charger is the director of.
• 09:56Center for International Development
• 09:57at Harvard and for Han is from magic
• 10:01is a economist based out of rice and
• 10:03the three of us combined forces and
• 10:06looked at said that there is lab based
• 10:09information that says that you cannot
• 10:11pull up to beyond a certain level,
• 10:13which is entirely appropriate.
• 10:15But can we do a bit of math to figure out
• 10:18who to test in low resource settings?
• 10:21So this is one example of that,
• 10:24and we found that even at.
• 10:26The at lower prevalence levels
• 10:28we can find a lot
• 10:30of efficiencies by pool testing.
• 10:32Even a full size is as low as 10,
• 10:36and as as soon as you increase them hire.
• 10:39That would be of pretty
• 10:41high level of efficiency.
• 10:43We also prioritize is based on
• 10:45some network modeling approach,
• 10:47which I'm not showing here in the interest
• 10:50of time that for low resource settings,
• 10:53looking at figuring out who has
• 10:55a who's a high degree node.
• 10:57In a network and prioritizing them actually
• 11:00gets you a lot of bang for your Buck,
• 11:03especially when the boxer shorts.
• 11:06And then what do you so?
• 11:09A lot of these interventions are as much
• 11:12about human behavior as much as they
• 11:14are about the behavior of the virus itself.
• 11:17So very earlier on, we focused on
• 11:20what does the US population think.
• 11:22So we did a population representative survey.
• 11:25Did an online survey online
• 11:27servers have gotten better.
• 11:29We paid a lot of attention
• 11:31of representative samples.
• 11:32So there. We not be.
• 11:35It won't be able to stratify them
• 11:36by a lot of characteristics,
• 11:39but overall it was fairly representative.
• 11:41We did robustness checks and
• 11:42this was done on February 8th,
• 11:452020,
• 11:45so before a lot of the features
• 11:47before doctor found,
• 11:48she became a household name and
• 11:50that would be relevant in the next
• 11:53slide we asked a bunch of questions,
• 11:55but I'm showing a bunch of.
• 11:57Pieces of data there on February 8th,
• 12:00they told us when we asked them
• 12:01who should be the the in charge of
• 12:04America is COVID-19 outbreak response
• 12:06and people said the CDC director,
• 12:08the director of NIH and then followed
• 12:10by the president and the Congress was,
• 12:12I think 1.5% what they were saying.
• 12:15They did not know the name of the
• 12:17direction of the NIH or the director of CDC,
• 12:20but where they were telling us
• 12:22that it should be a scientist.
• 12:24LED response.
• 12:25The other thing we asked them,
• 12:29whose advice would you?
• 12:31Trust the most and we found that it
• 12:33was that the top Dewar Healthcare
• 12:36Professional which is common to all
• 12:38sorts of other phenomenon phenomenon.
• 12:40A lot of our study, for example vaccines,
• 12:43but CDC ranks fairly low in terms
• 12:45of vaccine information source,
• 12:46but CDC and NIH were right up there and
• 12:49we have repeated these surveys, etc.
• 12:51We are analyzing it for this as well,
• 12:54but going back to some of the
• 12:56other questions.
• 12:57So how can we control the COVID-19 outbreak?
• 12:59Sorry for the typo in confined spaces and
• 13:02the reason why we are thinking about that.
• 13:04Is a lot of these micro level and
• 13:08a lot of the policy decisions as we
• 13:12open up our micro level decisions,
• 13:16companies,
• 13:16dorms,
• 13:17residential colleges in a certain
• 13:19300 over University for example,
• 13:22and cruise ships.
• 13:23So the first thing we started
• 13:26attacking with a postdoc amine
• 13:29and Sam Jenness who is a close
• 13:32collaborator based at Emory.
• 13:34Collaborating on some of the other
• 13:36stuff we said look the best model.
• 13:39The best source of data we have right now is
• 13:42from Diamond Princess Anne.
• 13:44If we could because there was a lot of
• 13:47reporting over reporting because there's
• 13:49a lot of other cases were not that high.
• 13:53We know a lot of details,
• 13:55so we sort of through with with
• 13:57another graduate students, etc.
• 13:59Katy Villa brand.
• 14:00We really did God really detailed data and
• 14:03we figured out that there were two networks.
• 14:06Uh, that were a small world networks
• 14:09that were existing side by side,
• 14:12the staff and the passengers and we said OK,
• 14:16but they are interacting so
• 14:18they have their own dynamics,
• 14:21their own degree of mixing etc and
• 14:24they are interacting with each other.
• 14:27And again from the beginning.
• 14:29The idea was that we work it out for
• 14:32Diamond Princess and then that serves as
• 14:35a template for other confined populations.
• 14:39So these are initial results.
• 14:41So we did we doing model,
• 14:43collaboration etc.
• 14:44And so with the initial thing
• 14:46is the first thing we evaluated
• 14:49and these are work in progress.
• 14:52That network, log downtime etc is.
• 14:54The earlier use stop the interaction
• 14:57between the two networks,
• 14:58the better control that you
• 15:01have on the outbreak.
• 15:03The other thing is,
• 15:04you know we are in a situation
• 15:06where going forward we're going
• 15:08to have a lot of decisions we made
• 15:12will be made on imperfect data and
• 15:14a lot of decisions will be made
• 15:17based on the idea that we have to
• 15:19have a grasp on reality for both
• 15:22symptomatic San asymptomatic.
• 15:24So how can we innovate in the
• 15:26context of imperfect data?
• 15:28So there are two lines of
• 15:30inquiry that we started.
• 15:32They are existing surveillance systems too.
• 15:34All to our surprise that CDC frankly wasn't
• 15:37utilizing it too to its full extent.
• 15:39When is influenza like in Leicester
• 15:41pin system and we went for flu
• 15:43negative influence?
• 15:44A negative I'll I there has been
• 15:46a change in healthcare behavior
• 15:47in terms of seeking care for
• 15:50respiratory illness,
• 15:51which is can be accounted for
• 15:52and we depend through that.
• 15:54And the other thing is sewage.
• 15:56Essentially sludge based surveillance,
• 15:57and for that again work.
• 15:59Starting with the problem and
• 16:01working backwards.
• 16:01So this this sort of these
• 16:03ideas came out with.
• 16:05Discussions with at Kaplan and then
• 16:08subsequently we reached out to Jordan
• 16:11patio what it came out of was that a lot
• 16:14of us were observing that very early around.
• 16:18There was evidence that at least viral
• 16:21name was being excreted in feces.
• 16:23Then the question was if it's
• 16:26being excreted in the feces,
• 16:28is it detectable in sewage or sludge,
• 16:31etc?
• 16:32Because there other attractive
• 16:33part interactive part was.
• 16:35That the world does this
• 16:37kind of surveillance,
• 16:38including and low income countries for polio.
• 16:41So there is this muscle memory
• 16:43and the ability to do this
• 16:45in this kind of a situation.
• 16:48So the first thing is that
• 16:50these data for flu influenza.
• 16:52I'll I influence,
• 16:53sorry flu negative influenza like illness.
• 16:55So we created standardized
• 16:57these scores and we found that
• 16:59this can be a leading indicator even
• 17:02adjusting for imperfections and testing
• 17:04Ware and sort of honing in places
• 17:06where we had some confidence. Uh.
• 17:10Can be a leading indicator for monitoring.
• 17:15Kobe 19 deaths and incidents.
• 17:16These are the data this should come
• 17:18out in a bio archive fairly quickly,
• 17:20and what we found a few things
• 17:22in from the sewage and such data.
• 17:24And then there was a.
• 17:26I would be remiss if I don't recognize the,
• 17:30uh, the dedication of not just Jordan
• 17:32Pescia from the school of engineering,
• 17:34but the medical students who
• 17:36collected specimens on a daily basis,
• 17:39and they're pretty trying circumstances.
• 17:40Obviously each is approved,
• 17:42etc from sewage medical students
• 17:44and graduate students as well
• 17:46as native grew by in his lab.
• 17:48Being a collaborator,
• 17:49the AG station folks being apart of it,
• 17:52this is a true collaborative project
• 17:54and what we found was that first of all.
• 17:58That the we can identify this RNA
• 18:02from sewage sludge that tracks well
• 18:06with the size of the with the.
• 18:11The trends in the outbreak and
• 18:13can serve as a leading indicator
• 18:15for cases by 5 to 7 days.
• 18:17There is other preliminary work
• 18:19that says that you can quantify
• 18:21the size of the outbreak,
• 18:23but I am a little bit more sceptical
• 18:26of that part right now because we need
• 18:28to calibrate that a little bit more.
• 18:31But as those indicators,
• 18:33so this has resulted already in the city,
• 18:35contacted us and Jordan will be providing
• 18:38weekly information to the city of New Haven.
• 18:41But also I've been working with
• 18:43the World Bank to expand this.
• 18:46Uh, and piloted in at least two countries.
• 18:50Countries in large Metropolitan areas,
• 18:51as well as smaller areas.
• 18:53So then what is the acceptance of vaccine?
• 18:56We're talking about vaccines,
• 18:57but vaccines if not taken,
• 18:59are pointless,
• 19:00and so we looked at that and we
• 19:02predicted that there is a lot of
• 19:05variability and we had sort of a
• 19:07predictive model based on demographics.
• 19:09So we calibrated this and said,
• 19:12can we create a risk map based
• 19:14on directly asked questions?
• 19:15But calibrating it again based on
• 19:18demographics to identify higher
• 19:19risk areas and that's.
• 19:20What some of this work?
• 19:22This is fairly recent,
• 19:23like a couple of days old,
• 19:24and this will be coming out fairly soon.
• 19:27The other thing is we don't want
• 19:29to be in a situation in vaccine
• 19:31distribution where governor's are
• 19:33fighting over vaccine like they
• 19:35did on some occasions on PE.
• 19:37So what we did was I worked with
• 19:39one of my keyboard mantis,
• 19:41who happens to be at the is a geography
• 19:44Republican geographer at UNC.
• 19:45Although matter and we looked at the
• 19:48what are the risk groups that we know?
• 19:50What are some of the other population
• 19:53characters characteristics we know
• 19:55and we came up with this map for.
• 19:57Distributing in placing vaccines
• 19:58and we have 5 version of it.
• 20:00Based on the policy goals.
• 20:02So if you have mortality reduction
• 20:04is your policy goals,
• 20:05this is very you situation,
• 20:07distribution centers etc and distribution
• 20:09based on the underlying and if you have
• 20:12it for transmission and so and so forth.
• 20:14The other thing is how do people
• 20:16mix during during an outside?
• 20:18Pandemics has been a question,
• 20:20so I've had a narrow one and a CDC grant
• 20:22funded on that area before the pandemic,
• 20:25but we repurposed it and so there are
• 20:27three 4 size the first original one with
• 20:30through STD CDC grant is workplace,
• 20:32the second is home,
• 20:34particularly low income countries,
• 20:35and then we have a supplement which is
• 20:37likely to get funded for health care
• 20:40workers that we're tracking them using
• 20:42RF ID technology, and the idea is.
• 20:44Uh, do, and with consent this has been
• 20:47there and we have an anthropologist in
• 20:49the team who has done the heavy lifting
• 20:52on acceptability and all sorts of stuff.
• 20:55That is that people are guessing
• 20:57now about the acceptability of other
• 21:00kinds of contact tracing and these
• 21:02kinds of these are the Maps that
• 21:04that we will be creating.
• 21:06This is from a previous study of Contacts
• 21:09between different kinds of people,
• 21:11not just 8 bass mixing.
• 21:13How can be facilitated vaccine development?
• 21:16There's all sorts of studies that
• 21:18are going on leveraging our health
• 21:20worker cohort that several people came
• 21:22together and establish to evaluate
• 21:25cordless of protection and severity.
• 21:27Advanced phase getting ready for
• 21:29advanced phase clinical trials
• 21:30and focusing on vaccine access.
• 21:32The last thing is that University
• 21:34of a unique responsibility to make
• 21:37sure lessons learned during this
• 21:39pandemic are remembered for centuries.
• 21:41So the first thing is public scholarship.
• 21:44This was. This is an op Ed.
• 21:46I wrote in the New York Times that came
• 21:49out right after the when the count was,
• 21:51I think one or two in the US and the title
• 21:54was is America ready for another outbreak?
• 21:57And the answer was no,
• 21:59but you can do a few things and I
• 22:01warned about not consequences of
• 22:03scientists not taking the lead.
• 22:05No false assurances,
• 22:06and it was very clear that if
• 22:08you give false assurances,
• 22:09there's a trust diversion it trust
• 22:11and then compliance will be low.
• 22:13And then scientific and public
• 22:15misinformation.
• 22:15Was the thread that a lot of us saw coming.
• 22:18And as you know, in the public discourse,
• 22:21a lot of that is happening yesterday.
• 22:23I think what the day before yesterday.
• 22:25There's another op Ed,
• 22:27that that was published,
• 22:28and I wrote highlighting.
• 22:29Look what is going to happen
• 22:31day after tomorrow.
• 22:32This is not the last big outbreak
• 22:34on unfortunately not even the
• 22:36last pandemic that will happen
• 22:37in this country in this world.
• 22:39And so the idea is to focus on
• 22:41CDC reform based on lessons now.
• 22:43And and and because CDC remains our
• 22:46best defense against Japan damage,
• 22:48but as an educational institution,
• 22:50it's our responsibility to make
• 22:52sure that we pass on this knowledge
• 22:55to our students an minties.
• 22:57So being Brown restart to a few of us to
• 23:01say that, can we create
• 23:03options for students elective,
• 23:05so we paired with myself,
• 23:07Doctor Sheila she noise from adults,
• 23:09ID and three students.
• 23:11Student volunteers came together
• 23:12with with having a course that.
• 23:15Worked on the higher terms of in
• 23:17the blooms taxonomy so that their
• 23:19education is not compromised in
• 23:21the middle of a pandemic and we
• 23:23were very encouraged in our initial
• 23:25evaluation approximately 86% says
• 23:27that the course achieved its stated
• 23:29aims and similar 87% of students
• 23:31said that it would recommend this
• 23:33course through the classmates
• 23:34because we obviously measured
• 23:36and then you know the efficacy.
• 23:38But the best way to pay back to
• 23:41this privilege of being part
• 23:42of this outbreak is to pay it.
• 23:45Forward and and I've had the privilege
• 23:47and a lot of us have had the privilege
• 23:49to invest in the next generation
• 23:51of interdisciplinary scientist.
• 23:53So they range from left to right
• 23:54of faculty member who's about to
• 23:56join my existing entities about to
• 23:58join Yale as an associate professor
• 24:00in Zaidi to medical students,
• 24:02post graduate students,
• 24:03and all of them,
• 24:04and so these are the ones only the
• 24:06select ones that have directly
• 24:08contributed to the work I have shown.
• 24:10And that's the most incredibly gratifying
• 24:12part of a lot of this work, so I'll.
• 24:16I said something.
• 24:18In a seminar that you know
• 24:19found that on it was surprising,
• 24:22unfortunately highlighted on the
• 24:23years full of Medicine website,
• 24:25the root page and and I'll repeat it,
• 24:28universities have a privileged
• 24:29position in civilization.
• 24:30We're guardians of knowledge
• 24:32for future generations.
• 24:33We are what we are learning now
• 24:35through science, through experience.
• 24:37Through policy we will pass on to our
• 24:39students are manatees and future scientists,
• 24:42so I'll end with the expression.
• 24:44No pressure, no pressure.
• 24:45My academic friends.
• 24:46This is a responsibility.
• 24:51Thank you very much.