# Learning from COVID-19 Data on Transmission, Health Outcomes, Interventions and Vaccination

February 24, 2021## Information

Xihong Lin, PhD

Professor, Department of Biostatistics

Harvard T.H. Chan School of Public Health

Tuesday, February 23, 2021

ID6223

To CiteDCA Citation Guide

- 00:03So, so first it is my honor
- 00:05to today to welcome Doctor.
- 00:07She only as our summer speaker,
- 00:10Xihong Lin is a professor from Harvard,
- 00:13jointly appointed by both as deaths.
- 00:16Annabelle Slash Department and she has
- 00:18received broad recognition and many
- 00:21awards for her great contribution to the
- 00:23field and her research has covered so
- 00:26many different topics ranging from Mr
- 00:28Logical work including hypothesis testing,
- 00:30had dimension statistics
- 00:31and color inference tools,
- 00:33data applications.
- 00:34And the computational statistics
- 00:36such as Statical Genetics.
- 00:38Scalable statical inference
- 00:39as well as applications with
- 00:41epidemiological and health data.
- 00:43So today she will share with us
- 00:46her work on analyzing large scale
- 00:49coordinating databases from both
- 00:50China and the US and provide several
- 00:53takeaways and discuss priorities.
- 00:55It's Insp endemic,
- 00:57so I will not occupy everyones time for more.
- 01:01I will hand it over to see home from here.
- 01:05So should we start
- 01:07alright? Thank you laying so
- 01:08much and for inviting me and
- 01:10for the very nice introduction
- 01:12and so I will share my screen.
- 01:141st oh, I cannot share the screen.
- 01:16I have just met
- 01:18your Co host so you should
- 01:20be able to get it now.
- 01:25Can you can you share your
- 01:26screen I can do now? Thank you thanks.
- 01:33Alright, can you see my screen?
- 01:35Yes. Cool excellent alright?
- 01:38So I'll share with you some of the
- 01:42work and we have been doing last year
- 01:46on the Covic 19 so this is the data
- 01:50just downloaded earlier this this week.
- 01:52So as you can see that the right now
- 01:56they're over 110,000,000 cases in
- 01:59US and also 2.4 two point 4,000,000
- 02:02deaths were 110,000,000 cases and
- 02:052.4 million deaths worldwide.
- 02:07So if you look at the curve on the left,
- 02:10that is the case curve and for
- 02:12a few selective countries.
- 02:14So as you can see for both the UK and
- 02:16also United States and the number of
- 02:18cases had been going down in January,
- 02:21so that is a good sign.
- 02:23And also Israel as you know that
- 02:25Israel has been a really leader in
- 02:27a vaccination and so their cases
- 02:29have been going down as well.
- 02:31But if you look at Africa I think
- 02:33the number this particular country
- 02:35you can see the number of cases.
- 02:37Has been going up likely due to the new
- 02:40violence in Africa and on the right.
- 02:42That is the case curve with the desk curve.
- 02:45You can see the patterns pretty similar,
- 02:47especially you can see for this
- 02:49African country and the number
- 02:51of deaths has going up quickly,
- 02:53so that is really worrisome.
- 02:55So here's a talk outline,
- 02:57so I'll start talking about the covid
- 03:01transmission intervention and using the data,
- 03:03and then they turn talk about
- 03:06the USN word data.
- 03:08Then I'll talk about epidemiological
- 03:11characteristics of Mackovic.
- 03:12Then I'll talk about the 221
- 03:14playbooks and also the defining
- 03:16challenges in particular about
- 03:19the vaccine rollout and uptake.
- 03:21Our focus more on the uptake and
- 03:24also how can we do scalable testing?
- 03:28And in particular,
- 03:29I talk about this support design and
- 03:32we call the hypergraph we called hyper.
- 03:35That is based on the hypergraph
- 03:39factorization.
- 03:40So I started working on the Covic
- 03:4219 research mainly by coincidence
- 03:44and last February.
- 03:46So my post,
- 03:47our former postdoc column one
- 03:49he is currently is professor in
- 03:51school public Health at Wild on
- 03:53Science and Technology University,
- 03:56which is located in Wuhan,
- 03:58and so last February,
- 04:00'cause Wuhan was epicenter.
- 04:01So I wrote him a message asking
- 04:04how and he and his family were
- 04:07doing and he told me that.
- 04:10He and his colleagues were
- 04:12analyzing the Wuhan data,
- 04:13then at that time there was already one case,
- 04:17and in Seattle and one case in Boston.
- 04:20So I sense that the the the
- 04:23virus might spread and so.
- 04:25I decided to join them and working
- 04:28on analyzing the Wuhan data so we
- 04:31work the we work for several weeks
- 04:34and in February especially child
- 04:36when his Cody worked really hard and
- 04:39then finish this preprint and we post
- 04:43the preprint on March 6 and so then
- 04:46with the hope that the findings we
- 04:48want we want to share their findings
- 04:51with the US and also other country
- 04:54as soon as possible to help them.
- 04:58The other countries,
- 04:59and so I I did not expect this
- 05:02preprint attract lots of attention
- 05:04that you can see the number of
- 05:07abstract view and download,
- 05:09and also there lots of.
- 05:13A lot of free trade and also the news outlet
- 05:17coverage and also the policy documentation.
- 05:20And after I twittered this paper in
- 05:23the Twitter and so then this paper
- 05:26preprint cover too much stuff and so we
- 05:29decided to split the paper into split
- 05:32the preprint into two paper and one
- 05:35was published in JAMA and last April.
- 05:38Another one was published in
- 05:40Nature last summer and so this work
- 05:43on the JAMA paper was led by.
- 05:46I'm Pan and a towel,
- 05:48and here they both were addressed.
- 05:50pH alarm and country.
- 05:52Who is the Dean of school?
- 05:54Public health as well doing science
- 05:57and technology University and the
- 06:00Nature paper was led by Charlo.
- 06:03And then emerge after the preprint
- 06:05was posted them in Twitter.
- 06:07And then I got quite a few interview request.
- 06:10But as a station,
- 06:12my first reaction was turned
- 06:13down all the interviews,
- 06:15so I turned on all the interviews
- 06:18and in March, and then April,
- 06:20I decided that probably is not a good idea.
- 06:24It's good to talk with the media
- 06:26and then they can understand the
- 06:28scientific funding correctly.
- 06:30Then that will help,
- 06:31and the combat the Covic.
- 06:33And so I decided to accept the interviews
- 06:36and so you can see there's a quite
- 06:39few coverage of the findings and
- 06:41also the interviews and in the US medias.
- 06:44And also in the UK medium and
- 06:47also in scientific journals
- 06:48such as Nature and Science.
- 06:50And the one thing I I learned
- 06:53was that 'cause when as a faculty
- 06:56member in Academic Institute
- 06:58and we were not trained to.
- 07:01Speak to the media and so to the
- 07:04scientific communication on the two.
- 07:05The general public is very important,
- 07:08so it's important to have more
- 07:10training and in this area.
- 07:12Another thing I learned was is
- 07:14important that speak in simple
- 07:16language and without the jargon and
- 07:18so general public could understand.
- 07:21And then I testified the in the
- 07:23science and Technology University
- 07:25$0.10 at Technology Committee
- 07:27of UK Parliament on April 17.
- 07:30And so this sense technology Committee
- 07:33has about 8 to 10 Parliament
- 07:35members like a senators on it.
- 07:38And then they later on they also
- 07:41invited few other witnesses.
- 07:43And then they wrote a letter to the
- 07:46Prime Minister Johnson and they make
- 07:49a 10 recommendations in their letter.
- 07:52Under so so then I I was honored
- 07:55that several of my recommendation
- 07:58we included in their recommendation
- 08:01in the recommendation they made it
- 08:04to the Prime Prime Minister Johnson.
- 08:07And then, uh,
- 08:08this last month and as a one year
- 08:12anniversary of the colic outbreak and
- 08:15nature published their third editorial
- 08:18and focusing on the major findings,
- 08:21a key finding from ideology.
- 08:24And so we will.
- 08:26We will honor that our paper was
- 08:29featured on the in this editorial.
- 08:33So let me first talk about
- 08:36the Wuhan analysis.
- 08:37Then we talk about the US and other
- 08:39countries and the 1st I'll introduce
- 08:42this effective reproductive number.
- 08:44So this concept,
- 08:45our value is right now and everybody
- 08:48understand what that is and so
- 08:50that measures average number
- 08:52of infected people by one case.
- 08:54So you can see on the right if
- 08:57Artie called before that means one
- 08:59person could affect to four people.
- 09:02That means the virus spread.
- 09:04So therefore, to in order
- 09:05to control the pandemic,
- 09:06the army to be less than one.
- 09:10And so here's a woman analysis under.
- 09:13So we found the two major features of Kovik.
- 09:17The first feature is the virus
- 09:19is highly transmissible,
- 09:20and so the before January 23rd,
- 09:23and there was no intervention.
- 09:25As you can see,
- 09:27the number of cases had went up quickly,
- 09:30exponentially,
- 09:31and so the first case was reported
- 09:34on the in this orphan and seafood
- 09:37market and this seafood market
- 09:40was closed on January 1st.
- 09:42Under then on March on January 23rd,
- 09:45and that was a lunch of the lock down and so.
- 09:50As you can see that before the lock down
- 09:54the estimated RTT values is about 3.5,
- 09:57so that means each purse case could be.
- 10:00Like 3.5 people and so that is not good.
- 10:04So that means this is really the
- 10:06disease is very transmissible
- 10:08and then after the lock down and
- 10:11with the social distancing is
- 10:13really how the RT value dropped to
- 10:16a little over 1.2 and then.
- 10:19But it's not good enough and so then
- 10:22after February 2nd and that was a lunch
- 10:25of the centralized isolation and quarantine.
- 10:28So they basically the city build.
- 10:31Two new hospital field Hospital and
- 10:33also 16 field hospitals converted
- 10:36from the stadium and expectation
- 10:38Center and after that.
- 10:40And then you can see the number of
- 10:43cases drop down very quickly and the RT
- 10:46values and by March 8th was about .27
- 10:50and then the pandemic was controlled.
- 10:55And so to estimate those RT values.
- 10:58So basically the model we use is a person
- 11:01partial differential equation model
- 11:03and so this captured by the left here.
- 11:06Then you can see here is a from them.
- 11:10So here this is the symptom onset
- 11:12and between exposure and symptoms
- 11:14answer discord, incubation period.
- 11:16So generally this is about 5 days and then
- 11:19from the exposure to presymptomatic onsite.
- 11:22This so this part period
- 11:24called latent period.
- 11:25So that means a patient is infected
- 11:28and but the person is not transparent,
- 11:32doesn't transmit the disease, and so.
- 11:35Between the pre symptomatic period
- 11:372 symptomatic period so this period,
- 11:39the person,
- 11:40even though a person doesn't have
- 11:42a symptom and but very load,
- 11:44is high enough and then the person
- 11:48could become.
- 11:49Infectious and so this period is
- 11:51about two days between presymptomatic
- 11:53two symptomatic and between
- 11:55exposure to pre symptomatic period.
- 11:57This is about 3 days,
- 11:59and so we built in those components
- 12:02and in the model,
- 12:03and so we introduce this
- 12:05presymptomatic compartment,
- 12:06and also because at that time the testing
- 12:09case will not that widely available,
- 12:12and so therefore there were a lot
- 12:14of cases which were uncertain,
- 12:16and so therefore we built in this.
- 12:20Unstained components,
- 12:20the observed data here those are the
- 12:23observed data and then those are.
- 12:26Then we construct all those components and
- 12:28drew the deep partial differential equation.
- 12:31So this is isolation component.
- 12:34And so so here the we
- 12:36after you feed this model,
- 12:39one can construct the
- 12:41reproductive number or value.
- 12:43So basically,
- 12:43as I mentioned,
- 12:45the datas are here and those are the data.
- 12:48The rest part are basically coming
- 12:50from the partial differential
- 12:51equations and then we fit the
- 12:54partial differential equation,
- 12:55Apostle model and using MCMC and
- 12:57the Gulf Coast parameter estimate.
- 13:00And so here you can see that before
- 13:03the intervention on our journey,
- 13:05then if there was suppose there
- 13:08were no intervention,
- 13:09then this will the blue curve will
- 13:12be the predicted number of infected
- 13:15cases and so you can see that 170% of
- 13:18the Wuhan population were infected.
- 13:20That will reach the natural
- 13:22herd immunity and so now has a
- 13:2510 million population size.
- 13:27Then that means 7,000,000 people need.
- 13:30Need to be infected in order to
- 13:32reach the herd immunity and so
- 13:34this is not a good strategy and
- 13:37many old people will die and the
- 13:39sudden after supposed when user,
- 13:41social distancing and lock down
- 13:43and so you can see from Wuhan the
- 13:46number of cases still going up
- 13:48but not as fast as the without
- 13:50intervention but after the.
- 13:52Centralized isolation quality on top
- 13:54of the social distancing and the
- 13:56number of cases went down very quickly.
- 13:59And so this is what what they
- 14:02did in one hand.
- 14:04So if the case subject was tested
- 14:07positive and this person to patient
- 14:09was admitted to the field hospital,
- 14:12and so this is different from
- 14:14the US in the sense that in the
- 14:17last spring and US also build
- 14:20multiple field hospital,
- 14:22but they only admit as a severe diseases,
- 14:25so amount and diseases cases
- 14:27they were isolated at home.
- 14:29And so that these notes are isolation at
- 14:32home could still infect the family members.
- 14:35And but in Wuhan all the mild cases and
- 14:38were admitted to the field hospital,
- 14:41so they were monitored.
- 14:43If anybody became severe and the
- 14:45patient was transferred to the ICU,
- 14:47the regular hospital and for the
- 14:49people who had a symptom and but who?
- 14:53Because at that time there were
- 14:55not enough testing kits and so
- 14:57they were quitting the odds of
- 14:59hotels and will University dorm.
- 15:02If so, all the children and stay with
- 15:05the parents so the family were together.
- 15:07If anybody became a test positive
- 15:10and the person was transferred to
- 15:12the field hospital in two weeks,
- 15:15if a person was tested negative
- 15:17and the person went home,
- 15:19and similarly for close contact and they
- 15:22were quarantined as a hotel as well.
- 15:25I see how can I ask a very basic question.
- 15:28If I look at the Group One Group
- 15:30two Group One is confirmed.
- 15:32I guess if it means if you
- 15:34perform some kind of PCR test
- 15:36you are positive and Group 2 is
- 15:37with symptom but not confirmed.
- 15:39Can it go the other way?
- 15:41Can you first be confirmed
- 15:43but with no symptoms?
- 15:45Oh yeah, I think there were cases.
- 15:49Possible that would have had
- 15:51no symptoms but test positive,
- 15:53but at that time because they were
- 15:55not enough testing kids so many
- 15:58of the cases and who were able
- 16:00to be tested at the same time,
- 16:02so that's why there were lots
- 16:04of undetected cases.
- 16:05I said thanks.
- 16:09And so this strategy worked
- 16:11quite well in Wuhan.
- 16:13So in less than two months they
- 16:17reach 0 confirmed case and then by
- 16:20March 18 and by April 8 and after.
- 16:24To confirm the case for three weeks
- 16:26and then the city was reopened.
- 16:29So it's a whole thing only took
- 16:31in two months and so the first
- 16:34take home take home messages,
- 16:36the social distancing and centralized
- 16:38isolation quarantine were critical
- 16:39for controlling the outbreak,
- 16:41so using the social distancing
- 16:43alone that help but was not good
- 16:46enough so that helped make our
- 16:49reduce around 1:00 and but did
- 16:51not bend the curve and the reason
- 16:53is the there were lots of the.
- 16:57Community transmissions on the
- 16:59social distancing help block the
- 17:01community transmission that is
- 17:03between household transmission but
- 17:05within family transmission and cost.
- 17:07Place transmission with common
- 17:09and so so the social distance.
- 17:12Distancing does not help block that and so,
- 17:16especially with how many families are
- 17:19multi generation families and they
- 17:22live in apartment so it compared to
- 17:24US as even harder under to isolate.
- 17:28At home.
- 17:29And the idea that centralized isolation,
- 17:31creating to social distancing that help
- 17:34bend the curve and stop the pandemic.
- 17:38And so we validate those findings
- 17:40and in other countries last spring.
- 17:42So if you look at the curve on the left,
- 17:45that is a Italy data.
- 17:47So you can see that Italy also did
- 17:50the social distancing that help reduce
- 17:52the R and the R curve lingered around
- 17:55one for over a month and did not bend
- 17:58the curve but and also on the right
- 18:01that Germany data in the spring.
- 18:04The same thing under the curve did not band.
- 18:09And the second feature from
- 18:11analyzing the Wuhan data is the
- 18:13Covic is highly converged on the,
- 18:15so we estimated about 87% of
- 18:17the cases were undetected.
- 18:19So in other words,
- 18:20the detected cases was only
- 18:22the tip of the iceberg,
- 18:24and so you can see that on the on
- 18:28the left and the right bars are
- 18:31the detected cases and then the.
- 18:34Yellow bars also uncertain the cases,
- 18:36so we estimated on the we estimate
- 18:39entertainment rate and so we estimated
- 18:42about 87% of the cases were uncertain,
- 18:45and many of those cases were asymptomatic
- 18:47or mildly symptomatic cases.
- 18:49By adding the yellow and red that
- 18:52can give us a prevalence estimate
- 18:54that is about 2.5% in one hand,
- 18:57and so this is similar to the
- 18:59theological studies based on antibodies,
- 19:02and that was about 3%.
- 19:04And then US result very similar.
- 19:07the CDC did theological study last
- 19:10year and then the estimated about
- 19:13862 twenty times the number of
- 19:15cases were six to twenty time of
- 19:19the cases which were reported.
- 19:23And also those undetected tasted
- 19:25post a high risk of resurgence
- 19:27if one reopened too early,
- 19:28lifting the controls, and so we estimate
- 19:31the probability of the researchers.
- 19:32Think about this is the first
- 19:34day and one has a confirmed case.
- 19:37When has a confirmed case.
- 19:38It doesn't mean there is no
- 19:40case at all because there are
- 19:42still a lot of undetected cases.
- 19:45And suppose when we open in 14
- 19:47days by lifting all the control
- 19:49measures and the first strategy is
- 19:51after the first day observing the.
- 19:53Zero confirmed Case No matter
- 19:55whether the second day has the case
- 19:58or not and when to reopen info.
- 20:00This and the second strategy is one has
- 20:04a confirmed 0 case for 14 consecutive days.
- 20:08That basically means 000 our
- 20:11way through and what is.
- 20:13Research is probability.
- 20:15So that is what we found that if one
- 20:19reopen in 14 days after the first day,
- 20:22observe 0 case.
- 20:23So that means it can be zero and 120
- 20:26again in this type of situation then
- 20:29the researchers probability is 97%
- 20:31and if one observes the zero case
- 20:34for 14 consecutive days and then
- 20:36the resurgence probability is 32%.
- 20:38So what is tell us is we need to be
- 20:41management and don't reopen too early.
- 20:44So this is happened last.
- 20:46May and many of the state in the
- 20:49South do it reopened too early.
- 20:51Then we saw those cases are searched
- 20:53in the in the summer in the South.
- 20:56So what's the take home away?
- 20:59Take a take away message on the
- 21:02number 2 is to control the pandemic.
- 21:05A single control measures not enough
- 21:08money to use multiple control measures,
- 21:11and including the mask,
- 21:13wearing social distancing and
- 21:15massive testing,
- 21:15contact tracing and also supported
- 21:18isolation and quarantines and also
- 21:20effective treatment and also the vaccine.
- 21:23And so the in the JAMA paper
- 21:26we call it multi faceted.
- 21:28Intervention and then later on in
- 21:31the summer and people give it a nice
- 21:34name and called the Swiss cheese model.
- 21:36So that is a nice name and so the
- 21:40challenge is we we know those
- 21:42in control measures,
- 21:43but it's difficult to implement
- 21:45those control measures and also keep
- 21:48high compliance in many countries.
- 21:49So the defining challenges the
- 21:51public house control,
- 21:53measure implementation and then
- 21:54keep up with the compliance and
- 21:57also the vaccine definitely is.
- 21:59Really critical and we.
- 22:01We know that by now there are
- 22:04two successful ovac seen,
- 22:06one in US1 is the face by Pfizer,
- 22:09the other is more donor under
- 22:11the efficacy is 95%.
- 22:12This really really amazing
- 22:14scientific advance.
- 22:15Developing the vaccine in such a short time.
- 22:20Under so we also.
- 22:22Last spring we also developed a
- 22:25website on that help estimate the
- 22:27RT value as a different resolution
- 22:30at the for different countries,
- 22:33States and counties and so you can see that.
- 22:38But we copies are key curve and
- 22:42for a different.
- 22:44Reach reaches and so this work
- 22:46was led by Andy Xu, my student,
- 22:49and she lucky there my poster.
- 22:52And so this website was featured on
- 22:55the in Nature Article last summer.
- 22:59So how do we fit this model?
- 23:02So because there are lots of data points,
- 23:05so we want to estimate the curve so
- 23:08therefore instead of using the partial
- 23:11differential equation model and we
- 23:13extended this epidemic model which was
- 23:15originally proposed by query in 2013,
- 23:18and so the model in this type of
- 23:20epidemic model is quite different
- 23:23from the traditional logistics
- 23:25traditional statistical model.
- 23:26So we need to build in the.
- 23:30If the. An infectious component,
- 23:33so here is supposed.
- 23:34Why is the number of cases so think
- 23:37about the number of cases and for
- 23:40each day in Connecticut and then
- 23:42one first need to calculate this
- 23:44Lambda T and this Lambda T is called.
- 23:47Basically calculates the number
- 23:49of people at risk,
- 23:50so that is calculated using the products
- 23:52of the serial interval distribution.
- 23:55Multiply the number of cases in the
- 23:57previous period said like 7 days and
- 24:00then the Ark is a parameter one moment.
- 24:03Estimate so in the original model,
- 24:05the estimate RT at each time point
- 24:07that estimate a lot of parameters,
- 24:09and then when building this
- 24:11person model and with Lambda T as
- 24:14offset and RT as a parameter.
- 24:16But they asked me lots of parameters
- 24:18and one also account for the
- 24:20reporting deley by using a lag.
- 24:22So we what we did here with we try to.
- 24:27Accommodate on the Covic features
- 24:29and so we estimate us zero interval
- 24:32distribution and from this comma
- 24:34distribution using the paper in
- 24:36publishing in nature method and
- 24:38then in order to estimate RT as many
- 24:41many values and we assume a curve
- 24:44and the estimate by using a spline.
- 24:47So there are few angle in work
- 24:49and so we want to estimate RT as a
- 24:52function but cover it and also the
- 24:56in the traditional epidemic model.
- 24:58One assumed answer.
- 24:59Him and trade is a constant
- 25:02overtime and so the,
- 25:03but in practice the entertainment
- 25:05rate is not constant,
- 25:07especially when the number
- 25:08of tests are goes up.
- 25:10What number of positive test
- 25:12rate goes up and then uncertain
- 25:15manner it will get better and.
- 25:17So we want to answer payment way to
- 25:20be a function of the coverage and
- 25:23also we want to instead of fixing the
- 25:26reporting deal if we want to use the
- 25:29data to model the reporting deley
- 25:31and using all those met component,
- 25:33we can estimate the prevalence.
- 25:35So here are some preliminary result and so.
- 25:39The the code so you this is for the US
- 25:42data you can see right now and many
- 25:45countries a number of cases between
- 25:47being going down really nicely and
- 25:49so the current USRT value is about .78,
- 25:52and so we hear when you can see
- 25:54we have this arty curve that
- 25:57expanded so below 1 now.
- 25:59And also you can see the number of new
- 26:01cases have been going down and also
- 26:04the number of deaths has been going down.
- 26:07But there is a lag between the best.
- 26:09Under the case. And also this is
- 26:13the state level are key value.
- 26:15So just give example like for California
- 26:18you can see that the art in California is
- 26:21about .67 and so does this very nice banded
- 26:24curve for the cases and also for that.
- 26:28So now let me talk about the what are the
- 26:31factors associated with Covic infection.
- 26:34So as we start from the Wuhan data,
- 26:37then I'll move to the US data.
- 26:40So the data we estimated the
- 26:43attack rate on the my age.
- 26:45So you can see that the each
- 26:48of the period separately,
- 26:50and so you can see that for the
- 26:53older people that purple and yellow,
- 26:55and then the tax rate was.
- 26:58Much higher than the younger people,
- 27:00and so this is a good lesson.
- 27:03And in the spring last spring,
- 27:05then later on, as you know,
- 27:07like in US and there were more cases.
- 27:11Elderly cases in the spring and but
- 27:14then the elderly is become very careful
- 27:17and try to protect themselves and
- 27:20most of the cases in the summer and
- 27:22also in the fall were younger people.
- 27:25And then on the right that shows that
- 27:28the male and female that you from one
- 27:31day to the attack rate was similar.
- 27:34But health care worker,
- 27:35the purple bar has much higher
- 27:38infection rate,
- 27:39especially before the intervention,
- 27:40and then after interventions.
- 27:42Acrid among the health care worker,
- 27:45and was better and so that calls
- 27:47for the importance of the PPS
- 27:50and before the intervention.
- 27:52People were not aware of the Covic,
- 27:55and so, therefore, is this not many people,
- 27:58not many health care workers.
- 28:00Hard to pee pees.
- 28:02So I give a talk on the Wuhan finding on
- 28:06the March just before the school public.
- 28:09Just before Harvard started the spring
- 28:12break and throw in one of the slides,
- 28:14I showed that the on the day
- 28:17before the ABC News,
- 28:19there's one picture of
- 28:20the health care workers.
- 28:22And so I showed up there so the
- 28:24the the health care worker will
- 28:26not properly protected in US and
- 28:29so they had no protection suit
- 28:31and no face shell for example.
- 28:34And then the infection could
- 28:36be go through eyes.
- 28:38I did not realize that those three
- 28:40slides on the showing the health care
- 28:42workers not properly protecting US were
- 28:45widely distributed during the weekend.
- 28:48So the March 13 was a Friday.
- 28:50Then on March 16, that was a Monday,
- 28:53and so there was a national campaign on
- 28:57the protection of health care worker,
- 28:59which comprehensive PP is.
- 29:01And so the in short time and there were.
- 29:04Over 1.7 million Xan signatures
- 29:07and sold in this.
- 29:09No, the this picture was taken from my talk.
- 29:12So during that period I got to
- 29:14know a lot of health care workers
- 29:17and many of them and wrote to me
- 29:20and so it's it's kind of like a.
- 29:22Nice to see,
- 29:23like a little statistical
- 29:25analysis and could
- 29:26help the community.
- 29:27And also in the spring on the so
- 29:30I did something that station are
- 29:33supposed to do and so that we spend
- 29:37quite a bit time how working with
- 29:41the state of Massachusetts and also
- 29:43with abroad and so helping shifting
- 29:46the PPE under swap on from China.
- 29:49And so I was on the state, Massachusetts.
- 29:53The task force in the spring and then like.
- 29:57One thing I was really touched
- 29:59last spring was.
- 30:00Many, many peoples and step in to help
- 30:03without asking expecting any credit.
- 30:05So they really a wonderful experience
- 30:08and by working with so many peoples
- 30:11and who stepped in to help and so
- 30:13like in the screen 'cause there
- 30:16were not many flight from China to
- 30:18US so was difficult to shift under
- 30:21those medical supplies and two US
- 30:23and then so was really wonderful.
- 30:25Many people help out and so you can
- 30:29see that there were four flight.
- 30:32Shifting the usapyon swap watered
- 30:34by the state of Massachusetts
- 30:36from Shanghai to Boston under the
- 30:38first was the flight of the first
- 30:41flight leaving could own,
- 30:42and because there were not many
- 30:45commercial flight available
- 30:46and travel flight available,
- 30:48so this flight was converted from the
- 30:50from the Air Canada Flight and the
- 30:53passenger flight to a charter flight
- 30:56and then the picture on the right is
- 30:59the first flight arriving Boston.
- 31:03And also I the innerspring or we
- 31:06launch how we feel up and so this
- 31:10app collects the information about
- 31:12the Covic 19 symptoms and behaviors
- 31:16and also testing a result.
- 31:18And so this was in collaboration
- 31:21with some junk.
- 31:23Many of you probably know fun by
- 31:26his work in CRISPR editing and
- 31:29gene editing and CRISPR and also
- 31:32also banned Superman.
- 31:34Who is the CEO of country?
- 31:36So this is really a great.
- 31:39Collaboration between academia
- 31:40and industry 'cause we are not
- 31:43very good at developing up,
- 31:45but people in industry.
- 31:47They're much better developing app.
- 31:49So so many volunteer helping with
- 31:51this how we feel project and we
- 31:54build a nonprofit organization and
- 31:56with so many volunteers and then
- 31:59this app has over 750,000 users
- 32:02and also 50 million responses.
- 32:04And so I'll present some of those
- 32:07results and this is the first paper.
- 32:10Out of this,
- 32:11how we feel project was published in Nature.
- 32:14Human behavior last summer.
- 32:17So here last spring,
- 32:18who were more likely to be tested?
- 32:21And it turns out that people
- 32:23who had symptoms,
- 32:24CDC symptoms or health care workers and
- 32:27essential workers and people of color,
- 32:29they were more likely to be tested.
- 32:31So that makes sense,
- 32:32because in the spring the testing
- 32:35kids were not as widely available,
- 32:37so the vulnerable group should
- 32:39have priority to be tested.
- 32:40And so this also present analysis challenge,
- 32:43because the people who were
- 32:45tested or likely to be sicker.
- 32:47And so therefore this is not a random sample,
- 32:50so when we studies Association between the
- 32:53factors associated with the infection,
- 32:55we have taken into account
- 32:57that people who were tested
- 32:59was not a random sample and so therefore
- 33:01in the analysis we use the inverse
- 33:04probability weighted procedures and
- 33:06to account for the selection bias.
- 33:08So we found that male with a higher
- 33:11risk of infection than females.
- 33:13And also we found that people of color
- 33:16were at higher risk of infection.
- 33:19And also the essential workers
- 33:21and health care worker and these
- 33:24were at higher risk of infection.
- 33:26Also, we found another household
- 33:29exposures and and also community
- 33:31exposure are significant risk factor
- 33:33for infection and so you can see that
- 33:37for the household exposures after
- 33:38the show is almost 17 for Community
- 33:41exposures as we show almost three.
- 33:44So So what that mean is we need
- 33:47to break the within household
- 33:50and close place transmission and.
- 33:53Cluding, the nursing home,
- 33:55homeless shelters and prisons,
- 33:57and so and also we need to control
- 34:00the community transmission and so
- 34:02this finding was supported by the
- 34:05Massachusetts data that Massachusetts
- 34:08last year reported that almost 90%
- 34:11of covid cluster were household.
- 34:13So what that mean is household
- 34:16transmission is dominant is prevalent.
- 34:18Dominant lots of transmissions.
- 34:20And also we found the most important
- 34:23symptoms and was not the fever and
- 34:26cough was lots of peace and smell.
- 34:29So in particular we found out
- 34:31ratio is almost 33 associated with
- 34:34loss of taste and smell.
- 34:36About 40% of those who were past
- 34:39positive had lost of taste buds,
- 34:41taste and smell.
- 34:42Among those who are not testing about
- 34:466.6% among those who are test negative.
- 34:49That was about 5%.
- 34:50So this is an important symptom is also
- 34:54distinguished from the flu symptom.
- 34:56Then we also build a prediction
- 34:59model giving there were not enough
- 35:01tests available and then can we
- 35:04use the screening on two and two?
- 35:07Predict whether a person is
- 35:09likely to be infected or not.
- 35:12So by using the CDC symptom,
- 35:14you can see that the RC
- 35:16curve is AOC is about 70%.
- 35:19Using all the variables and it's
- 35:22about 80% if we use a simpler
- 35:25model only used for variable,
- 35:27including the three exposure
- 35:28variable and also the loss of taste,
- 35:31smell,
- 35:32the symptom variables and then
- 35:34you can see the AOC is also 80%.
- 35:37And so this is very simple model but has
- 35:40very good predictability for infection.
- 35:42And when we build this model we use.
- 35:45This actually proves a boost that is a
- 35:48scalable gradient tree boosting method.
- 35:51So now let me talk about the
- 35:54defending challenge on the in 2021.
- 35:57So first is the vaccine rollout and optic,
- 36:00so the science was really
- 36:02wonderful last year,
- 36:04so developing the vaccine such as
- 36:06short time with such high efficacy,
- 36:09that's really amazing and so.
- 36:12So the challenge is the vaccination program.
- 36:15So basically,
- 36:16how can we get the vaccine into people's arm?
- 36:20And so so that basically includes the
- 36:23distribution and also the administration.
- 36:26Also,
- 36:27it's important to have equitable
- 36:29and scalable vaccination,
- 36:31and also is important to
- 36:33overcome vaccine hesitancy.
- 36:34I'm going to focus on this one.
- 36:39And the second defining challenge is
- 36:43the massive scalable testing and so.
- 36:47PCR test yes, a gold standard,
- 36:50but it is expensive and to do
- 36:53the massive regular testing.
- 36:55So I'm going to talk about
- 36:58efficient testing strategy using
- 37:00the pooled testing and also the
- 37:03other strategies rapid testing.
- 37:05And the third component is the
- 37:07implementation and compliance of
- 37:09public health control measures.
- 37:10So if you look at a quick job
- 37:13of the cases in January is not
- 37:16likely to do to the vaccine,
- 37:19because only less than 10% of the
- 37:22US population had been vaccinated.
- 37:24I seem like the last last month and
- 37:27the the implementation and compliance
- 37:29and control measures and became
- 37:32better and people pay more attention
- 37:35to the behavior changes so that.
- 37:37Definitely is an important message.
- 37:41So let's look at the vaccine rate and so.
- 37:45Overlap and this is from the one word data.
- 37:48You can see.
- 37:49Israel is definitely the role model
- 37:51and so the right now they have an
- 37:54average 70 doses and per 100 people.
- 37:57And so after we account that some people
- 38:00have two doses on average about 40%
- 38:03people in Israel had been vaccinated
- 38:05and with so that's really amazing.
- 38:07And you have this less than 10%
- 38:10if on the right you can see we
- 38:12have a serious equity issue.
- 38:15And in particular,
- 38:16you can see basically nobody
- 38:18in Africa has been vaccinated,
- 38:20so that's really not good.
- 38:23So the another defining challenges
- 38:26vaccine hesitation.
- 38:27So in order to achieve the
- 38:29vaccine induced herd immunity,
- 38:31we need to overcome vaccine hesitation.
- 38:34So I'm going to present the
- 38:36findings and from the how we feel
- 38:39data show McCabe is my Postal.
- 38:42He take a lead in this work in
- 38:45collaboration with many colleagues.
- 38:48So here is a way, a lunch,
- 38:51the Maxim question in how we
- 38:53fill up in early December.
- 38:55So with here the result of analyzing the
- 38:58first month data about 30,000 people.
- 39:01So we develop a partnership with
- 39:04Kinetica and last spring and so
- 39:06that's why you can see we have more
- 39:09respondents and in the kinetica,
- 39:11and also because the countries
- 39:13is located in California.
- 39:15So we had more respondent respondent.
- 39:18In California,
- 39:19so if you look at overall vaccine hesitancy,
- 39:23hesitancy read,
- 39:24you can see like thoughts are more hesitant,
- 39:27and so overall the vaccine hasn't
- 39:30hesitancy rate is about 1818% from the
- 39:33hallway field data and 82% on the.
- 39:37What said they are likely were
- 39:39more likely to take the vaccine.
- 39:42So if you look hard, um,
- 39:44vaccine hesitancy rate by race and ethnicity,
- 39:47then you can see that people of color
- 39:50are much more likely to be vaccine hesitant.
- 39:53So in particular,
- 39:55if you look at a black for example,
- 39:58the vaccine hesitancy is is all.
- 40:0046%, almost 50% so so compared
- 40:03to white is about 15%,
- 40:06but compared to Hispanic,
- 40:08about 30% you can see a large
- 40:11fraction of them are undecided group.
- 40:14So what that mean is that a
- 40:18community engagement through
- 40:19the education of outreach is important.
- 40:23To overcome vaccine hesitancy,
- 40:25so here are the results.
- 40:27Who are more likely to be vaccine
- 40:30hesitant and so we found the
- 40:32younger people are more likely to
- 40:34be a vaccine hesitant and females,
- 40:37and also health care worker essential
- 40:40workers and also the people of color.
- 40:43In particular,
- 40:44black people are 3.5 times more likely
- 40:47to be vaccine hesitant than white,
- 40:49and people with pre existing
- 40:52conditions and low income.
- 40:54And also rural areas and also the
- 40:57thoughts and also places with high kufic
- 41:01burden and also the people who they are.
- 41:05So those are more likely to be vaccine
- 41:09hesitant people who wear masks and
- 41:12also use the protective measures.
- 41:15They are less likely to be vaccine hesitant.
- 41:20Talk to us in summary.
- 41:22So the the vulnerable group are
- 41:24more likely to be vaccine hesitant,
- 41:27and so they include people of
- 41:30color health care worker,
- 41:32essential worker and the young people female
- 41:35and the regions with high kovik burdens.
- 41:38And also the people with
- 41:40pre existing conditions,
- 41:41parents and low income.
- 41:44And also people not using
- 41:46the protective measures.
- 41:48So an Irish last late last year,
- 41:51the lunch, a community engagement alliance.
- 41:54And so this is,
- 41:56uh,
- 41:56involved multiple centers and the one
- 41:59of the goal is to do the Community
- 42:03engagement to help with participation
- 42:06in clinical trial and also.
- 42:09Overcome the vaccine hesitancy.
- 42:13So what this tells us is community
- 42:17engagement for vaccination,
- 42:18of which an education is important,
- 42:21so that Pic home number 5 is important,
- 42:24remained bigil and to scale up scale
- 42:27up the control measure and vaccination
- 42:30by protecting the vulnerable group,
- 42:33including the health care workers
- 42:35and essential workers and elderly.
- 42:37And also it's important
- 42:39to reach the zero kovik.
- 42:42So what that mean is.
- 42:44We need to be careful and reopen
- 42:46slowly when the number of cases are
- 42:49sufficiently small and also with
- 42:51the control measures are so if when
- 42:54we opened too early and we slipped
- 42:57in the control measure like what
- 42:59happened last summer and in the South,
- 43:02and is likely to see the researchers
- 43:04and also is important to pay
- 43:06attention to the long color,
- 43:09the long term effect especially among the
- 43:12young people and then also the to a build.
- 43:15I've seen uptick and it's important
- 43:18to have community engagement and
- 43:20outreach and build public trust.
- 43:23So basically, how can we implement the?
- 43:27Control measures and also implement
- 43:30vaccination and ensure high compliance
- 43:33is the defining challenge this year.
- 43:36And the truth,
- 43:38the other component is for this year is
- 43:41how can we boost the testing capacity
- 43:44and buy a cover by doing more test.
- 43:47And so because it's uh if one needs
- 43:50to do the test frequently and to do
- 43:53the PCR test is difficult to to do
- 43:56that for many institution because
- 43:59it's costly and so they put the
- 44:01testing provide an alternative.
- 44:03So I'm going to talk about this
- 44:06efficient put testing.
- 44:07A design using the hyper
- 44:09graph factorization first.
- 44:11What is the protesting?
- 44:12The goal is that would put testing is
- 44:16to screen a large population with a few
- 44:19tests and giving the limited resources.
- 44:22So this will help reopen the school
- 44:25safely and the simple idea is used.
- 44:28This uh document design sofa.
- 44:30Suppose we have 100 people and we
- 44:33do 20 tests and then so we pulled
- 44:37the people sample into different.
- 44:39Pools and suppose there's only one case,
- 44:42and then we test each pool support.
- 44:44Each pool has a 10 people.
- 44:46And then we tested each put do 10 pull
- 44:49test and how we found this cool is costing.
- 44:53Then we test every individual
- 44:55in this pool so in.
- 44:57Therefore instead of doing 100
- 44:58test you only do 20 tests and
- 45:01so this is the basic idea.
- 45:03Put testing.
- 45:04So what is the limitation of this
- 45:07simple of protesting design?
- 45:09And so this document design
- 45:11allow one individual go to
- 45:13one pool that is Q equal to 1,
- 45:16then cycle through the pool until
- 45:18all individuals are assigned.
- 45:20So if you look at this example
- 45:23with eight subjects and six pool,
- 45:25then you can see that we assign
- 45:27the face first six subject to the
- 45:306 four ABCDEF and then recycle
- 45:33the segments and each subject.
- 45:35And do the pull A&B so only one person
- 45:38per pool, so this is not optimal,
- 45:41only one pool,
- 45:42only one pool per person and this
- 45:44could lead to a non redundancy
- 45:46that also reduce the sensitivity.
- 45:48So the question is can we do better?
- 45:51Can we assign each individual
- 45:53to more than one pool?
- 45:54That basically makes a Q equal to two.
- 45:57So let's start from something
- 45:59like if there's a safe assign
- 46:01one person to two pools.
- 46:03So for example I assigned
- 46:05the first person to.
- 46:062A B second person to put a C and
- 46:09third person to pull busy and so on
- 46:12and then cycle through the order.
- 46:15So that basically this idea
- 46:17assign each person to two pools.
- 46:19What is the problem?
- 46:20The problem is by doing this simple
- 46:22way the design is not balanced.
- 46:25You can see that pull it has
- 46:27five subjects and puppy has four
- 46:29and pull up as only one subject.
- 46:32Because when when does them the
- 46:34pulling and by assigning one
- 46:36person to more than one pool
- 46:38while need to dilute the sample.
- 46:40So if one has a different solution for
- 46:43different pools that will affect the.
- 46:45Accuracy under then the sensitivity.
- 46:47So can we do better so that is
- 46:50a basic idea of a more balanced
- 46:52design we call the hyper design.
- 46:55So this using the hyper graph factorization.
- 46:58So the basic idea is we want to
- 47:01make the spell is that possible?
- 47:03So for example like here you can see
- 47:07that assigned person A to pull a BE
- 47:10person B person to pull CD person
- 47:123 two pull ENF person four to pull.
- 47:15PNC Person 5 to pull the D&F and so
- 47:19on and so this. This idea is after you.
- 47:22Each pool has four samples and
- 47:25so you can see for the 1st pool
- 47:28and the test is negative.
- 47:30The second pull the test is positive.
- 47:323rd pool passes positive and so on.
- 47:35Then afterwards we do the pool testing
- 47:38and then we can decode to see that
- 47:41which person is likely to be a case.
- 47:44And here you can see that.
- 47:46After do the decoding person 3, four,
- 47:49and seven are likely to be a positive,
- 47:53and then we test each of them
- 47:55individually and find out persons.
- 47:581 Seven are the cases and so why
- 48:00it is called hyper graph design.
- 48:04And that because this is related
- 48:06to the hypergraph,
- 48:07and in complete awe metrics
- 48:10and so you can think about this
- 48:13as the six pools are the six.
- 48:16Vertex is under the edges are the
- 48:19people and soap example like a person.
- 48:22One will assign this.
- 48:23This is edge person one and so
- 48:26that's assigned the pool at A&B
- 48:29and then person two assigned to
- 48:31C&D and so so this is so that's
- 48:34why it's called a hypergraph.
- 48:36So basically what we do is we
- 48:38need to assign the individuals
- 48:40and in the right sequence to make
- 48:43them as balanced as possible and
- 48:45not overlap as much as possible.
- 48:47And so by doing this design,
- 48:50when we kill equal to 216 pool,
- 48:53we have 5 factorizations and
- 48:55so you can see for
- 48:57each factorization there's no overlap and
- 49:00and also between every two consecutive.
- 49:03Assignment under then there
- 49:05is no overlap as well.
- 49:08And by doing this hypergraph designs
- 49:10and so you can see that we can
- 49:13have a balanced pool and also is
- 49:16very easy to implement and so this
- 49:19Calculator and so and also very easy
- 49:22to decode and so this is based on
- 49:26the company company Atomic Comics.
- 49:29Population so we can do this calculations
- 49:32and for Q equal two and three,
- 49:34but for Q equals greater than three,
- 49:37the calculations much more challenging.
- 49:39And so by doing that then you can see that.
- 49:43And here we plot out the.
- 49:46Efficiency against the prevalence,
- 49:47so only if the prevalence is
- 49:50low is worthwhile to do.
- 49:51Put testing if the prevalence is high,
- 49:53there's no need to do put testing so you can
- 49:57see that doing the hyper design and it is.
- 50:01Efficient and then the efficiency is
- 50:03almost 6 compared to individual design
- 50:06and also expect her than a redesign.
- 50:09That efficiency is 4.6 and when the
- 50:12preference become higher and then you
- 50:14can see that the efficiency goes down
- 50:17and then also comparing the hyper
- 50:20design with a radius and efficient,
- 50:22the sensitivity is pretty similar and
- 50:25also when we have 384 subject per batch
- 50:28and you can see that the hyper design
- 50:31still outperformed the other design.
- 50:34And for the Pytest peoples design.
- 50:36And so it.
- 50:39Especially when the prevalence become
- 50:41higher and then you can see that uh,
- 50:44sensitivity almost reach to 0.
- 50:46And so this also thought we look at a
- 50:49different design in different scenarios,
- 50:52and we showed that is hyper design
- 50:54is optimal and in terms of allocating
- 50:57resources and so here we plot out
- 51:00the X axis is the total number
- 51:02of sample collect each day.
- 51:04Suppose each day we collect 3000 samples.
- 51:07Suppose we only have the resources
- 51:09to do 12 foot tests.
- 51:11Then you can see that efficiency screening
- 51:14capacity using this Q equal to two is 122.
- 51:17That is much.
- 51:18A better and so so then also
- 51:21if one has a Q equal to three,
- 51:24that means a law allowing assigning
- 51:26one person to three pools.
- 51:28Then in that situation we need to
- 51:32use the hypergraph and with the.
- 51:35Those kind of. 20 different hyperedges.
- 51:43So in summary,
- 51:44to scale up widespread testing,
- 51:47hyper this is based on hypergraph
- 51:49factor factorization design provide
- 51:51efficient pool design to maximize
- 51:53the balance and efficiency,
- 51:55and the protesting is useful when the
- 51:57prevalence is low when the preferences highs,
- 52:01there's no need for protesting,
- 52:03just do the individual testing and we build
- 52:06a website that allows the investigator
- 52:09and the two design their own study.
- 52:13And so to combat kovik and so
- 52:15we are really in this together.
- 52:18And so we have to be together
- 52:21and be stronger.
- 52:22And so it's important to let the data speak
- 52:26and also develop evidence based strategy.
- 52:29And we show that there are
- 52:31two feature of the Covic.
- 52:33One is is highly transmissible,
- 52:36second is highly convert.
- 52:37And also it's important to remain vigilant
- 52:40and to use the multifaceted interventions.
- 52:43And to combat Covid,
- 52:44and so the there are multiple
- 52:47defining challenges this year.
- 52:49One is a Black Max magazine,
- 52:51distribution, uptake and education.
- 52:53The other is a scalable testing,
- 52:55so we talk about the put testing and
- 52:58so I want to thank the many of the
- 53:02collaborators and so who made many
- 53:04contributions to help with the project.
- 53:07And also there's a quick announcement
- 53:10and the cops and less at lunch.
- 53:13This Covic 19 data.
- 53:14Lisa Weaponer last December and
- 53:17so this is every two weeks and
- 53:19on Thursday from 12:00 to 1:00.
- 53:22And so these are we have the
- 53:24last few two month. Last month.
- 53:27We have a wonderful speaker.
- 53:29Great turn out.
- 53:30So those are the speaker in the
- 53:32coming weeks and from Denmark
- 53:34Mukherjee who will talk about Covic
- 53:37in Indian and Harvey Fineberg.
- 53:39Many of you know and he's a
- 53:42former president of National
- 53:43Academy of Medicine and also.
- 53:46Jim Young Kim is a former president
- 53:48of World Bank and so they're going to
- 53:51give the next week talks and thank you.
- 53:55Thanks young for this
- 53:57wonderful talk is very useful.
- 53:59I want to weather the audience
- 54:02have any questions for she home.
- 54:05Yeah I have a question.
- 54:07Yes, please song.
- 54:08So I'm wondering,
- 54:09will people who are willing to
- 54:11respond to the how we feel study be
- 54:14more likely to have lower hesitancy?
- 54:20Um? I would think the how we feel.
- 54:24We study people probably.
- 54:27I would think that
- 54:29probably likely to be true,
- 54:32and so the how we feel samples
- 54:35the because of people use the app,
- 54:38so at least that they are coping
- 54:41aware and they think a quickly is
- 54:44problem and so it's possible that
- 54:47in the national samples when we have
- 54:51a more representative samples and
- 54:53the hesitancy rate may be higher.
- 55:01Donna has a question.
- 55:03Yeah, I see her name was
- 55:05incredible work you've done.
- 55:07It's just absolutely
- 55:09phenomenal and breathtaking.
- 55:10How you've addressed
- 55:11each issue arising in the kobid epidemic,
- 55:15one by one and come up with such clarity.
- 55:20To guide us. So my question
- 55:22is about the hyper designs.
- 55:23I've been aware of pool testing,
- 55:25which I you know.
- 55:26We all know it's been around for awhile,
- 55:29but I'm just wondering, you know,
- 55:31is there like a rule of thumb like
- 55:34safe the prevalence rate is like 5%?
- 55:36How many digit
- 55:37in your graph like how many fewer tests
- 55:40would you have to use using a hyper
- 55:42design versus like the standard approach
- 55:44that you know people would tend to use
- 55:47which is to just test everybody. Yeah,
- 55:49so that is if you can see that from here.
- 55:53Yeah, it's a little hard to
- 55:56see it's a little small.
- 55:57Oh this hyper yeah I can the the
- 56:00so you can see the efficiency
- 56:02that is about hyper design.
- 56:04Yes almost six so that means that we
- 56:07can each task and have 6 people and
- 56:10by individual design so you can see
- 56:13that suppose you have 100 people.
- 56:15This is 96 so 96 / 6 and then then
- 56:19you can see that that is. How many?
- 56:23How many fewer tasks it less than 20?
- 56:26Yes, I think about it.
- 56:28If you do individual test that is 100.
- 56:32What is the?
- 56:33What is the?
- 56:34I didn't understand really what
- 56:35the 96 and the 3:50.
- 56:40Yeah batch, so there are 96 so in the
- 56:42so if you think about when you win
- 56:45you do test and then basically you
- 56:47need to layout the sample in a batch.
- 56:50If you think about it already
- 56:52then think about that.
- 56:53Basically they have eight.
- 56:54You have to think about.
- 56:56The Matrix is 8 by 12 matrix.
- 56:58You put all the samples and
- 57:00in this 8 by 12 array.
- 57:05OK, thank you yeah.
- 57:08And also if you look at the capacity
- 57:11here you can see the capacity is
- 57:13much better so you can see that.
- 57:15Suppose I need to test 3000 people a day.
- 57:19Execution can only afford half 12 tests
- 57:22and then you can see the efficiency.
- 57:25Screening capacity is almost 120.
- 57:30So that is really good.
- 57:31That's very, very good, yeah?
- 57:39So I also have.
- 57:43You still have a question
- 57:44or say thank you very much.
- 57:47OK, so I also have a related question.
- 57:50So she how you mentioned that before.
- 57:53A future work you want to perform a
- 57:57regarding the reproduction number
- 57:59estimation and this intervention
- 58:01work is to consider different
- 58:04other covariates when you are
- 58:06modeling the reproduction rate.
- 58:08So I wonder,
- 58:09have you also considered like trying
- 58:12to take into consideration different
- 58:14type of various the mutation of
- 58:17different various and then maybe certain
- 58:21various various high reproduction rate?
- 58:23And perhaps others.
- 58:25This process?
- 58:25Yeah, that
- 58:26is excellent suggestions on the so yeah,
- 58:29if we could have those data will be great
- 58:32that we could include those in the model
- 58:36besides the different type of variance.
- 58:38And also like the vaccination rate.
- 58:41That would be a very good variable included.
- 58:44And so the challenge for us right now is,
- 58:48as you know UK has been doing
- 58:50a great job in the sequencing,
- 58:53viral sequencing and so in other words the
- 58:56surveillance and sequencing surveillance.
- 58:58But not US, and so we have not doing a great
- 59:02job in sequencing and so so therefore the
- 59:06UK could monitor the new virus and well,
- 59:09but I think with the one of the things
- 59:12we need to do this year is to increase
- 59:16the various viral sequencing capacity
- 59:18so we could monitor the new variants.
- 59:21So then also make the data available and to
- 59:25the public and then that can be included.
- 59:28In the analysis.
- 59:30So what I've found that last year and
- 59:33during the Covic people were much more
- 59:35willing to share the data computer for an,
- 59:39though this is really,
- 59:40really wonderful and also the much
- 59:43more preprint and compared to before,
- 59:45and that were posted in about
- 59:47archive and made archive,
- 59:49and people were really willing
- 59:51and to share their findings to
- 59:54the Community as soon as possible.
- 59:56So these are really wonderful
- 59:58spirit about open science and.
- 60:00And is.
- 01:00:01Fired on the by many researchers last year.
- 01:00:05Thanks, that's really informed him.
- 01:00:10Sorry, go ahead who is
- 01:00:11trying to ask a question.
- 01:00:14Me, I mean yeah, I see who I have a
- 01:00:19question regarding to this pulling pulling
- 01:00:23test. You said the pulling test and
- 01:00:26compare with the individual test.
- 01:00:29The sensitivities are similar
- 01:00:31and right now I'm thinking
- 01:00:34if each individual does sensitivity they
- 01:00:37can be test by individual test one this
- 01:00:42individual mix with five other. Cure
- 01:00:45lung disease samples.
- 01:00:47Basically, the concentration is diluted,
- 01:00:50so how does sensitivity will be keep
- 01:00:54the same and how the next ways how
- 01:00:59to compare if there's one positive
- 01:01:02case with five individual their
- 01:01:05normal cases an A normal situation
- 01:01:08controls and compare with all six.
- 01:01:12There just get exposure
- 01:01:14with low concentration.
- 01:01:16So there will be probably have
- 01:01:19some sensitivity issue if
- 01:01:22pulling together compared to
- 01:01:24individual tests, then the error for
- 01:01:27measurement error testing error for
- 01:01:29the two different types of tests.
- 01:01:33How did you consider them additional
- 01:01:36to the hyper structured testing?
- 01:01:39Yes, I think this is a great question.
- 01:01:42Sorry I did not make that clear.
- 01:01:44What I meant was that hyper design and are
- 01:01:47ready that they had a similar sensitivity,
- 01:01:49but the sensitivity is lower
- 01:01:51than the individual tests.
- 01:01:52If you look at the curve in about I see.
- 01:01:57At the green and red they have
- 01:01:59a similar sensitivity by the
- 01:02:01compared to the individual test.
- 01:02:03That is, this black line and it has
- 01:02:05higher sensitivity and so then they
- 01:02:08as you are definitely right when one
- 01:02:10do one month does the pooled testing
- 01:02:12because the sample needs to be diluted,
- 01:02:15so therefore we need to pay a price
- 01:02:17and then sensitivity will be lower.
- 01:02:20Yeah, so the overall one the population.
- 01:02:25Pilots large scale testing.
- 01:02:27We may have more undetectable test.
- 01:02:30Think about if six samples always happen
- 01:02:33is 1 sample has a positive positive case,
- 01:02:37so we may have some testing error
- 01:02:40here. Yeah yeah, so yes.
- 01:02:43Yes, I'm sorry.
- 01:02:44Can I just jump in for a second?
- 01:02:47So 'cause I think the comparison as
- 01:02:50you could compare this hyper design
- 01:02:52to just testing everybody or the
- 01:02:54hyper design to the traditional
- 01:02:56pool testing approach where you
- 01:02:58just divide 100 people in each one
- 01:03:00is in a single batch and I think
- 01:03:03she hung what you're saying and it
- 01:03:05makes sense to me intuitively is by
- 01:03:08repeating people in multiple batches
- 01:03:10were increasing the chances of having
- 01:03:12doubles and triples in the same batch.
- 01:03:14And then lowering the chance of having
- 01:03:17false negatives as opposed to the
- 01:03:20traditional design where you take the
- 01:03:23100 people and they're only in one match.
- 01:03:27Is that right?
- 01:03:28Yeah, that's so that's right.
- 01:03:30So you don't want to put a
- 01:03:32hundred 100 people in one batch,
- 01:03:34because if you do that,
- 01:03:36then the sample need to diluted a lot and
- 01:03:39then you will sacrifice the sensitivity.
- 01:03:42So that's why when want to
- 01:03:44do the optimal design,
- 01:03:46want to account for both the
- 01:03:48balance and also sensitivity and
- 01:03:50including both of them and so then.
- 01:03:52So that's why when we build this when we
- 01:03:55define this efficient screening capacity,
- 01:03:57this calculation.
- 01:03:58That incorporated sensitivity
- 01:04:00in the calculation as well.
- 01:04:03I see thanks yeah.
- 01:04:05So the the pool design.
- 01:04:07So if you look at the traditional design,
- 01:04:10so here you can see that each person,
- 01:04:13the traditional design.
- 01:04:14Basically each person is assigned
- 01:04:16to a single pool and so this
- 01:04:19is this called document design,
- 01:04:21and so this design so you can
- 01:04:24see that the six for six people.
- 01:04:28In this example, like the person, one and.
- 01:04:32Assigned to a person to assign to P,
- 01:04:35and so this is not efficient design.
- 01:04:38And so if we assign each person to
- 01:04:40multiple pools and after decoding
- 01:04:42that will improve the efficiency.
- 01:04:47So generally the cute does
- 01:04:48should not be too big.
- 01:04:50So here you can secure equal to 1.
- 01:04:53That means one person assigned
- 01:04:54to one pool to equal to 2 means
- 01:04:57a person sent to two pools and
- 01:04:59just think about this is very
- 01:05:01interesting and so you can see the
- 01:05:03basically using using the graph and
- 01:05:05the ABCD basically means the pool
- 01:05:07and each edge indicated person.
- 01:05:09So you can see this person one
- 01:05:11is assigned to pull A&B and then
- 01:05:13says that's why there's edge here
- 01:05:15and person to assign to C&D.
- 01:05:17So this person too.
- 01:05:19And then so on.
- 01:05:24Thanks young, I have one.
- 01:05:26I have one last question.
- 01:05:28If other people do not have
- 01:05:30more question so I wonder is
- 01:05:32also related to the sensitivity.
- 01:05:34I wonder how we considered too instead
- 01:05:36of using on the testing directly but
- 01:05:39construct some posterior for each person.
- 01:05:42Use other covariates including
- 01:05:43your past history.
- 01:05:44Whether you have higher risk an.
- 01:05:48I wonder if we use such
- 01:05:50personalized information combined
- 01:05:52with this testing results,
- 01:05:53can we have better sensitivity?
- 01:05:57Very good question.
- 01:05:59Yeah, I can see the potential.
- 01:06:02I can see the potential for doing that.
- 01:06:05Yeah, I think they're right now in
- 01:06:10the screening program and the no.
- 01:06:13Demographic information is collected,
- 01:06:15and so only the sample collected.
- 01:06:18So for example, like abroad they.
- 01:06:22But bro, the dead on.
- 01:06:25The spring when we first started
- 01:06:28it was about maybe a 3000 of
- 01:06:32sample per day and so right now.
- 01:06:34As you know,
- 01:06:36broad cover almost 9025% of the testing,
- 01:06:39and in the New England areas almost
- 01:06:43cover like 3,000,000 and test and so
- 01:06:46the event of data were lots of data.
- 01:06:50Sand were collected and.
- 01:06:53In the testing,
- 01:06:53but those data cannot be used for research.
- 01:07:00Thanks, I don't know if the
- 01:07:02audience have further question.
- 01:07:04Maybe you can also email see how afterwards.
- 01:07:08So we're running a little bit overtime,
- 01:07:11but it's very. This wonderful talk.
- 01:07:14Can we have learned so much
- 01:07:16from Seahawks services talk?
- 01:07:18Thank you again. Thank
- 01:07:20you very much.