00:00 Good morning everyone.
00:02 My name is Roberta Gasti.
00:03 I'm the chief economist for human development,
00:06 and it is my pleasure today to be here and
00:10 chair this important event on behalf of Art CR,
00:13 our acting chief economist,
00:15 who's very sorry not to be here to listen to what promises to be a very
00:20 interesting and informative
00:23 conversation.
00:23 Today we will be talking about
00:26 Understanding the epidemiology of coronavirus.
00:30 Um,
00:31 I think this is a very positive,
00:33 hopeful title,
00:34 and,
00:34 uh,
00:35 this is something that I think everyone since uh this virus has,
00:39 has,
00:39 uh,
00:40 emerged and wrecked,
00:41 uh,
00:42 the world's life,
00:43 uh,
00:43 would like to do.
00:46 We are fortunate to have today a
00:50 prominent epidemiologist
00:51 who will go over some of
00:53 the implications of different nonpharmaceutical interventions
00:57 on the spread of the disease,
00:59 and we also have
01:01 Our bank colleagues who will provide us with an overview of
01:07 the different tools that are available to
01:09 us as practitioners in economics and health
01:13 to understand better the spread of the disease.
01:17 Uh,
01:18 without further ado,
01:19 let me start by inviting Damien de Valke and Jed Friedman,
01:23 uh,
01:24 to present a bird's-eye view of the instruments
01:27 available to understand the epidemiology of coronavirus.
01:31 Uh,
01:32 both,
01:32 uh,
01:32 Damien and Jed are senior economists in the
01:35 Development Research Group of the World Bank.
01:37 Um,
01:38 they are both working on health.
01:40 Damien with,
01:41 uh,
01:42 an emphasis on,
01:43 um,
01:44 Uh,
01:45 the interaction between health and education and on the epidemiology
01:49 of HIV AIDS
01:51 and Jed,
01:52 uh,
01:52 on the,
01:53 um,
01:54 impact of,
01:55 um,
01:56 uh,
01:56 health on productivity,
01:58 among other things.
01:59 Uh,
01:59 why don't I turn to you,
02:01 uh,
02:01 Damien and Jed,
02:02 uh,
02:03 for your,
02:04 uh,
02:04 introductory presentation,
02:06 and then we'll pass on,
02:07 we'll move on to the next speakers.
02:09 Thank you.
02:09 Over.
02:13 Hi,
02:13 Roberto.
02:14 Thank you very much.
02:15 This is Jed.
02:16 Um,
02:17 Damien and I are just here to
02:20 give a,
02:21 uh,
02:22 a really brief orientation to the speakers to come
02:27 that we look forward to from,
02:28 uh,
02:29 we look forward to hearing.
02:30 Um,
02:31 so we will only take a few minutes to discuss,
02:35 um,
02:35 a few issues related to
02:40 of uh non-pharmaceutical interventions.
02:43 Um,
02:44 what was the motivation for this?
02:47 Um,
02:48 e seminar,
02:50 well,
02:51 as we all know,
02:52 uh,
02:52 our institution has reoriented much of staff effort
02:56 now towards,
02:57 uh,
02:57 counteracting and ameliorating the effects of the
03:01 pandemic,
03:02 and,
03:03 uh,
03:03 we all believe it would be useful to review
03:06 the underpinning epidemiological models,
03:09 um,
03:09 so that we can understand the
03:12 Anticipated
03:13 courses of illness in the countries we work in.
03:16 Um,
03:17 and so what we hope to discuss in the seminar,
03:19 as Roberta just said,
03:20 is the
03:21 epidemiology of the SARS-COV-2 virus,
03:25 uh,
03:25 as well as the effectiveness of strategies to reduce,
03:29 uh,
03:29 the spread in the population.
03:31 So,
03:32 uh,
03:33 Dommy and I will begin with a short primer.
03:37 Um,
03:38 which is largely a brief overview of key terms and some ideas.
03:41 So here's just one slide on some terminology
03:43 that I think we've all become familiar with.
03:46 We've all become chair epidemiologists in the last
03:49 month or so,
03:50 and I'm glad that we have actual epidemiologists on the line today.
03:54 Uh,
03:55 to give us a much more informed view.
03:57 Um,
03:57 but just,
03:58 uh,
03:58 to bring everyone up to speed,
04:00 um,
04:00 let's,
04:01 let's just talk about a few terms such as the reproduction or are,
04:04 are not.
04:05 Um,
04:05 we've all seen this.
04:07 It's defined as the average number of
04:08 secondary infections from an initial infected person.
04:13 Uh,
04:13 the basic R0 is the sort of,
04:15 sort of the maximum epidemic potential of a pathogen,
04:19 uh,
04:19 in the absence of any distancing or other types of interventions.
04:23 And,
04:24 uh,
04:24 estimates for the SARS-CoV-2,
04:26 um,
04:27 vary a bit,
04:28 but
04:29 they essentially in the 2 to 3.5 range,
04:32 which means that for each infected person,
04:34 we should expect,
04:35 uh,
04:36 2 to 3.5 additional infections.
04:38 Uh,
04:39 in the absence of any,
04:40 any intervention.
04:41 Um,
04:41 and then,
04:41 of course,
04:42 the effect of R0,
04:43 which,
04:44 um,
04:44 is the
04:45 empirical,
04:47 uh,
04:47 R0 of what happens in the population
04:50 as we begin,
04:51 uh,
04:51 to try to control transmission.
04:53 And of course,
04:54 this depends on susceptibility,
04:55 so the percent that,
04:57 um,
04:58 uh,
04:58 are,
04:59 have yet to be exposed to the virus as well as the effectiveness of interventions.
05:03 And I just saw a piece yesterday,
05:05 uh,
05:05 estimating that
05:06 The effective R0 for Washington DC has,
05:10 is,
05:11 has fallen from 2.5 to 1 in the last few weeks as a result of the,
05:16 the stay at home measures and other things.
05:18 So that's
05:19 a bit of good news if that's true.
05:22 Um,
05:22 the latent period,
05:24 uh,
05:25 is the time from infection to onset of infectiousness.
05:29 Uh,
05:29 this,
05:29 of course,
05:30 is important in understanding the,
05:32 the spread of the disease,
05:33 and then that,
05:34 that's followed by the infectious period.
05:37 Again,
05:37 measured usually in days of the time a person's,
05:39 uh,
05:40 is infectious.
05:42 Um,
05:42 and I,
05:43 I,
05:43 we've been told by Imperial College colleagues now that maybe the best guess of the
05:47 infectious period is that it begins roughly half a day before the onset of symptoms.
05:52 Um,
05:53 and then there's the serial interval,
05:55 which is often used in,
05:56 in modeling,
05:57 which is the time between the onset of symptoms in the
05:59 index case and then the onset of symptoms in their context.
06:03 Seasonal forcing,
06:04 of course,
06:04 uh,
06:05 this is one way to get at differences in how climate affects transmission.
06:09 Not much is known about this.
06:11 This is obviously another critical factor,
06:13 uh,
06:14 that I don't think we'll be
06:15 speaking about too much today,
06:16 but there might be questions.
06:18 And one way to model seasonal forcing is
06:20 to vary the amplitude of variation and transmission.
06:24 And of course,
06:25 there are some familiar statistics to all of us now,
06:27 such as the case fatality ratio,
06:29 which is the number of deaths divided by the total number of people diagnosed.
06:33 Uh,
06:34 for a certain period of time,
06:35 ideally it's for that period of time to represent the duration
06:38 of the course of illness in the vast majority of cases.
06:42 And one way to account for the possibility of asymptomatic infections
06:47 is to switch from the case fatality ratio to the infection fatality ratio,
06:51 where the denominator also includes estimates for
06:54 the number of asymptomatic and undiagnosed infections.
06:57 And of course,
06:58 what's very relevant uh for policy and a lot of the health teams
07:01 now working in coronavirus preparation are various features of the health system.
07:06 Hospital beds,
07:07 staffing,
07:07 ICU beds.
07:08 This all relates,
07:09 of course,
07:09 to treatment of severe cases,
07:11 the duration of treatment,
07:13 and then the presence of key equipment,
07:15 um,
07:16 such as oxygen,
07:17 ventilators,
07:18 and then the personal protective equipment that staff need,
07:21 health staff need to try to prevent,
07:23 uh,
07:23 exposure themselves.
07:25 And then a large topic of this talk will be non-pharmaceutical interventions.
07:30 Uh,
07:30 there's various types I'll discuss in a second,
07:33 and,
07:34 um,
07:35 Uh,
07:36 what's relevant though for their effectiveness is the degree of compliance or
07:39 enforcement with these types of interventions and of course their duration.
07:44 Um,
07:45 so just continuing on this,
07:46 uh,
07:46 a brief overview here in this slide
07:48 of the types of non-pharmaceutical interventions at,
07:51 at our disposal.
07:54 Um,
07:54 the first would be surveillance.
07:57 Uh,
07:57 this is important
07:58 throughout,
07:59 but it's particularly important at points in the
08:02 epidemic,
08:03 um,
08:03 when transmission
08:05 is at a relatively low level and you can hopefully
08:08 try to contain it solely through the strategy of testing,
08:11 tracing,
08:11 and isolation.
08:13 Which of course is having
08:15 widespread surveillance in the,
08:16 in the population where you're
08:18 testing either widely or,
08:20 or,
08:21 or potential cases and then tracing all their contacts and,
08:24 and isolating them.
08:26 Um,
08:27 from the international perspective,
08:29 uh,
08:29 there are trade and travel restrictions.
08:31 Many countries adopted these,
08:32 uh,
08:33 at the start of March.
08:34 They,
08:35 they range in severity
08:37 from traveler screening,
08:38 which is allowing borders to be open,
08:40 I'd say screening for,
08:42 for symptoms,
08:43 and then if found to say,
08:45 have a fever,
08:46 then forcing traveler quarantine.
08:48 Uh,
08:48 and they run all,
08:50 all the way to the extreme version of,
08:52 of total border closure to people and goods,
08:54 which we've,
08:55 we've also seen at points around the world.
08:59 Quarantine,
08:59 of course,
09:00 is the most extreme form of social distancing,
09:02 uh,
09:03 which is really trying to impose this
09:05 corridor of at least 2 m between
09:08 all people and certainly between infected and non-infected people.
09:12 So quarantine is,
09:13 is not allowing,
09:14 uh,
09:15 any passage out of either a household if the
09:17 household's quarantined or even of entire regions as,
09:19 as was tried,
09:20 for example,
09:21 in northern Italy.
09:23 There are other forms of social distancing that are a little less severe,
09:27 um,
09:27 bans on public gathering,
09:29 public transport,
09:30 closing schools and businesses,
09:32 which we've,
09:32 we've all,
09:33 um,
09:34 seen in,
09:34 in,
09:35 in
09:36 I uh,
09:37 many countries that we're listening from today and then stay at home orders,
09:41 which are not as severe as quarantine insofar as you're
09:43 allowed to go out for essential services such as food,
09:46 uh,
09:47 procurement.
09:48 Finally,
09:49 there's hygiene recommendation and enforcement,
09:52 uh,
09:53 where possible,
09:54 uh,
09:54 promotion of preventive behaviors such as hand washing,
09:58 uh,
09:58 is certainly one tool in the arsenal
10:00 of,
10:01 uh,
10:01 reducing the spread of transmission.
10:03 Uh,
10:04 and then there's recommendations for face masks and even mandates,
10:07 um,
10:08 which is sort of an interesting subarea now with,
10:11 with more and more,
10:12 um,
10:13 uh,
10:14 evidence suggesting that it's,
10:15 it's a 11 effective tool in driving the R0 to,
10:20 uh,
10:21 below 1,
10:22 which,
10:22 of course,
10:23 is the goal of a lot of these.
10:24 So,
10:24 as soon as,
10:25 uh,
10:25 one new infection will infect less than one
10:28 going forward,
10:29 we would expect to see a decline in overall transmission.
10:33 And of course,
10:33 the effectiveness of all of these NPIs will depend on various factors,
10:37 as I said,
10:37 the duration of them,
10:39 the degree that population complies with them,
10:42 and then,
10:42 of course,
10:43 epidemiological factors as well,
10:45 such as the latency and,
10:46 and the R0 that I mentioned.
10:49 So,
10:49 thank you for bearing with me.
10:50 This is just a brief overview that will continue now with my colleague,
10:54 Damian,
10:55 I believe.
10:56 Uh,
10:56 so if I can invite Damian to continue.
10:59 Yes,
10:59 thank you,
11:00 Jed.
11:01 Um,
11:02 So,
11:03 uh,
11:03 what I,
11:04 I will do in,
11:05 in the,
11:05 uh,
11:06 the few coming,
11:07 uh,
11:08 slides,
11:09 uh,
11:09 Jed,
11:09 I think we still,
11:11 we cannot see your,
11:12 uh,
11:12 you're sharing the slides,
11:14 but we cannot see them.
11:15 Yeah.
11:16 Um,
11:17 It's uh trying to uh review um
11:22 a few of uh um
11:25 online resources for epidemiological modeling and projections.
11:29 I've reviewed some of them in a,
11:31 in a blog post that
11:32 at the end of March,
11:34 which probably would need some updating.
11:37 What's important
11:38 to know also is that the World Bank,
11:40 the International Decision Support Initiative,
11:42 the Gates Foundation,
11:44 And the World Health Organization has developed a
11:46 partnership to review and compare all models,
11:49 and
11:49 maybe David and Marli will tell us more about that.
11:53 Then one of the available tools of course,
11:56 we'll hear more about it today is the
12:00 estimates that the Imperial College COVID-19 response team provided.
12:06 Uh,
12:06 they've
12:07 providing,
12:08 they've been providing these estimates for
12:10 the world and for each country
12:12 under five scenarios going from
12:15 unmitigated epidemic to
12:17 early suppressor suppression strategy.
12:20 They'll they'll give,
12:21 I'm sure more detail about that,
12:22 but.
12:23 So
12:26 one very useful feature of their paper is that there is an online appendix,
12:29 and when you click on that,
12:31 you get access to an to an Excel table give you,
12:34 uh,
12:35 giving you
12:35 country level estimates
12:38 with even the further possibility to adjust some
12:40 of the parameters such as the baseline air knot
12:43 and the intensity of social distancing measures.
12:45 So it's a very useful tool.
12:47 Another tool that is available online is at the University of Basel.
12:52 It's an online tool that allows you to run your own projections
12:56 and enter country specific numbers.
12:59 I found it quite intuitive and flexible,
13:01 but it's important to
13:03 know that you will
13:04 need
13:05 to closely calibrate your proper
13:07 parameter estimates to the data
13:09 that you have.
13:10 Some
13:12 World Bank teams have used it to.
13:15 Make projections for the country they are working on.
13:18 Um,
13:19 it's a very important feature is that
13:22 the age structure of the population is preloaded
13:25 for each country on the online tool.
13:28 Then there is also projections available that have been
13:32 made by the Institute for Health Metrics and Evaluation.
13:35 Currently the projections are available for the US and European countries,
13:40 but it seems that uh projection for African countries will be coming soon
13:45 and the World Bank
13:48 is collaborating
13:49 for obtaining regularly updated projections for the countries
13:54 which have,
13:56 which are recipients of
13:58 COVID-19 financing from the World Bank.
14:01 It's important to know that with that tool,
14:04 the scenario projector assumes current social distancing
14:08 measures are maintained,
14:09 so it's different from the other tools where
14:11 you can go
14:13 all the way from unmitigated scenario to
14:18 mitigation measures and and suppression measures.
14:21 Also,
14:22 it's a model that is not based on a SIR model.
14:26 So SIR is susceptible infectious
14:29 recovered,
14:30 but it's a model that fits the Emperor Kelly observed COVID-19
14:34 population death curve.
14:36 So in my next slide,
14:38 I will show you an example of
14:40 how our World Bank team
14:42 has used the University of Basel tool to work on some projections.
14:47 It's a large middle income country.
14:50 they've shared it with the government,
14:51 but
14:52 the government has
14:53 preferred to uh
14:55 to keep this,
14:56 um,
14:57 not public yet,
14:58 so I'm not at liberty to give you the name of the country.
15:02 But you see how they've used the tool
15:06 in these two graphs
15:08 to predict on the left
15:11 the number of COVID-19 patients requiring hospitalization.
15:15 In red with the curve,
15:16 if there was no action,
15:18 no action.
15:19 And in blue
15:21 with
15:23 the
15:24 The shape of the epidemic
15:26 with a 2 month lockdown
15:28 and this is of course you have seen this picture before,
15:32 a nice example of flattening the curve
15:35 and on the right with the same color red for no action,
15:39 blue for a 2 month lockdown.
15:41 You see,
15:41 uh,
15:42 the COVID,
15:42 the projected COVID-19 cumulative fatalities.
15:46 I should also add that this World Bank team worked
15:49 hard on the University
15:51 of Basel T and then when the Imperial College
15:54 estimates
15:56 were,
15:57 were published,
15:57 they were very happy to see that their
15:59 numbers were very similar to the
16:02 um
16:03 suppression scenario from Imperial College.
16:08 No,
16:09 um,
16:09 I'm
16:10 moving on this slide that goes a little bit beyond
16:14 the pure epidemiology and try to talk about all testing
16:19 can be very important to inform
16:22 decisions and the effectiveness of uh non-pharmaceutical
16:26 interventions.
16:27 Um,
16:28 Jet,
16:28 Roberta,
16:29 and Aditya Matu and myself,
16:30 we've written a
16:32 short note on this,
16:34 and I'm sharing with you,
16:35 uh,
16:36 these figures.
16:37 So you'll see in blue the
16:39 epidemiological curve for the infection.
16:43 And we have been
16:44 yellow,
16:46 uh,
16:46 the fraction of the population that that is recovered.
16:50 So what we
16:52 Argue in that piece and through this figure is that
16:55 testing and two types of tests are very important to make decisions.
17:00 So one test is the test,
17:02 the test of
17:04 infectiousness,
17:05 and that's usually do
17:07 through a PCR test.
17:10 And you see that to the
17:14 left of the first red line.
17:17 That's the time when you are early in the epidemic
17:21 when
17:22 basically you can try to implement a test,
17:24 trace,
17:25 and selective
17:27 isolation,
17:27 a TTI strategy
17:29 relying mainly
17:31 on
17:33 the
17:34 infectiousness test
17:36 based on the PCR.
17:38 Similarly,
17:38 on the right of the figure to the right of the second
17:41 red line
17:43 when
17:44 the
17:45 epidemic.
17:47 The epidemic
17:48 outbreak has basically
17:51 has gone
17:52 down enough,
17:53 then we can
17:54 try to use again the same TTI test trace and selective
17:59 isolation
18:00 strategy
18:01 to try to prevent the resurgence of a new
18:05 epidemic wave.
18:07 In between these
18:09 two red lines.
18:11 When
18:12 the outbreak is really in full force,
18:14 it's time to use the suppression strategy
18:17 which we've colored in green.
18:21 We've also indicated that another test,
18:23 an antibody test,
18:25 could be used
18:27 at a certain point
18:29 first to protect essential workers because that test
18:33 detects who is immune to the.
18:37 Uh,
18:38 to,
18:38 uh,
18:38 to,
18:38 uh,
18:39 um,
18:40 COVID-19
18:41 and then eventually gradually
18:43 to allow other people to work and we also recommend that
18:47 uh
18:48 there would be a regular antibody testing
18:50 of a representative sample of the population.
18:53 As well as continuous monitoring of morbidity and mortality
18:58 to inform
19:00 these decisions on when to use the test,
19:03 trace and selective isolation strategy and when
19:07 to
19:08 enforce the suppression strategy.
19:11 No,
19:12 I'll,
19:12 I'll leave this and,
19:13 and just finish by outlining our e seminar today.
19:17 First,
19:18 um,
19:19 the Patrick Walker and Azra Ghani from the,
19:22 uh,
19:22 Imperial College COVID-19 Response Team will present their,
19:26 their model.
19:27 Uh,
19:29 they will tell us about the underpinning of this model in its main prediction.
19:34 Tell us all the effectiveness of the non-pharmaceutical information are modeled,
19:40 what are the main policy recommendations,
19:42 and they will touch upon specific issues for low and middle income countries
19:46 such as the age structure of the population.
19:49 The presence of many multi-generation households,
19:53 crowded habitat like slums,
19:56 the difficulty to enforce social distancing measures
19:59 when people are living and working in the informal sector,
20:02 and then our colleagues
20:04 from the Health,
20:05 Nutrition and Population.
20:07 Uh,
20:08 at the bank,
20:08 David Wilson and Marili Gorians
20:11 will discuss the trade-off between the need to curb the
20:15 coronavirus
20:17 and the pressure to reopen economies,
20:20 and they will go over a few key consideration in that
20:23 that will inform the trade-off.
20:26 The epidemic forces,
20:27 the capacity to manage a reduced measures,
20:31 population health,
20:33 the capability of the public health in the country,
20:36 the health system capacity,
20:38 national decision making capability,
20:40 and the role of
20:43 technical innovations.
20:45 And with this,
20:47 um,
20:48 Um,
20:49 will,
20:50 uh,
20:52 Give the floor to Patrick.
20:54 Thank you very much.
20:59 Uh
21:01 Thank you,
21:02 Damien.
21:02 This is Roberta.
21:03 I just wanted to take a moment to introduce
21:06 uh Pat uh Patrick Walker and Azrahani to our audience.
21:11 Um,
21:12 they are respectively lecturer and professor
21:15 at the infectious disease epidemiology department of Imperial College,
21:19 and
21:21 we of all,
21:22 the armchair epidemiologists,
21:24 as
21:25 Jed nicely put it,
21:27 read and learned tremendously
21:29 from the work that the Imperial College team
21:31 has done in helping the world understand the patterns
21:36 and the spread of coronavirus,
21:38 and
21:39 The potential effects of different policies.
21:41 So I understand that Patrick will start the presentation,
21:44 so thank you so much for being here and particularly thank you for the
21:47 excellent work that you are doing to um help us understand the disease.
21:51 Over.
21:55 Thank you so much,
21:55 uh,
21:56 Damien and Roberto.
21:56 I'm just,
21:57 just
21:58 trying to make sure I can master the technology,
22:01 um.
22:02 But yeah,
22:02 thank you very much for that really er
22:04 excellent introduction,
22:05 and I think.
22:07 Uh,
22:07 when it comes to actually.
22:10 It comes to
22:13 Coronavirus,
22:14 uh,
22:14 COVID-19,
22:15 epidemiology,
22:16 we're all fairly armchair cos we've only had
22:19 a few months to,
22:20 er,
22:21 to actually,
22:21 um,
22:22 I'm just gonna
22:23 move to the,
22:23 the correct slide
22:25 I picked the wrong ones.
22:42 Reviews for this.
22:53 Apologies for that.
22:55 So yeah,
22:55 so,
22:55 uh,
22:56 like I was saying,
22:56 I mean it's,
22:57 it's a newly emerging disease,
22:58 so I think we should all feel a little bit
23:01 more OK about being armchair epidemiologists.
23:03 We've only had,
23:04 um,
23:05 4 months now to,
23:07 to learn stuff about this disease and there's
23:09 not a lot of data to actually go on
23:12 and you know,
23:13 and
23:14 as we sort of know with these.
23:16 This is in when when outbreaks emerge,
23:18 there's a lot of difficulty in actually getting
23:21 representative data because
23:23 of a range of severity in terms of infections represent and
23:27 likelihood that you're going to
23:29 see them reported or they will
23:31 get into your surveillance system.
23:33 And also that is likely to change over time.
23:36 But um,
23:37 but one of the few things we do know about,
23:39 um,
23:39 COVID-19 is that there's this clear,
23:41 um,
23:42 severity pattern with age,
23:43 whereby,
23:44 uh,
23:45 the older you are,
23:46 the higher the likelihood of,
23:48 um,
23:49 having sort of severe complications,
23:50 respiratory symptoms and,
23:52 and needing
23:53 more,
23:53 uh,
23:54 you know,
23:54 advanced intervention to
23:57 a higher risk of fatality.
23:59 Um,
23:59 and this is,
24:00 these are estimates that were generated by,
24:02 um,
24:02 people in our team,
24:03 um.
24:05 and these are being used to kind of inform our
24:07 severity predictions
24:08 and by age
24:09 and also we know that there are some kind of relevant comorbidities characterized
24:13 primarily focused around
24:15 morbidities that exist in the settings where we've seen.
24:19 Uh
24:20 Large outbreaks,
24:21 so these are things like
24:22 cardiovascular disease.
24:24 Uh,
24:25 pulmonary
24:26 disease
24:27 and um diabetes.
24:29 Um,
24:30 we're also learning more as uh Damien was,
24:33 uh.
24:34 Very ably
24:36 Uh,
24:36 discussing earlier,
24:37 earlier,
24:38 and we're learning a lot more about the transmissibility of the.
24:42 So,
24:42 uh,
24:43 yeah,
24:43 so this is work,
24:44 um,
24:45 it's also been done by people,
24:46 other people in our team,
24:48 um,
24:48 Sam Bath and Seth Waxman and others,
24:51 and then what they're doing is they're taking data on the,
24:53 uh,
24:54 the deaths over time,
24:55 which even in high,
24:56 high income settings are the things that we think are most reliable
25:00 case reporting changes so quickly over
25:02 deaths are your
25:03 ways that you can most reliably get friends.
25:06 Um,
25:06 but unfortunately what makes that hard is you have this delay of around.
25:11 20 days from when you're infected to when you
25:14 I if
25:15 if
25:15 that's.
25:16 You're lucky enough that that's.
25:20 Um,
25:20 and that actually makes inferring the trends in
25:22 deaths of being in Europe quite hard,
25:24 but from this analysis,
25:26 what we are seeing is that this kind of curve is leveling off,
25:30 factual that actually if there weren't any.
25:33 That
25:34 um the curve initially is kind of
25:36 doubling times consistent with an R0 of about 3 or 4,
25:39 which is actually very highly infectious compared to something like.
25:42 So normal seasonal flu,
25:44 um,
25:44 which usually has an out of about 1.5.
25:47 Um,
25:48 and that,
25:48 um,
25:50 as we're implementing these measures,
25:51 we're seeing rather than the,
25:53 uh,
25:53 the debts double and double and double,
25:55 we're seeing that they are actually leveling off and.
25:58 that does correspond with some of the larger what we know as
26:02 non-pharmaceutical.
26:03 Uh
26:04 interventions,
26:05 um.
26:06 Things that are often called lockdown
26:08 and they are beginning to show impact so hopefully over the
26:11 we're gonna see
26:12 signs in more time.
26:14 Some of the hardest hit.
26:16 If these measures are maintained.
26:19 Um,
26:20 but even in these high income settings,
26:22 the problem is that the,
26:23 that the reasons they're needed is partially because of the,
26:26 the direct burden,
26:26 but also because they have this massive strain on the health system
26:29 and they lead to incredibly high demand for
26:32 hospitalization,
26:33 ICU,
26:34 oxygen and medical ventilation.
26:36 Also,
26:37 they,
26:37 uh,
26:38 pose a high,
26:38 high burden on the personnel who are offering care,
26:42 so.
26:43 High rates of
26:45 infection and self-isolation
26:47 problems and the need for extensive,
26:51 um,
26:51 and these are even,
26:51 these are some of the countries that have some of the strongest.
26:58 So,
27:01 Uh,
27:01 Charles
27:04 So,
27:05 um,
27:05 thinking about how modeling plays a part in this,
27:07 well,
27:08 it's,
27:08 it's
27:09 when you have a,
27:09 a,
27:09 you know,
27:10 a brand new disease,
27:11 a new disease that actually
27:13 kind of needs some sort of model in which to think about
27:16 the kind of the logical
27:18 scenarios that you might see,
27:20 and the model is just a,
27:21 it's,
27:21 it's not a,
27:22 especially where this early in October,
27:24 you can't really expect them to be kind of
27:26 completely accurate forecasts of the future,
27:28 but what they do give you is a sort of logical framework.
27:31 For
27:32 the assumptions you're making about different strategies,
27:34 and actually it kind of really helps to kind of hold you to those assumptions.
27:38 This is work that
27:39 the COVID-19 team have providing.
27:43 Neil,
27:43 the
27:44 head of our group,
27:45 uh,
27:46 has been
27:46 an AR as well,
27:47 uh,
27:47 providing to the UK government along with other modeling groups we have in the UK
27:51 um around different scenarios,
27:53 what,
27:54 what you do,
27:55 um,
27:55 what can you do and what is it likely to achieve,
27:58 where will that get you,
28:00 um,
28:00 and it does kind of
28:01 hold you to the logical assumptions.
28:03 So,
28:03 for example,
28:04 if these large suppression strategies.
28:07 We are gonna have to think about an exit strategy because we can't just go back to zero
28:12 intervention because then we do definitely get
28:15 sort of a second wave.
28:16 Um,
28:17 so,
28:18 so it,
28:18 it's helpful to really think about,
28:20 you know,
28:20 the future.
28:22 Um,
28:23 but,
28:23 a lot of countries don't have these kind of in-country modeling groups,
28:26 um,
28:27 that we have in the UK and other countries have as well,
28:29 but not many.
28:30 Um,
28:30 but they're all facing very similar questions at the moment,
28:33 um,
28:34 and
28:35 some of them might be a bit earlier in the pandemic,
28:37 but those questions are likely to be quite similar.
28:39 Um,
28:39 but what we've been trying to do in terms of our sort
28:42 of the global side of things is particularly with lower income settings,
28:45 what can we say
28:46 that kind of mimics some of these modeling approaches,
28:49 given that we probably can't do something comprehensive for every country.
28:52 Um,
28:52 and what would be useful given that
28:55 Clearly we're in a very time critical.
28:58 You have to
29:00 That needs to be useful,
29:00 it needs to
29:03 Um
29:05 So what we've,
29:05 when we've been thinking about this,
29:06 we've kind of identified 5 main factors which are
29:09 likely to vary substantially,
29:11 particularly by income.
29:13 Some of them are obvious,
29:13 some of them less so,
29:14 but kind of quite an obvious one is that
29:16 when you have a disease like this that has this kind of
29:19 um.
29:20 Severity signal with age where
29:22 the older you are,
29:23 the more likely you are to have severe consequences.
29:26 Well,
29:27 in many lower income settings,
29:29 populations are a lot younger.
29:30 They have higher fertility rates.
29:31 They have
29:32 lowest survival rates outside of,
29:34 outside of the
29:36 window,
29:36 um,
29:37 and that
29:38 leads to,
29:38 you know,
29:39 there being
29:40 the average age of an infection
29:42 go down,
29:42 and the,
29:43 we would might anticipate that that would be a,
29:45 a force to reduce
29:47 a person's burden.
29:51 That's one thing,
29:51 there's also a pattern with
29:54 sex,
29:54 biological sex,
29:55 which is
29:56 males are slightly
29:57 more likely to have severe disease,
29:59 but that's probably more likely to be balanced across country.
30:02 Um,
30:03 also there's societal structure.
30:05 Um,
30:06 which can vary quite a lot and in very important ways.
30:09 I'll try and speak up,
30:11 but please,
30:11 yeah,
30:11 let me know if I'm,
30:12 I'm not loud enough.
30:13 Um,
30:14 that
30:15 the,
30:15 well,
30:16 what we have in,
30:17 in the UK where I am now
30:18 is that outside of the pandemic,
30:20 elderly people,
30:21 uh,
30:22 elderly people tend to be a lot more kind of isolated,
30:24 and it's something that we would see as a,
30:26 a health problem outside of the pandemic window.
30:28 But if you are an elderly person,
30:30 you,
30:31 um,
30:31 I'll,
30:31 I'll go into that in a second time.
30:33 But there's also a that's for the next slide,
30:35 then there are also these patterns of comorbidities that might change.
30:39 Um,
30:40 also,
30:40 there's clearly going to be big disparities with
30:42 both health and system strength and capacity.
30:45 Finally,
30:45 we shall get onto that there are these,
30:48 the,
30:48 the,
30:48 the extent to which countries are likely to be vulnerable
30:51 to the social and economic impact of both the disease itself
30:55 and any interventions,
30:56 especially some of these
30:57 stringent interventions,
30:58 is going to differ widely by the level of income country.
31:04 So this is what I was saying a second ago,
31:05 um,
31:06 that,
31:06 yeah,
31:07 in,
31:07 so this plot is showing the
31:09 average household size of a person aged 65.
31:12 And what we see in both these,
31:13 this,
31:14 this kind of data and,
31:15 and in
31:16 contact surveys where we ask people about the number of contacts they make
31:19 in higher income settings,
31:21 particularly in Europe,
31:22 older people make less contacts.
31:24 They live in much smaller households,
31:25 and it's this pattern of kind of social isolation,
31:28 which is generally seen as a problem,
31:30 you may live on your own.
31:31 Um,
31:32 in,
31:33 uh,
31:33 lower income settings,
31:35 uh,
31:35 older people are far more likely to share
31:37 their house with their children or their grandchildren,
31:40 so in very low income settings,
31:41 often the average household size of a
31:43 person aged 65 could be
31:46 6 or 7,
31:48 and
31:49 also there are big differences in,
31:51 um.
31:52 School attendance and work patterns
31:54 that we that we think along with the data on social
31:58 contact patterns that exists in low and middle income settings,
32:01 not much,
32:02 but actually you,
32:03 you,
32:03 you very rarely see these patterns of social isolation of elderly people,
32:07 they're far more present in terms of the
32:09 number of contacts they make in their society,
32:11 um,
32:12 and that's,
32:13 as so we anticipate that.
32:15 Older,
32:16 there will be fewer older people,
32:17 but those older people that are there will
32:19 be more vulnerable to having the disease spread
32:22 to them,
32:22 uh within their households.
32:24 So
32:25 what's that trade-off in terms of on the one hand that could make it less severe,
32:29 on the other hand,
32:30 the fact they're less isolated makes them more likely to get it.
32:33 Well,
32:33 that's something that you can kind of try to explore with modeling,
32:36 making
32:37 sure that you're,
32:37 you're very clear about the assumptions that you're making.
32:41 What we have is we have a model that mimics some of the major uh
32:46 attributes of the
32:47 larger microsimulation model
32:50 that has been used to inform.
32:51 Um,
32:52 Patrick,
32:53 sorry to interrupt.
32:54 This is Roberta,
32:55 and I'm talking to,
32:57 uh,
32:58 Ale,
32:58 you,
32:59 Ale,
32:59 and,
33:00 um,
33:01 Ryan just to indicate that I've got some messages from,
33:04 uh,
33:04 those who are following us on YouTube that your slides
33:08 cannot be seen on the YouTube channel.
33:10 Ah
33:12 So I like,
33:13 I'll give a minute to you,
33:15 uh,
33:15 Ale and,
33:16 um,
33:17 Ryan to sort it out,
33:18 but I also take this opportunity,
33:20 besides the,
33:22 uh,
33:22 small technological glitch,
33:24 to indicate to our,
33:26 um,
33:27 to those who follow us on YouTube today that they
33:30 can send us questions through the chat room in YouTube,
33:33 and I'll represent them,
33:35 uh,
33:36 on your behalf to our speakers and the same,
33:39 uh,
33:39 uh,
33:40 the Webex,
33:41 um.
33:42 Um,
33:42 uh,
33:43 audience can do,
33:44 but,
33:45 uh,
33:45 those who are following us on Webex,
33:47 and it's been a,
33:47 uh,
33:48 first come,
33:49 first serve,
33:49 a sample of the 1st 20,
33:51 I believe,
33:52 or 50 who have signed up,
33:53 uh,
33:54 they can also raise their hand,
33:56 uh,
33:56 and,
33:57 uh,
33:58 ask the question directly in the Q&A session.
34:01 So over with this small organizational announcement,
34:05 um,
34:05 and maybe
34:06 whether Ryan or Ale
34:09 can send,
34:09 give me.
34:11 Send me a text
34:12 to indicate whether
34:14 what needs to be done to have the um slides seen on YouTube.
34:19 We definitely see them on the web Webex channel.
34:21 Thank you,
34:22 over.
34:25 Thank you Robert,
34:26 uh,
34:26 yeah,
34:26 and I think I,
34:27 I saw one of the questions about how,
34:29 how we're calibrating it,
34:30 and this is what,
34:30 what this,
34:31 this slide is sort of trying to show
34:34 that
34:34 what we do is we,
34:35 we take,
34:36 uh,
34:36 this model is kind of capturing sort of the,
34:38 some of the,
34:39 the major attributes.
34:40 What we do is we take,
34:42 uh,
34:43 the,
34:43 uh,
34:43 what we know about the transmissibility of the disease in terms of.
34:48 Um,
34:48 and that's a parameter we can vary by context.
34:51 Take what we think we know about
34:54 mixing patterns by age within a setting,
34:56 and we take the severity of infection by age,
34:59 we put them all together in a model,
35:01 um,
35:02 as long,
35:02 uh,
35:03 uh,
35:03 uh,
35:03 as well as some of the more key
35:05 parameters of the epidemiology that Damien was describing,
35:08 things like
35:08 time between
35:10 one infection to pass through another infection,
35:12 and,
35:12 uh,
35:13 the time from onset to death,
35:14 things like that.
35:16 Um,
35:16 and from that we can kind of generate what we think might
35:19 be the trade-off between changing age
35:21 patterns and the changing social mixing patterns
35:24 by income strata and by country.
35:26 And
35:26 this slide here
35:28 is just showing kind of
35:29 how we think that would vary.
35:30 So this is assuming the same R0 across all settings,
35:33 here we're taking an R03 which is.
35:35 As Damien was saying,
35:36 and as,
35:37 as the data in Europe is showing us,
35:39 our sort of best estimates for what we,
35:41 what this disease is doing,
35:42 uh,
35:43 and take the sort of likely impact in it of a
35:46 total impact of an unmitigated disease,
35:49 uh,
35:49 uh,
35:49 pandemic,
35:50 and that RO 3 highly transmissible disease means that it will spread very widely
35:55 in a within a community without any action,
35:57 um.
35:58 Uh,
35:59 but that actually as you think about more the
36:01 more sort of severe severe scale of the disease,
36:04 uh,
36:04 moving from hospitalization,
36:05 requiring critical care and,
36:07 unfortunately,
36:08 sadly,
36:09 potentially,
36:10 uh,
36:10 leading to mortality,
36:11 but actually
36:12 because you do have these broader demographic patterns,
36:16 uh,
36:16 the,
36:17 um,
36:17 we like,
36:18 we do expect an unmitigated scenario with no intervention.
36:21 Actually higher income settings would be per person,
36:23 at a per person level.
36:26 More heavily affected,
36:27 experience a higher burden,
36:28 but this is making a very strong assumption at this stage,
36:31 that,
36:32 that these that these severity patterns are matching those
36:35 for the health system in China.
36:38 Now clearly China,
36:39 at least with particularly in this pandemic,
36:42 made a really large effort in making sure that that the majority of people
36:46 are getting hospitalized.
36:47 Uh.
36:49 Patrick,
36:49 sorry,
36:49 this is Roberta.
36:51 Apologies for interrupting.
36:52 One message to you is that if you can speak as close as possible to the mic,
36:56 that will help.
36:58 Second,
36:58 I'm asking
36:59 Alejandra or Ryan to please unmute themselves and let us know whether the
37:03 YouTube channel now works with the slides because I'm continuing to get,
37:07 to get messages and since I'm,
37:08 I follow everyone on Webex,
37:10 I am unable
37:11 to um.
37:14 Have the information on whether.
37:15 Yeah,
37:16 hi Roberta,
37:16 uh,
37:17 Ryan is currently with ITS.
37:18 Unfortunately,
37:19 I don't know what's going on because it seems there's no audio.
37:23 Um,
37:23 but he's working on it.
37:26 Oh excellent.
37:27 So I would appreciate um Ali,
37:29 whether uh whenever this is sort of sorted out,
37:32 whether you can um just let me know
37:35 on the Webex or on the channel.
37:38 Since we have this moment of mute,
37:40 I would like them to ask Patrick to just,
37:44 uh,
37:44 at the end of his presentation to just give like a quick
37:47 point by point.
37:49 Uh,
37:50 sort of key messages summary
37:53 for those who have not been able to follow the um.
37:57 Uh,
37:57 the conversation.
37:58 Meanwhile,
37:59 I see many questions that are popping up on Webex.
38:03 Uh,
38:03 I don't know,
38:03 Patrick,
38:04 if you see them or whether Professor
38:06 Ghani is,
38:07 uh,
38:08 answering them,
38:09 but it might be good to
38:11 maybe take a moment to,
38:13 uh,
38:14 indicate them to the audience so that,
38:16 uh,
38:17 also the audience can see the conversation that is ongoing.
38:21 Um,
38:21 just to take a pause while the
38:24 technology is being fixed.
38:25 Um,
38:26 there was a question on.
38:28 Uh,
38:29 from Dmitry on how,
38:31 uh,
38:32 R 0 was,
38:33 um,
38:34 uh,
38:34 estimated,
38:35 and I wonder whether Professor Ganni wants to amuse
38:38 herself now while we take this pause to give,
38:41 um,
38:41 a quick answer for everyone,
38:43 also those on YouTube who might be,
38:45 uh,
38:46 now,
38:47 uh,
38:48 might hear it as well.
38:49 Professor Ganni,
38:50 would you want to,
38:51 um,
38:52 address that question?
38:53 Yes,
38:54 that's fine.
38:54 I've actually just put a comment in for those who might not be able to hear.
38:58 Um,
38:58 the,
38:59 our estimates of the,
39:00 uh,
39:01 basic reproduction number come from the early growth rate of epidemics.
39:04 We actually
39:05 estimated a,
39:06 a lower,
39:07 um,
39:08 value of around 2.4 from the Chinese data originally.
39:12 Um,
39:12 the doubling times of the epidemics in Europe have been
39:16 much faster,
39:16 and our current best estimate is around 3.
39:20 clearly it's a little bit sensitive to the
39:22 context and the setting in which transmissions occurring.
39:29 Um,
39:30 excellent,
39:30 thank you.
39:31 Um,
39:32 Ale,
39:33 uh,
39:33 do you know if the problem is fixed on YouTube?
39:36 Um,
39:37 I'm
39:39 OK,
39:40 no,
39:40 Ryan,
39:41 I'm on the,
39:42 hi,
39:42 this is Ryan here.
39:43 I'm on the line with
39:45 our IT colleagues,
39:46 and they're trying to solve the problem but do not yet have a solution,
39:49 so I'll
39:50 let you know as soon as possible,
39:52 but unfortunately the slides
39:54 aren't coming through,
39:55 um,
39:56 uh,
39:57 but I,
39:57 you know,
39:57 I'm,
39:58 I'll get back to you as soon as,
39:59 as soon as they have this solved.
40:02 Very good.
40:03 Um,
40:04 well,
40:04 why don't you signal that maybe in the chat room to the YouTube,
40:07 uh,
40:07 followers,
40:08 and then
40:09 we can maybe continue with the presentation and then I'll
40:11 ask Patrick just to summarize the main messages so that,
40:15 um,
40:15 those on YouTube,
40:16 which is a much larger audience,
40:18 uh,
40:18 can still follow the presentation.
40:20 Thank you and over,
40:21 Patrick,
40:22 see you.
40:31 Patrick,
40:32 you're muted.
40:35 And that um and yeah,
40:36 and we do actually have
40:37 some additional scenarios
40:39 accompanying the report that I can also provide a link for
40:43 if,
40:43 if people want to sort of see how these may change with
40:46 in different contexts,
40:47 if or not differs
40:50 to an extent.
40:51 So yeah,
40:51 so,
40:51 so this is just to say,
40:52 well this is based upon
40:54 severity estimates by age from a place where there was relatively speaking,
40:58 at a global level,
40:59 very good access to care.
41:01 And that's gonna differ
41:03 quite substantially by context and and and change the patterns a little bit,
41:07 which I'll get onto
41:08 in a,
41:08 in a minute.
41:10 So
41:10 what we then looked at is this is kind of the,
41:12 the,
41:13 the worst possible news,
41:14 the,
41:15 what happens if we don't do anything,
41:16 but of course
41:17 as,
41:17 as a
41:18 globe we are gonna do a lot to try and prevent this burden,
41:21 given how
41:22 big it is,
41:22 given that we estimate that this could be
41:24 40 million if we don't
41:26 deaths globally if we
41:28 don't actually
41:29 do something.
41:29 But but countries will certainly do things.
41:32 And um.
41:33 But,
41:34 you know,
41:34 um,
41:35 and
41:37 Without a vaccine,
41:38 that's all gonna be these non-pharmaceutical interventions,
41:40 modifying our social contact behavior,
41:43 um,
41:44 particularly in settings that don't have the er
41:46 capacity and the technology to implement some of these
41:49 really stringent test er testing and treating and tracing mechanisms.
41:53 A lot of countries probably will,
41:54 will,
41:55 will not have that ability
41:56 in the time that it takes.
41:58 So yeah,
41:59 so,
41:59 and so here this is where we sort of looked at,
42:01 well,
42:02 let's say you can't suppress the infection,
42:05 what's the best if you didn't suppress the infection,
42:07 if you couldn't
42:08 decide not to,
42:09 what would that lead to in terms of what could you do
42:12 to reduce this burden?
42:14 And this relies upon a concept called herd immunity,
42:16 which is,
42:17 which is
42:18 quite become quite a controversial concept in the
42:20 UK but it's actually just a scientific concept,
42:21 it's not particularly an intervention.
42:23 What it all it means is that
42:25 if you can't suppress infection,
42:26 any reductions you can make
42:28 to your contact rates within your community
42:31 can reduce the spread of the infection,
42:33 and eventually that infection will lead to a level of immunity
42:37 that will then stop the er the infection
42:40 from,
42:41 make the infection peter out because you have a level of immunity in the population.
42:45 Where if you hadn't reduced your contact rates,
42:47 it would overshoot that level,
42:49 so you'd actually get much higher levels of,
42:51 of burden.
42:52 So it's actually just reducing the level of overshoot,
42:55 um that you would get if you don't do anything,
42:58 um.
42:59 So what we find is,
42:59 you know,
43:00 with a,
43:00 with an honor of about 3,
43:02 those reductions are about a 45% reduction in contact rates
43:06 that you might get some additional substantial additional benefit if
43:09 you reduce the contacts in your elderly people even more
43:13 by some of these sort of shielding strategies,
43:15 and
43:15 we can perhaps discuss
43:17 how that might be possible in some settings more than others,
43:20 like a,
43:20 like a place with a household size of 7 is
43:22 clearly going to be more problematic to do something.
43:25 But um
43:26 yeah,
43:26 so,
43:27 so clearly these reductions are uh easy to
43:30 model in a,
43:31 they're easy to model,
43:32 but they're much harder to achieve in practice and
43:35 the solutions for that will vary by setting.
43:38 But what we do find is these have,
43:39 we have these big impacts.
43:40 So here the yellow bar shows what happened.
43:42 Nothing
43:43 and the.
43:43 Uh,
43:45 yes,
43:45 Patrick,
43:46 this is Roberto.
43:47 Sorry for being um
43:50 inter for interrupting you all the,
43:51 all the time.
43:52 So now everything is working on YouTube.
43:55 Uh,
43:55 but I'm getting like
43:57 a slew of messages on my phone from
44:00 colleagues and friends who are following on YouTube saying
44:03 we can't miss this is such an important presentation,
44:06 so I'm sorry,
44:06 but I have to ask you to go back
44:09 for slides.
44:10 I think that the problem started,
44:13 maybe you can go over.
44:16 A bit
44:17 more quickly on the
44:19 demographic and social structure,
44:20 but I would like you to essentially
44:23 start,
44:24 I think,
44:24 from here and then go over again
44:26 the perfect,
44:28 go over again the structure
44:29 in um
44:30 when I was going to school in Italy,
44:32 people used to say repetitta you want.
44:34 That two front,
44:35 which is,
44:35 you know,
44:36 repetition helps,
44:37 but it's a bit boring.
44:39 I'm sure
44:40 that,
44:40 um,
44:40 the audience will go back to bear with us for a couple of minutes.
44:43 This is not boring at all.
44:44 So thank you,
44:45 Patrick,
44:46 and thank you for all of those on YouTube for,
44:49 uh,
44:49 being so enthusiastic,
44:50 such an enthusiastic audience.
44:52 Over to you.
44:54 Thank you Roberto.
44:54 OK,
44:54 yeah,
44:55 so,
44:55 so yeah,
44:55 so,
44:56 so we,
44:56 we were mainly looking at how,
44:58 uh,
44:59 the.
45:00 How,
45:01 um,
45:01 some of the sort of guidances we give to high income countries,
45:04 how that might change and how our projections of
45:07 the spread,
45:08 likely spread of the pandemic may be altered in
45:10 different contexts and to see if we can give some
45:12 kind of broad level guidance.
45:15 Um,
45:15 so we identified five factors.
45:17 One,
45:17 and they're all I think fairly um intuitive.
45:21 So one is that um
45:24 is that
45:24 er
45:26 populations in lower income towns tend to be younger
45:28 and for er
45:29 for an infection like this that's more severe with age,
45:32 that clearly is likely to have an impact.
45:34 um
45:35 er
45:35 there also there's societal structure and how we
45:38 make contacts and that varies by settings,
45:39 so I'll talk about the next slide.
45:41 Comorbidities which have been focused on the.
45:45 Been identified in the countries
45:46 where the spread has been,
45:48 which is mainly
45:50 higher income settings,
45:50 and we'll come on to that.
45:52 And clearly there's disparities in health system
45:54 strength and capacity by income settings.
45:57 What we imagine,
45:58 and also the social and economic vulnerability of
46:00 societies
46:02 to both the direct impacts of
46:04 pandemic
46:05 and
46:05 any possible impact of the,
46:07 not some of the non-pharmaceutical intervention
46:10 Damien was talking about earlier.
46:12 So I've traveled quite quickly,
46:14 but
46:15 they are,
46:15 what we're finding is that lower income countries,
46:17 yes,
46:17 they are younger on average,
46:18 so there are fewer older people.
46:20 But we think those older people are gonna be
46:21 more vulnerable to having the infection spread to them,
46:25 because in low income settings like in the UK for example,
46:28 you're,
46:28 if you're an elderly person,
46:30 on average you share your house with about one other person.
46:33 You may even be by yourself and it's
46:34 outside of the pandemic window that's
46:36 seen as a problem because it's,
46:37 you know,
46:38 you,
46:38 you are socially isolated and that's
46:40 not a good thing.
46:42 In lower income settings,
46:43 the average household size of an older person is much higher.
46:47 It can be 6 or 7 in very low income countries.
46:50 Now clearly that that's going to change your contact patterns,
46:53 and it means that those contacts are sort of spread more evenly across the society,
46:57 particularly when
46:58 school attendance is maybe lower and less frequent,
47:02 so you don't get these sort of
47:04 large contacts of of younger people and work patterns are far more
47:08 spread out.
47:09 Um,
47:09 so we actually think that the contact patterns in
47:11 lower income settings are going to be far more flat
47:13 and higher income settings.
47:15 And,
47:15 and then,
47:16 so then what is this trade off in terms of
47:19 having a younger population,
47:20 but also where the older population are sort of more central in the society.
47:25 So,
47:25 um.
47:27 So that's the kind of question you can look at in a model whilst
47:30 making sure you
47:31 are very clear about the assumptions that you're gonna have to make.
47:34 So we did that looking at,
47:35 you know,
47:36 so what we do is we have a kind of a more simpler model framework,
47:40 but that incorporates most of the key elements
47:42 of the sort of more sophisticated micro simulations
47:45 that have been,
47:46 been provided to,
47:47 to the UK government and other governments.
47:50 So these are things like the
47:52 reproduction number,
47:53 the time it takes to,
47:55 for a person,
47:57 the epidemic to spread from one person to another,
47:58 known as the generation time.
48:00 And the,
48:01 all the delays between getting infected,
48:03 becoming sick,
48:04 chances of becoming severely ill,
48:05 the chances of dying,
48:07 and how long that takes and how that depends on age,
48:09 based upon the data that we have so far.
48:12 We can then
48:13 adjust this to the setting,
48:14 so we have the,
48:15 the,
48:15 the pathogen specific
48:17 parameters.
48:18 We can then take the setting specific parameters,
48:21 which are the demographics
48:22 and the the sort of representative context structure we think
48:25 within a country
48:26 and use that to sort of generate what we think might happen.
48:29 And
48:30 here,
48:30 next slide I'm going to show us what we
48:32 think would happen if nothing if nothing was done,
48:34 if we just let
48:35 the er
48:36 the the path that the um
48:38 pandemic just sweep through and didn't change our behavior or anything,
48:40 which clearly is not
48:42 what's happening and it's probably something that would never happen in reality,
48:44 it just gives us an idea of what we're dealing with.
48:47 Um,
48:49 what we would find is that,
48:51 as it has a reproduction number we're seeing in,
48:53 in 3,
48:54 that,
48:54 that
48:55 we know from the.
48:57 Epidemiological theory that that's going to lead to a
49:00 very high proportion of the population being affected.
49:03 By the end of the pandemic,
49:04 but actually if you look at the sort of the more severe aspects of infection,
49:07 which are age driven,
49:09 at least partly age driven,
49:11 hospitalization,
49:12 more,
49:13 more severe critical care,
49:14 um,
49:15 people needing critical care or
49:17 eventual,
49:18 sadly mortality,
49:19 we would,
49:20 we would um um anticipate that that would lead to a lower per person risk of death or.
49:27 In a lower income setting than a higher income setting,
49:30 but the problem here is that we make these assumptions,
49:33 that these parameters are
49:34 coming from
49:35 the early stage of the epidemic in Europe and China,
49:39 and those
49:39 settings have much higher healthcare capacity,
49:43 um,
49:43 than a lot of settings.
49:45 So clearly there's going to be that disparity that we
49:47 are going to have to try and account for,
49:48 and I'll get on to that later.
49:51 Um,
49:51 but what we did do before that is then think,
49:54 well,
49:55 what is the potential we have to mitigate the the
50:00 spread of this disease,
50:02 um.
50:03 And
50:04 first,
50:05 before we start thinking about,
50:06 you know,
50:06 closing,
50:07 locking down society,
50:08 if we suppress an infection,
50:11 what if we don't have the capacity to suppress infection?
50:14 What's the best that we could do?
50:15 Um,
50:16 and that's,
50:17 and it,
50:17 and it relies on this concept of herd immunity,
50:19 which has
50:20 become quite a sort of politicized term in the UK,
50:23 but it's actually not
50:24 any,
50:24 it's,
50:25 it's a just a scientific concept and it
50:27 comes from this idea that
50:28 as the disease is passing through,
50:30 eventually people will be requiring immunity,
50:32 and that will slow the disease down.
50:34 Um,
50:35 now,
50:35 if you lower your contact rates,
50:38 then that will,
50:39 um,
50:40 if you,
50:40 sorry,
50:40 if you don't lower your contact rates,
50:42 then actually once the level of,
50:44 uh,
50:44 immunity the population needs to prevent the outbreak taking off again,
50:48 once that's reached,
50:49 it won't just stop,
50:50 it will overshoot.
50:52 So you actually get a higher proportion
50:54 of the population affected than would be needed to get.
50:57 Herd immunity,
50:58 so lowering your contact rates
51:00 here about on average about 45%
51:02 on average,
51:04 we predict would
51:05 kind of halve the burden,
51:06 um,
51:07 not accounting for health healthcare strength,
51:10 um,
51:10 from something like 40 million deaths to 20 million deaths,
51:13 which is clearly like a a massive impact,
51:15 uh,
51:16 in terms of the burden this pandemic will cause.
51:19 But we did find that these,
51:20 these,
51:21 uh,
51:21 and with further impact if you can.
51:24 Further shield the most vulnerable,
51:26 so if,
51:26 if there are the extent to which you can
51:28 either cocoon older members
51:30 or protect them from whilst other people are,
51:33 you know,
51:33 from make their preferentially protect them.
51:36 And we could talk about how that might be more
51:38 difficult in lower income settings if you're sharing your house with
51:41 6 other people,
51:42 that might be harder than in
51:44 high income settings where that just means not leaving the house,
51:47 um.
51:48 And these are clearly quite easy things to model
51:50 in a
51:52 in a in a model,
51:53 you just change the parameter,
51:54 but in reality,
51:55 you know,
51:55 it's it's
51:56 how you,
51:57 they're best achieved,
51:58 it's probably like to vary by country.
52:00 And one of the things about these mitigation strategies
52:02 is because of these sort of flat contact patterns,
52:05 flatter contact patterns in lower income settings,
52:08 we expect them to have a lower proportional impact in
52:11 lower income settings because the disease can still spread fairly ably
52:15 to the to the older more vulnerable members of society.
52:19 So you know,
52:19 obviously
52:20 that's,
52:21 you know,
52:22 still a,
52:22 a fairly horrific burden caused by the pandemic.
52:26 Um,
52:26 but also there's,
52:27 it's,
52:27 it's gonna have a big,
52:29 a big strain on healthcare resources.
52:32 So this is just a map of what we
52:34 think the sort of under our optimal mitigation scenario,
52:37 the number of people per 1000 population
52:40 throughout the pandemic who would require a critical care bed,
52:43 in other words,
52:44 would,
52:44 would require ICU,
52:46 would require mechanical ventilation,
52:48 um.
52:49 And
52:50 that we are estimating would be higher on a per person level
52:54 of demand
52:55 in higher income settings with older populations.
52:59 But
52:59 then we need to think about how this demand for healthcare corresponds to supply.
53:04 Supply is clearly also going to be correlated with income.
53:07 This is work that was led by Charlie Whittaker,
53:09 who is one of Azra's PhD students,
53:11 um,
53:12 trying to update World Bank estimates to get more sort of fine grain estimates of
53:17 what the,
53:18 um.
53:19 What the current supply of
53:22 things like
53:23 the number of hospital beds per 1000 population
53:25 and within those hospital beds,
53:27 the proportion of those
53:29 that have the,
53:29 you know,
53:30 the capacity to treat the more severe
53:32 illness
53:33 in
53:34 more severe people with more severe consequences of infection.
53:38 And as we,
53:38 you know,
53:39 it's probably unsurprising,
53:40 there's a high correlation.
53:42 The
53:42 lower income setting,
53:44 the lower income of country,
53:45 the lower number of hospital beds and the lower number of physicians and nurses
53:49 they will have per 1000 population.
53:51 Not only are the number of hospital beds lower
53:53 of those hospital beds,
53:55 a smaller proportion of those used to
53:58 treat
53:58 severe cases,
54:00 um,
54:00 and actually in lots of lower income settings there's
54:03 near or.
54:04 We seem to be hearing a complete absence of these mechanical
54:08 ventilators which are currently saving a lot of lives.
54:12 And in these lower income settings,
54:14 these cases
54:15 aren't received this mechanical ventilation are
54:17 likely to experience a very high risk of mortality.
54:22 The absence of mechanical ventilation.
54:25 So then what we did is we took our sort of our scenarios of healthcare demand,
54:30 and we integrated them with our estimates of healthcare supply,
54:33 in terms of
54:34 both the capacity for healthcare,
54:36 er,
54:36 accounted for how long you would need a bed,
54:38 and you would need an ICU bed,
54:40 and the impact that healthcare has upon treatment.
54:43 Now I must say that we are still working with experts,
54:45 we're
54:46 like uh
54:47 uh Robert was saying,
54:48 we're,
54:48 you know,
54:48 we're we're all trying to catch up on this,
54:50 but
54:51 uh the experts to try and understand what some of these parameters are.
54:55 So
54:55 what the output I'm showing here is a bit more provisional
54:58 in terms of the quantitative,
55:00 um.
55:02 The qualitative results will be very robust.
55:05 What we on the top right is a plot showing
55:09 what we think you would need the
55:12 sort of the ideal ICU bed capacity,
55:15 100,000 in an unmitigated.
55:17 Scenario and that's the dashed
55:19 line
55:20 um
55:20 and the
55:22 the dashed blue line and then the dotted
55:24 um
55:25 the dotted gray line shows
55:26 what the typical
55:29 um ICU bed capacity is within an upper middle income.
55:33 In a low middle income or a low income country,
55:37 that should be much lower,
55:38 um,
55:40 er and what that,
55:41 what that will mean in terms of your,
55:43 uh,
55:44 the sort of,
55:45 The burden of these.
55:49 The pandemic in lower income settings is
55:51 that you're gonna see this big bump
55:54 in what I'm here calling preventable,
55:56 um burden.
55:57 I,
55:57 the nomenclature I think is something we're still working on.
56:00 But um,
56:00 but basically there's gonna be a lot of people who,
56:03 uh,
56:04 would
56:04 survive in a high income setting that won't survive in a low income.
56:08 For example,
56:09 uh we uh our Prime Minister is I think low to mid-fifties.
56:13 ICU
56:14 wouldn't be getting that ICU in a low income setting,
56:16 so
56:17 we'd be far more likely to have not survived,
56:19 so we think we think that will both push
56:22 the
56:23 per capita burden up
56:25 in these settings and also reduce the age at
56:28 which people are more likely to die from it.
56:35 There's also this this,
56:36 this question about comorbidities.
56:39 Now a lot of the comorbidities that have been identified are,
56:42 are clearly very important,
56:43 but they're also ones that are most prevalent in the,
56:46 the places where the,
56:47 the disease has spread,
56:48 and those are the more high income settings.
56:50 Um,
56:50 and what,
56:51 what we've been doing is looking at how that varies also by income strata.
56:55 Now,
56:56 it,
56:56 for some of the most
56:58 uh identified ones so far like uh coronary um like CBD.
57:03 Actually what you see is that there is a
57:04 tends to be lower prevalence and lower income settings.
57:07 Part of that might be lifestyle,
57:09 uh,
57:10 but another part is probably survivorship.
57:13 As you see in the ICU plot,
57:14 there are,
57:15 there are probably gonna be very few things
57:16 like heart bypasses that are made available.
57:19 So actually your survivorship with these comorbidities is probably a lot lower.
57:23 So
57:23 that might alter the severity by age,
57:26 but also there are other,
57:27 um,
57:28 and also those,
57:29 what we have,
57:30 the,
57:30 the commodates we found are very correlated with age,
57:33 so it gives you a good target for who to
57:35 shield,
57:36 and these are the older people,
57:37 they're also most likely to have
57:39 er the.
57:41 Um,
57:42 comorbidities.
57:43 So,
57:44 but in lower income settings there are a lot more potential comorbidities,
57:46 and we just don't know what impact they're gonna have.
57:49 Things like HIV infectious disease,
57:51 HIV AIDS,
57:52 uh,
57:52 I have particularly bias on malaria which I work on most of the time outside of
57:56 pandemic,
57:57 um,
57:57 other things like general things like malnutrition,
58:00 and these are far less correlated with age,
58:02 far more in younger age groups,
58:04 far more.
58:04 and
58:06 defining what a relevant comorbidity I think is is going to take some time
58:10 and understanding who is vulnerable and who needs to be protected the most.
58:14 It's gonna be a key thing to work out to mitigate
58:17 the onward impact of the pandemic as we go forward.
58:22 Yeah,
58:22 so,
58:23 Thinking about
58:25 what you can do here,
58:26 we've we've presented the mitigation scenarios,
58:28 and these mitigation scenarios are,
58:30 are things that we think would and have already
58:34 shown that they will strain healthcare systems
58:37 beyond capacity if
58:38 without suppression strategies in
58:41 Europe.
58:42 We also think,
58:42 given that that that will also be the case many times over in lower income settings,
58:47 um.
58:48 And that's leaving,
58:49 you know,
58:49 countries we anticipate with
58:51 to
58:52 a kind of a real big problem that doesn't really have an easy solution.
58:55 Either they can
58:57 reduce and go to suppress the disease,
59:00 suppress the disease and,
59:01 and hopefully their health system will not be overwhelmed,
59:03 but that has to be maintained until you get an exit strategy.
59:07 And an exit strategy might be a vaccine,
59:09 it it might be some,
59:11 you know,
59:11 high technology test or treat scenario,
59:13 but these are scenarios that
59:15 are probably less likely to be available
59:17 in lower income settings,
59:19 unfortunately.
59:20 Um,
59:21 and,
59:22 uh,
59:22 and one of the things is if,
59:23 if you suppress,
59:24 and then you go back to doing nothing,
59:26 um,
59:27 because of these sort of immunity patterns,
59:29 there won't be any build up of immunity.
59:30 You do risk
59:32 a second wave,
59:33 uh,
59:33 where,
59:34 uh,
59:34 eventually the health system will be overwhelmed,
59:36 but just at a,
59:37 at a later date.
59:39 So,
59:40 so it's a really hard trade-off,
59:41 um,
59:42 and
59:43 maybe sup suppressing for a short amount of
59:45 time and then going straight back to normal
59:47 is,
59:47 will,
59:47 is the the the type of scenario it's most likely to.
59:50 Like,
59:52 um.
59:53 Which would then risk
59:54 having similar impact as if you hadn't suppressed in the first place,
59:58 and might have a limited impact.
1:00:01 Patrick,
1:00:02 this is Roberta.
1:00:03 I just wanted to give you the warning that um we are
1:00:06 at the end of your presentation.
1:00:09 Because of
1:00:10 the
1:00:11 uh technological glitch,
1:00:14 I would like to ask you to
1:00:16 first speak very close to the microphone.
1:00:19 Second,
1:00:20 to
1:00:20 uh kindly summarize
1:00:23 the key hypothesis in a very sort of bullet point type of way.
1:00:27 And the conclusion in terms of
1:00:30 what,
1:00:30 uh,
1:00:31 these different suppression measures,
1:00:33 uh,
1:00:34 can deliver,
1:00:35 particular with particular,
1:00:37 um,
1:00:37 emphasis on low-income countries,
1:00:39 um.
1:00:41 And then,
1:00:41 um,
1:00:42 for all of those who have have been patient to hang with us on YouTube,
1:00:47 please rest assured that we will share
1:00:49 the recording of the event,
1:00:51 uh,
1:00:52 the,
1:00:52 uh,
1:00:52 slides.
1:00:53 I asked actually,
1:00:55 uh,
1:00:55 the technical team to see whether it's possible to share them already now,
1:00:59 uh,
1:00:59 so that you will have all of the material.
1:01:01 So before we move on to the next,
1:01:03 uh,
1:01:04 speaker,
1:01:04 the speakers,
1:01:05 David Wilson and Mali Jorgensen.
1:01:07 Uh,
1:01:08 please,
1:01:08 um,
1:01:08 Patrick,
1:01:09 summarize the main point,
1:01:11 um,
1:01:11 of your talk.
1:01:12 Thanks so much.
1:01:13 Yeah,
1:01:13 no problem.
1:01:14 Well,
1:01:14 I think,
1:01:14 I think basically what you've just said is kind of
1:01:16 the last point that we really had to make.
1:01:19 It's just that,
1:01:19 that the consequences of these suppression strategies need to be
1:01:23 thought through,
1:01:23 and they're gonna also be
1:01:25 particularly,
1:01:26 uh,
1:01:26 you know,
1:01:28 hard to,
1:01:29 they're gonna have impacts that are indirect and
1:01:32 loweringham saying,
1:01:32 so here this is just showing that.
1:01:34 There are a lot of countries that have much lower food security,
1:01:38 and
1:01:38 by suppressing or mitigating,
1:01:40 we could delay or
1:01:41 or extend the peak,
1:01:43 and that then can help to,
1:01:45 you know,
1:01:45 that could lead to disruption around things
1:01:47 like harvest seasons and things like that
1:01:50 that really need to be thought through and to balance out
1:01:53 both the indirect
1:01:54 direct effect of both the pandemic and and the interventions.
1:01:58 So,
1:01:59 yeah,
1:01:59 so,
1:02:00 so I'll just go through the last bit,
1:02:01 but,
1:02:01 but
1:02:02 quickly,
1:02:02 but yeah,
1:02:03 basically lots of countries are at the early stage.
1:02:06 We anticipate that they're not going to
1:02:08 stay that way.
1:02:09 um
1:02:10 we can hope that they won't,
1:02:11 but I think it's prudent to plan that they will.
1:02:13 The epidetitis will take overwhere.
1:02:14 We have got some tools that you can look at coming
1:02:17 in the next few days that I won't get onto but
1:02:19 hopefully go through.
1:02:21 But these are just our conclusions.
1:02:23 So um
1:02:25 yeah,
1:02:25 so these tools are available on our website and
1:02:28 follow,
1:02:28 follow the link and hopefully that will.
1:02:30 Give you,
1:02:31 you know,
1:02:31 what you need,
1:02:32 um,
1:02:33 but yeah,
1:02:33 so,
1:02:34 so a lot of lower and middle income countries have yet to see the mortality,
1:02:37 level of mortality that we're seeing in Europe.
1:02:39 That's probably just we think because they're primarily
1:02:41 at an early stage of the epidemic.
1:02:43 Um,
1:02:44 and we think that that all countries are very high risk of large
1:02:47 scale epidemics that are likely to
1:02:48 overstretch and probably overwhelm health systems,
1:02:52 um,
1:02:53 and that will lead to significant excessor morbidity.
1:02:57 Low middle income
1:02:58 countries,
1:02:59 the economies of them are far more
1:03:00 vulnerable to the indirect effects of interventions,
1:03:03 uh,
1:03:04 and shielding and things like that are things that
1:03:06 are gonna need to be very actively considered.
1:03:08 There's no quick,
1:03:09 easy solutions,
1:03:10 but we are gonna need to respond rapidly because I think it is going to be the next
1:03:14 2 or 3 months that will really determine
1:03:16 what happens in this pandemic.
1:03:18 I'll just add some acknowledgements,
1:03:19 but I wouldn't have had enough time to go through everyone
1:03:22 anyway,
1:03:22 we've had so much support from so many partners that
1:03:25 I didn't have time to put them all on the slide.
1:03:27 Sorry for going over time.
1:03:30 Thank you so much,
1:03:30 Patrick.
1:03:31 Uh,
1:03:31 Professor Ganni,
1:03:32 would you want to add a few uh quick
1:03:35 final conclusions that would hit
1:03:38 the intuitive mechanisms
1:03:40 of how you got these estimates,
1:03:41 and this will be also for the benefit of those
1:03:43 who didn't have the chance to see the whole presentation through
1:03:46 on YouTube.
1:03:47 Thank you.
1:03:50 OK,
1:03:50 you have a few
1:03:52 hopes.
1:03:56 Hang on,
1:03:56 um,
1:03:57 just a few concluding,
1:03:58 um,
1:03:58 remarks really.
1:03:59 Um,
1:04:00 clearly modeling's really come to the forefront during this pandemic and people
1:04:03 will have seen these models and many of them out there.
1:04:07 I,
1:04:07 I think the main important thing to bear in mind is models themselves
1:04:11 are a tool to improve our understanding.
1:04:14 Much of what we do and have done for many
1:04:16 years has been to spend a lot of time collating.
1:04:19 Um,
1:04:19 good quality epidemiological and clinical data
1:04:22 in order to parameterize the models,
1:04:24 and I think that's a very important part.
1:04:27 Um,
1:04:27 so much of the projections we're making,
1:04:29 they will change over time and they will change because we will learn more
1:04:32 about the
1:04:33 disease and the epidemic as it progresses.
1:04:36 Um,
1:04:37 so I think I just wanted to stop
1:04:39 with a few words about where we are now because,
1:04:41 uh,
1:04:41 many countries across the world,
1:04:43 of course,
1:04:43 have
1:04:44 instigated
1:04:45 quite,
1:04:45 uh,
1:04:46 severe social distancing
1:04:48 measures,
1:04:49 and we,
1:04:50 many of us are really faced with the challenge of how do we get out of this?
1:04:53 What is the appropriate exit strategy?
1:04:56 Um,
1:04:56 I,
1:04:56 there are no simple answers to this,
1:04:58 and I don't think anybody in the world really has them,
1:05:00 and I think
1:05:01 the models are useful for in terms of illustrating
1:05:04 where we could be and what
1:05:06 the challenges are with maintaining this level of suppression because
1:05:10 without some
1:05:11 other tool,
1:05:12 um,
1:05:12 in particular a vaccine,
1:05:14 it's very unlikely given the high basic reproduction number of this virus
1:05:18 that we can lift these tools,
1:05:20 um,
1:05:20 and expect to go back to normal.
1:05:22 Um,
1:05:23 and when we think about this in a,
1:05:25 a lower income countries's context,
1:05:28 I think it is important to think now,
1:05:30 um,
1:05:30 before jumping
1:05:31 into exactly the same,
1:05:33 um,
1:05:34 patterns or strategies that have been adopted in high income countries.
1:05:37 There are many.
1:05:38 Downsides
1:05:39 um to some of these suppression strategies,
1:05:41 not least the wider impacts on the health and wellbeing of societies.
1:05:45 So this is a difficult
1:05:46 um
1:05:47 area to address um and will require a wide range of specialists to think
1:05:51 more about in the coming weeks.
1:05:54 OK.
1:05:57 Thank you so much,
1:05:58 Patrick and Asra,
1:05:59 and now
1:06:00 it's the moment to turn to
1:06:02 uh David Wilson,
1:06:03 who's a
1:06:04 a director uh in uh the health,
1:06:08 nutrition and population practice of the World Bank,
1:06:10 and
1:06:11 uh our colleague Marreli Gordon,
1:06:13 who's a senior monitoring and evaluation specialist in the same global practice,
1:06:17 and that will tell us.
1:06:19 They will share reflections exactly on
1:06:22 exit strategies.
1:06:23 So this is a tough question,
1:06:25 and I appreciate
1:06:27 the humbleness of the humility of saying we don't have
1:06:31 the answers and we need to think through in advance
1:06:34 whether these tools that have been applied in rich in advanced countries
1:06:39 can be applied.
1:06:41 Successfully,
1:06:42 incredibly
1:06:43 in low income settings.
1:06:45 So thank you for now,
1:06:47 and David and Marelli,
1:06:48 the floor is yours.
1:06:49 I will have to ask you to be as concise as possible because we
1:06:52 would like to leave some time for questions before we end at 10:30.
1:06:57 A reminder for those on YouTube,
1:06:59 you can ask questions,
1:07:00 and I know that you've been shortchanged.
1:07:03 by the technology Glitch and those on Webex,
1:07:06 please ask your questions in the chat or raise your hands.
1:07:09 Thank you.
1:07:10 Over to David.
1:07:15 I think that
1:07:16 my comments flow nicely from what's been presented and I hope I
1:07:20 can compress it to about 5 minutes and claw back time for discussion
1:07:24 because I'm building on what's been said.
1:07:26 We've tried to look at some considerations for how countries could reopen,
1:07:31 and we've tried to do that with as much of
1:07:33 an orientation as possible towards lower and middle income countries because
1:07:38 so much of the literature and experience is the
1:07:42 Northern European or East Asian and has some but limited applicability,
1:07:48 and we've really identified about 7 considerations,
1:07:51 many of which have been touched on.
1:07:53 The concept of epidemic force,
1:07:56 obviously we don't see you,
1:07:58 um,
1:07:59 this might be by design,
1:07:59 you might have chosen not to show yourself,
1:08:01 but I think you can see me now now it's so much,
1:08:03 now it's so much better.
1:08:04 Thank you.
1:08:06 So we have about 7 considerations which have been touched on.
1:08:09 The notion of epidemic force,
1:08:11 obviously primarily reproduction rate,
1:08:13 but also looking at super spread of potential and also looking at the extent to which
1:08:17 overall spread is focused as it has been,
1:08:20 for example,
1:08:21 in China,
1:08:22 Italy,
1:08:23 and the US or dispersed
1:08:26 and based on those considerations,
1:08:27 it may be possible to remove lockdowns earlier or later
1:08:32 and partially or more completely.
1:08:35 We've also,
1:08:36 as people have touched on already,
1:08:37 looked closely at population health.
1:08:39 Obviously age,
1:08:41 and obviously we think that Africa's comparative youth,
1:08:44 Uganda's average age is 16.7,
1:08:46 Italy is 47,
1:08:48 Lombardy's,
1:08:48 as you know,
1:08:49 Roberta's even older.
1:08:51 We've also looked at issues such as obesity,
1:08:54 NCDs,
1:08:55 uh,
1:08:55 smoking,
1:08:56 and other risk factors,
1:08:57 which obviously are positive for Africa,
1:08:59 but might be offset by exposure to indoor cooking pollution,
1:09:03 by widespread immunocompromisation,
1:09:06 and also,
1:09:07 um,
1:09:07 potentially by malnutrition and other factors.
1:09:10 So the picture's mixed,
1:09:11 but perhaps a little bit positive.
1:09:13 We've looked closely at what we would call the capacity to take targeted measures,
1:09:17 which is difficult,
1:09:18 but it does depend a lot on countries' epidemic experience,
1:09:22 population preparedness,
1:09:23 and population.
1:09:25 Willingness to take measures
1:09:26 and I think we see that throughout countries in
1:09:29 East Asia which have managed this extremely well,
1:09:31 Japan,
1:09:32 uh,
1:09:32 Taiwan,
1:09:33 for example,
1:09:34 South Korea,
1:09:34 Hong Kong,
1:09:35 Singapore,
1:09:36 parts of China,
1:09:37 and
1:09:38 we've looked closely at
1:09:40 the concept of public health capacity,
1:09:43 which we all understand and is going to be
1:09:44 central to manage our way out of this epidemic.
1:09:47 We've had difficulty looking at health service
1:09:49 capacity because ideally we would like to have
1:09:53 both fully protected health workers,
1:09:55 safe health facilities,
1:09:57 and
1:09:58 an adequate surplus of critical care capacity.
1:10:02 The challenge we face is defining what critical care capacity
1:10:05 means in very low resource countries such as Madagascar,
1:10:08 for example,
1:10:09 who have an estimated 7 to 12 hospital beds.
1:10:12 We focus a lot on state capacity,
1:10:15 particularly decision making capacity,
1:10:17 and we do see that higher capacity states have
1:10:20 managed their way out of this better so far.
1:10:22 But we also think the role of decision making is critical.
1:10:25 We've had to elevate decision making to the highest levels to succeed.
1:10:29 We've had to be able to take data from
1:10:31 real-time digital sources,
1:10:33 from
1:10:33 modeling,
1:10:34 from economics,
1:10:35 and make very complex decisions,
1:10:37 which are probably a function of state capacity.
1:10:40 And then we've also looked at the extent to which innovations would help,
1:10:43 and the innovations that are closest to
1:10:44 hand are probably incremental innovations and testing,
1:10:48 which is still really important.
1:10:50 Potential innovations of a simplified treatment,
1:10:53 not yet at hand,
1:10:54 but potentially not too,
1:10:55 too far away,
1:10:56 and then the obvious hope of vaccines.
1:10:59 Based on these considerations,
1:11:01 we looked at the literature and found about 6 papers on how we reopen.
1:11:06 All of them are
1:11:07 for high income contexts,
1:11:09 but they still have some useful principles.
1:11:12 And
1:11:13 we then really took a consensus for principles for how you might
1:11:18 prepare a roadmap,
1:11:19 tried to see how we could tailor it for southern context,
1:11:22 and then tried to apply it to categories,
1:11:23 which I'll discuss.
1:11:25 And
1:11:26 The obvious point is if surveillance is strong
1:11:29 and cases are either very low or falling,
1:11:32 then
1:11:33 Everything else becomes easier and we can
1:11:35 move towards relaxing measures or reopening much,
1:11:39 much more quickly.
1:11:41 So everything really hinges on this,
1:11:43 and for much of the world we simply don't have the data.
1:11:46 One striking point for the World Bank is that only
1:11:49 2% of all reported cases are from IDA countries,
1:11:53 and I suspect that IDA countries do have fewer cases,
1:11:56 but I'm certain they have much fewer testing.
1:11:59 We've also focused on the role of microdata because we think
1:12:02 managing our way out of this epidemic is going to mean
1:12:05 having surveillance,
1:12:06 having testing,
1:12:07 having
1:12:08 public health case finding,
1:12:09 tracing,
1:12:10 isolation,
1:12:11 quarantine measures
1:12:13 at the local level,
1:12:15 and
1:12:15 obviously we focus on the extent to which there's adequate public health capacity
1:12:20 to try and eliminate linked cases as one criteria for countries to open.
1:12:26 What we grappled most with,
1:12:27 as I've said,
1:12:28 is health service capacity,
1:12:29 but we've tried to focus primarily on health workers being
1:12:33 completely protected and health facilities being safe for patients.
1:12:37 We're still grappling with what critical care means.
1:12:40 And based on these considerations,
1:12:43 we've tested a very simple notion of countries that
1:12:46 may already be open or could be opened very quickly
1:12:51 with limited intermittent or targeted measures.
1:12:54 We've also tried to look at places that might be able
1:12:57 to reopen in the near future with the same limited measures,
1:13:00 and in places that may actually need a lot more information or a lot more progress.
1:13:05 And this is entirely to test a concept.
1:13:08 It's in no way to provide national advice or to contradict national advice,
1:13:13 but if we're to look at countries that are
1:13:15 already open or might open with limited measures.
1:13:19 We actually have
1:13:20 a lot of countries in East Asia already in this category,
1:13:23 um,
1:13:24 countries and territories such as China,
1:13:25 Hong Kong,
1:13:26 Taiwan,
1:13:26 Singapore,
1:13:27 South Korea,
1:13:28 Japan,
1:13:28 Vietnam,
1:13:29 Australia,
1:13:29 New Zealand.
1:13:30 We think there are a cluster of countries in Europe that meet those criteria,
1:13:34 Germany and Austria who are moving towards this already,
1:13:37 the Czech Republic,
1:13:38 Slovakia,
1:13:39 and Iceland,
1:13:39 for example,
1:13:41 uh.
1:13:41 When we look at the next category of countries that
1:13:44 actually could reopen quite soon with limited and targeted measures,
1:13:49 we see quite a lot of islands who have an extra
1:13:51 ability to insulate themselves from the wider world and reopen internally.
1:13:55 And we think that,
1:13:56 uh,
1:13:57 Sri Lanka,
1:13:57 for example,
1:13:58 is a good example of a
1:14:00 lower middle income country that could reopen substantially very soon,
1:14:04 as is the smaller and more isolated island of Mauritius,
1:14:08 and potentially also promisingly the very
1:14:10 low income isolated island of Madagascar.
1:14:13 We also think there are 20 or 30 islands in the
1:14:15 Pacific who've locked down and if testing confirms low rates,
1:14:19 could potentially rely on external circuit breakers.
1:14:22 But yesterday we got a very encouraging presentation from
1:14:26 South Africa's CapriSA modeling Group given to cabinet,
1:14:30 which actually suggested South Africa is now plateauing and peaking
1:14:33 at an earlier level than expected and it's diverging from
1:14:37 the pessimistic European scenarios that they had initially used,
1:14:41 and they're showing limited community transmission.
1:14:44 Now,
1:14:44 if this holds,
1:14:45 South Africa may be an example of a continental
1:14:48 African country that could actually progressively reopen now.
1:14:52 And just to emphasize,
1:14:53 because of its very large installed TB program
1:14:56 and gene expert capacity adapted with CAF cartridges,
1:15:00 um,
1:15:00 South Africa's tested on a large scale,
1:15:02 and other African countries are also expanding testing,
1:15:05 and some of the glimmers of data we're getting are moderately positive.
1:15:09 But then finally,
1:15:10 there's a whole swathe of the world for which we
1:15:13 need much more testing data and much more progress.
1:15:16 And I would highlight our concerns about
1:15:19 much of South Asia,
1:15:21 except Sri Lanka,
1:15:22 the large populous countries of Southeast Asia,
1:15:25 and in particular Indonesia and Philippines.
1:15:29 Much of Africa for which we have very little data and much of South America.
1:15:33 And just to note that if there's a seasonal dimension,
1:15:36 winter is approaching in
1:15:38 Southern Africa and South America.
1:15:40 And
1:15:41 this is where I'm gonna really conclude by saying,
1:15:44 I think that we started in a very different place to colleagues at DEC,
1:15:48 but I think we're converging
1:15:50 because
1:15:51 the obvious point.
1:15:53 Preceding any decisions about
1:15:55 reopening is expanded testing,
1:15:57 a point that was highlighted in all of the presentations,
1:16:00 and I think if we can do that,
1:16:02 we'll have a much better idea of how we can consider reopening,
1:16:06 but I do think we're looking at very partial,
1:16:09 very intermittent,
1:16:10 uh,
1:16:11 openings with a lot of internal circuit breakers.
1:16:14 I think circuit breakers and fire stops are going to
1:16:16 be the ways that countries can try to cautiously reopen.
1:16:20 I can't underscore the stakes here.
1:16:23 Any
1:16:23 of those of us who've seen the remarkable pictures of large scale
1:16:28 protests that verge on riots coming from Mumbai
1:16:30 today will know how explosive the situation is.
1:16:33 Let me conclude there and hopefully claw back a bit of time.
1:16:37 Thank you and over.
1:16:43 Thank you,
1:16:43 David,
1:16:44 uh,
1:16:44 is Marielisa coming on after you,
1:16:46 or have you spoken for the both of you?
1:16:49 I hope we'll be online.
1:16:51 She's,
1:16:51 um,
1:16:52 presenting the Lesotho
1:16:54 project today and was in a meeting with the country,
1:16:57 so we had to shuffle our order.
1:16:59 So can I just ask to ask if Marilli made it?
1:17:02 Um,
1:17:02 otherwise,
1:17:03 Roberto,
1:17:04 are you online,
1:17:04 Marilli?
1:17:05 I am.
1:17:05 Thanks,
1:17:06 David.
1:17:06 Over to you.
1:17:07 Thank you.
1:17:07 Excellent.
1:17:08 Thank you.
1:17:08 Thanks,
1:17:08 David,
1:17:09 for the overview and to Patrick and Azra.
1:17:11 Um,
1:17:11 I know we've had many email discussions.
1:17:13 It's,
1:17:13 it's good to be connected in person,
1:17:15 hear your voices as well.
1:17:17 Um,
1:17:18 so,
1:17:18 a few points on my end about the use of mathematical modeling in this,
1:17:22 in this space,
1:17:23 maybe just to
1:17:24 tie the two presentations together.
1:17:27 Um,
1:17:27 first,
1:17:28 to say that I think we'd all agree that mathematical modeling,
1:17:31 both epidemiological and predictive analytics,
1:17:34 um,
1:17:34 have proven essential in raising awareness of the scope of the epidemic
1:17:38 to countries that have gone through it to help
1:17:40 them prepare for it and plan for shortages.
1:17:42 Um,
1:17:43 This is,
1:17:44 you know,
1:17:44 there are many things that are unprecedented,
1:17:46 but
1:17:46 the unprecedented use of models,
1:17:48 the amount of times that
1:17:50 I've seen models being discussed and model parameters being discussed
1:17:54 in,
1:17:54 in the media,
1:17:55 we,
1:17:55 we've just never,
1:17:56 never seen this before.
1:17:58 So,
1:17:58 I,
1:17:58 I think,
1:17:59 you know,
1:17:59 I would say that
1:18:01 models obviously play an important role,
1:18:04 and at the same time,
1:18:05 and I think this is a healthy thing,
1:18:06 they have received
1:18:07 scrutiny also that maybe,
1:18:09 um,
1:18:10 you know,
1:18:10 epidemiologists and infectious disease modelers have also not,
1:18:13 not seen,
1:18:14 seen before.
1:18:15 I think one of the particular areas of scrutiny has been around the use,
1:18:18 usefulness,
1:18:19 and interpretation
1:18:20 of statistical models versus
1:18:23 these um more mechanistic SIR um Infectious disease models and clearly
1:18:28 the questions um raised in the chat today reflect,
1:18:32 reflect that or not.
1:18:33 I think it is important in all of this
1:18:34 that we recognize that there are many unanswered epidemiological questions
1:18:39 that we need to,
1:18:41 um,
1:18:42 still answer
1:18:43 and that the,
1:18:44 these will be impacted on by the models and vice versa.
1:18:47 Um,
1:18:48 the asymptomatic and pre-symptomatic cases
1:18:51 proportions in the population.
1:18:53 Um,
1:18:54 duration of infectiousness,
1:18:55 duration of infection,
1:18:57 um,
1:18:58 and duration of infectiousness after infection is cleared,
1:19:01 level of immunity,
1:19:02 duration of immunity,
1:19:03 viral load,
1:19:04 and its relationship to both infectiousness and,
1:19:07 and immunity,
1:19:08 um,
1:19:08 acquired long-term immunity.
1:19:11 Um,
1:19:11 and then most importantly,
1:19:12 as David alluded to,
1:19:14 the effect of the in interventions,
1:19:16 not just nationally
1:19:17 applied interventions,
1:19:18 but granular,
1:19:19 really detailed,
1:19:21 really detailed interventions.
1:19:24 Whilst all of these,
1:19:25 there's all of this uncertainty that we have to account for,
1:19:27 policymakers have to make decisions and revisit them frequently,
1:19:31 deciding when to
1:19:33 um
1:19:34 have stay at home orders,
1:19:35 when to release them,
1:19:36 how to release them,
1:19:37 in which phases for which populations,
1:19:39 in which areas,
1:19:40 which geographic areas they can be released for which kinds of industries.
1:19:44 And
1:19:45 so,
1:19:45 these models are going to be essential for
1:19:47 this next stage of response management as well.
1:19:50 To do that well,
1:19:52 they need to be granular enough and they need to be adaptable.
1:19:55 That means
1:19:56 that
1:19:57 Whilst model choice is important,
1:19:59 what is even more important is adapting,
1:20:02 adapting,
1:20:02 adapting,
1:20:03 recalibrating these models
1:20:05 against the data that are available.
1:20:07 Um,
1:20:08 we know,
1:20:08 for example,
1:20:09 some of the model groups does this twice a week and have a schedule
1:20:12 that is published to say we will be Iterating
1:20:15 constantly,
1:20:15 um,
1:20:16 because making decisions and deciding on,
1:20:19 on Rn effective,
1:20:20 effective reproductive rates,
1:20:21 etc.
1:20:22 that is
1:20:23 affected by the intervention itself
1:20:25 is,
1:20:25 is
1:20:26 critical in,
1:20:27 in what the entire,
1:20:28 the entire model,
1:20:29 um,
1:20:29 looks like.
1:20:30 Um.
1:20:31 So,
1:20:31 whilst modeling has been useful upfront
1:20:34 in estimating the overall epidemic potential,
1:20:36 estimating,
1:20:37 therefore,
1:20:37 the
1:20:37 healthcare needs,
1:20:39 um,
1:20:40 the
1:20:41 The important need is now shifting to assessing the potential magnitude
1:20:45 of the different measures,
1:20:46 and these are measures that are going to be
1:20:48 implemented,
1:20:49 not implemented,
1:20:50 implemented,
1:20:50 not,
1:20:51 not implemented,
1:20:52 right?
1:20:52 So,
1:20:53 models
1:20:53 can and should be essential to answer a wider range of efforts,
1:20:57 a wider range of questions.
1:20:59 In that,
1:21:00 this is Roberta.
1:21:01 Let me just give you,
1:21:02 unfortunately one.
1:21:03 Minute warning because we have run
1:21:05 vastly over time today.
1:21:07 Thank you.
1:21:07 Absolutely.
1:21:08 So there's 3 things the bank is doing to help with this.
1:21:10 First of all,
1:21:10 we're co-convening with IDSI,
1:21:13 WHO and the Gates Foundation,
1:21:14 a global COVID modeling,
1:21:16 um,
1:21:17 rapid assessment effort
1:21:18 to help countries understand which
1:21:21 parameters go in and how they can better use models.
1:21:24 Secondly,
1:21:24 is we're developing
1:21:25 Social distancing and stay at home impact dashboard for every
1:21:29 country that will be available on the World Bank website.
1:21:32 And thirdly,
1:21:33 a vulnerability dashboard
1:21:35 that will help countries to understand at a granular
1:21:38 local level where are the areas of highest,
1:21:40 highest vulnerability,
1:21:41 and we hope that this will be useful
1:21:43 and impact on these models themselves.
1:21:46 Over.
1:21:47 Thanks,
1:21:47 Roberta.
1:21:51 Thank you,
1:21:51 Mali,
1:21:52 and
1:21:53 thank you everyone.
1:21:54 It's a moment,
1:21:54 we have about 8 minutes for
1:21:57 Q&A sessions with our speakers.
1:21:59 Just by way of summary,
1:22:02 uh,
1:22:02 Damien and Jed gave us a nice
1:22:06 overview of the key concepts
1:22:08 uh related to the epidemiology of uh COVID-19 and
1:22:13 an overview of the mathematical models that are out
1:22:16 there that can help us make these predictions.
1:22:19 Um,
1:22:20 Patrick and Azra
1:22:22 presented
1:22:24 the predictions of their model,
1:22:26 and just to give you a quick
1:22:27 snapshot of their conclusion,
1:22:29 they show that
1:22:31 mitigation strategy that
1:22:34 is based on partially shielding the elderly,
1:22:36 every year 60% of reduction in their social contacts and slowing down
1:22:42 without completely interrupting transmission in the general population,
1:22:45 so with a 40% reduction.
1:22:47 Would have the burden of death in the world from 40 million to 20 million.
1:22:52 They find out,
1:22:53 however,
1:22:53 that in low income countries,
1:22:55 the impacts are likely to be much stronger,
1:22:58 and a data point that I see here in their paper
1:23:02 is that in a typical low income setting,
1:23:06 the demand
1:23:07 of critical care beds would outstrip the supply by a factor of 25,
1:23:13 while this factor is only 7 for high income countries.
1:23:17 Their conclusion was that there are no easy answers,
1:23:20 and then essentially we should,
1:23:22 uh,
1:23:22 buckle up for,
1:23:23 uh,
1:23:24 long periods of measures of containment if we don't want that,
1:23:27 that the virus comes back.
1:23:30 Um,
1:23:30 David and Marlise,
1:23:31 uh,
1:23:32 have reflected on,
1:23:33 uh,
1:23:34 a sort of matrix of,
1:23:35 uh,
1:23:36 the intersection of country capacity and
1:23:40 possibility and options for.
1:23:41 For reopening.
1:23:43 Clearly this depends on
1:23:45 the epidemiology and type of spread of the virus because one
1:23:49 of their conclusions was that islands that are able to maintain
1:23:53 the spread low within
1:23:56 the country could reopen relatively
1:23:59 sooner
1:24:00 internally while still maintaining their closure of the border to avoid
1:24:05 new infections.
1:24:06 Let me start
1:24:08 the Q&A session with a question for,
1:24:10 from myself,
1:24:12 uh,
1:24:12 which is also related to one of the questions that we got
1:24:15 via YouTube from George.
1:24:18 So,
1:24:19 The question from,
1:24:21 um,
1:24:21 I believe,
1:24:22 a question to Patrick was,
1:24:24 does your model assume long-term immunity,
1:24:27 and does it consider the slim possibility that the mortality rate
1:24:31 might be overestimated because of that?
1:24:34 My related question was,
1:24:37 uh,
1:24:37 as epidemiologists,
1:24:41 What do we,
1:24:42 what do you understand about the acquired immunity to COVID once,
1:24:47 uh,
1:24:47 somebody has contracted the disease and then becomes negative?
1:24:52 Many of the
1:24:53 reasonings that are being done these days is that um
1:24:56 we hope that with antibody testing we'll be able to
1:25:00 identify who is not contagious anymore and who could be
1:25:04 safely
1:25:05 employed to interact with the vulnerable and elderly,
1:25:09 but is the immunity real?
1:25:11 So let me
1:25:12 pass it over to Patrick and Azra
1:25:14 with these initial questions,
1:25:16 and then I'll come back with an extra round.
1:25:18 Over.
1:25:21 Excuse me,
1:25:22 yeah,
1:25:22 so I'm,
1:25:22 I'm afraid,
1:25:23 uh,
1:25:23 Azra had to leave,
1:25:24 uh,
1:25:25 but,
1:25:25 um,
1:25:25 I'll do my best in her absence.
1:25:27 Um,
1:25:28 in terms of,
1:25:29 uh,
1:25:29 uh,
1:25:30 let me try and remember them in order,
1:25:31 in terms of severity,
1:25:33 um,
1:25:34 I think that that,
1:25:35 uh,
1:25:35 has been a key question because like I said at the beginning of the talk,
1:25:39 there's
1:25:39 the data that you have to parameterize that is quite limited,
1:25:42 and I think.
1:25:44 We,
1:25:44 along with a lot of the world have been waiting for some really good serology,
1:25:49 um,
1:25:49 surveys to,
1:25:50 to start coming through.
1:25:52 I think we're starting to see them and,
1:25:53 and I think the picture is um
1:25:55 broadly consistent with the level of severity,
1:25:59 um,
1:25:59 that,
1:25:59 that,
1:26:00 that was estimated.
1:26:01 Um,
1:26:02 it could still be some,
1:26:03 some
1:26:04 recalibration and readjustment that needs to happen
1:26:06 once we do some formal fitting.
1:26:08 But,
1:26:09 um,
1:26:09 but yeah,
1:26:10 that will certainly be
1:26:11 updated as and when,
1:26:12 as and when we can.
1:26:14 Um,
1:26:14 but,
1:26:14 but clearly I think,
1:26:15 I think some of the,
1:26:17 some of the,
1:26:18 uh,
1:26:19 kind of,
1:26:19 uh,
1:26:20 more
1:26:20 hopeful estimates of this idea that there might be
1:26:23 really high proportion of the population who've already been infected in London,
1:26:27 for example,
1:26:27 I think,
1:26:28 uh,
1:26:28 and in the UK I think are,
1:26:30 are,
1:26:30 are not,
1:26:31 sadly I think aren't
1:26:33 bearing fruit,
1:26:34 and then it does look like we have a way to go.
1:26:36 Um,
1:26:37 and the second is the affected population immune?
1:26:42 Oh
1:26:44 yeah,
1:26:44 oh,
1:26:44 so,
1:26:45 so that's a shame that I've just gone because I have to confess that I'm,
1:26:47 I'm not a virologist,
1:26:49 and we have a big,
1:26:50 what we do have is,
1:26:51 is a wider,
1:26:52 um,
1:26:52 group,
1:26:53 a much larger response group,
1:26:54 and also that group,
1:26:55 um,
1:26:56 we,
1:26:57 they go to,
1:26:57 um,
1:26:58 in the,
1:26:58 at least in the UK we go to a scientific advisory group,
1:27:01 uh,
1:27:01 where you have a lot of expert virologists and people like that,
1:27:04 and that's where we get our assumptions from the modeling and parameterization.
1:27:08 I think it's,
1:27:09 it's worth saying that,
1:27:10 that,
1:27:10 um.
1:27:12 But yeah,
1:27:12 it's,
1:27:12 it's,
1:27:13 it's those data again you can hopefully reparameter and could be important
1:27:17 um
1:27:18 like anything at the population level you
1:27:20 can see some levels of reinfection but then
1:27:24 the,
1:27:24 you know,
1:27:24 if they kind of outlying cases that don't necessarily
1:27:26 mean that people aren't broadly making immune responses,
1:27:29 but
1:27:30 yeah,
1:27:30 again it's,
1:27:30 it's,
1:27:31 it's,
1:27:31 it's um
1:27:32 yeah,
1:27:33 early days.
1:27:38 Thank you,
1:27:38 Patrick.
1:27:39 Let me see if anyone from Webex uh has questions or wants to raise their hand.
1:27:45 Um,
1:27:45 Alec can unmute you if you would like.
1:27:50 Yes,
1:27:50 there's a question from
1:27:51 Dimitri.
1:27:53 Um,
1:27:54 how is contact rate related to our,
1:27:56 uh.
1:27:57 Um,
1:27:58 OK,
1:27:58 let me just,
1:27:58 Dimitri,
1:27:59 would you like to,
1:28:00 I just muted you.
1:28:02 Would you like to?
1:28:03 To ask you a question?
1:28:06 Uh,
1:28:07 yes,
1:28:07 uh.
1:28:09 So,
1:28:09 I'm trying to figure out how to model this,
1:28:11 uh,
1:28:12 how to calibrate this model as well.
1:28:14 So,
1:28:14 I,
1:28:15 as I understand,
1:28:16 the contract rate is related,
1:28:18 directly related to RR0.
1:28:22 And so,
1:28:23 in the,
1:28:23 in the model,
1:28:24 I assume that uh the countercreatives are not divided by the length of the disease.
1:28:31 I,
1:28:31 I assume there is 18 days.
1:28:34 Is it a fair assumption to make?
1:28:37 Oh,
1:28:37 OK,
1:28:38 so,
1:28:38 uh,
1:28:38 I think,
1:28:39 uh,
1:28:40 well,
1:28:40 it,
1:28:40 what you,
1:28:40 what we can do is you,
1:28:42 you and I can,
1:28:43 can talk offline and I can take you through exactly every assumption we're making.
1:28:47 Um,
1:28:48 so the,
1:28:49 so what you're saying is,
1:28:51 is pretty much in a,
1:28:51 in a microcosm,
1:28:53 correct?
1:28:53 You,
1:28:53 you,
1:28:54 your,
1:28:54 your R0 is the,
1:28:56 uh,
1:28:57 is something is the rate at which you infect people per day
1:29:00 multiplied by the number of days you stay infectious.
1:29:03 So you
1:29:04 want to sort of account for how long you're infectious for,
1:29:07 um,
1:29:08 and,
1:29:09 and so what generally what you do is you,
1:29:11 there are two components that go into your R0 estimation.
1:29:14 There is the
1:29:15 exponential growth rate,
1:29:16 so sort of the doubling time,
1:29:17 so
1:29:18 a different doubling time of 3 days or 4 days or 5 days will have a different.
1:29:22 And
1:29:22 also the duration of infectivity,
1:29:24 cos that tells you the number of generations you're going through
1:29:27 within that,
1:29:28 so,
1:29:28 so
1:29:28 if you're seeing much faster growth,
1:29:31 but fewer going through fewer generations,
1:29:34 that means more infections per generation,
1:29:37 a higher R00.
1:29:38 It's getting a bit technical,
1:29:39 but,
1:29:40 but,
1:29:40 um,
1:29:41 but yeah,
1:29:41 we can,
1:29:41 we can walk through that,
1:29:42 it's with you,
1:29:43 your basic
1:29:44 in terms of your duration,
1:29:46 there's also the,
1:29:47 well,
1:29:47 at least with Rmoni you have to account for the
1:29:49 fact that not everyone makes contact at the same rate.
1:29:52 That's where our age dependent comes in,
1:29:54 and that's where,
1:29:55 yeah,
1:29:56 you,
1:29:56 you have to calibrate to account for that,
1:29:57 and that also has onwards impact or severity
1:30:00 if
1:30:01 your severity depends on age,
1:30:02 which it does,
1:30:04 um.
1:30:05 The
1:30:05 one thing is the duration of infectivity,
1:30:08 we assume a lot shorter than 18 days.
1:30:10 Now 18 days is how long it takes you to,
1:30:12 you can have if you have severe disease,
1:30:14 you can
1:30:15 experience the um.
1:30:17 Experience the.
1:30:19 That's a keyla of the disease for a long time,
1:30:22 but that doesn't necessarily mean that you are infectious and
1:30:24 it doesn't necessarily mean you're representative of all infections,
1:30:26 so
1:30:27 I think at the moment we're assuming a sort of an
1:30:29 average infectious period of somewhere in the order of 3 days,
1:30:32 but with a lot of variation.
1:30:34 Um,
1:30:35 and that depends a bit longer if you have a more severe long lasting.
1:30:39 You have to cancel the
1:30:40 much less,
1:30:41 you know,
1:30:42 less severe cases as well as the most severe.
1:30:45 Thank you.
1:30:48 Patrick,
1:30:48 and thank you for the excellent question.
1:30:50 We have arrived at 10:30 and it's time for us to conclude.
1:30:54 Um,
1:30:55 some of you have asked whether this is published and all the
1:30:58 parameters and the assumptions are available for people to work with.
1:31:02 Patrick,
1:31:02 would you want to indicate where
1:31:05 people could find all of this?
1:31:06 I understand you,
1:31:07 they are in detail.
1:31:10 Yeah,
1:31:10 there's a GitHub,
1:31:11 yeah,
1:31:11 which,
1:31:11 and we're making things,
1:31:13 yeah,
1:31:13 it's,
1:31:14 it's,
1:31:14 um,
1:31:14 yeah,
1:31:15 we're
1:31:15 going as quick,
1:31:16 but yeah,
1:31:16 the GitHub's there,
1:31:17 so that then
1:31:18 you can just recreate everything in our report.
1:31:20 I will,
1:31:21 I don't know where,
1:31:22 where's the best place to put that Roberta,
1:31:24 um,
1:31:25 the link.
1:31:27 Well,
1:31:27 we will circulate to everyone
1:31:30 the recording.
1:31:31 Yeah,
1:31:31 what we can do is that we'll circulate to everyone the recording of the event,
1:31:35 uh,
1:31:36 your presentations,
1:31:37 and with that we could probably circulate not only your paper but also the link
1:31:42 where people can,
1:31:43 uh,
1:31:43 access.
1:31:43 I remember that reading your paper that was within the paper an Excel sheet link
1:31:49 where I could
1:31:51 just
1:31:52 in
1:31:53 there and.
1:31:55 Yeah,
1:31:57 play around.
1:31:58 Yeah,
1:31:58 and just,
1:31:59 just to say that everything will be posted,
1:32:01 the recording and the documents and the presentations were
1:32:03 going to be posted in the e-seminar's website.
1:32:07 So
1:32:09 they're already some of them posted.
1:32:11 And we will have the recording,
1:32:13 um,
1:32:13 hopefully tomorrow.
1:32:16 Perfect.
1:32:17 This is excellent.
1:32:18 So I wanted to thank
1:32:19 um our speakers,
1:32:21 uh,
1:32:22 Damien De Valke,
1:32:23 Jeff Friedman from the World Bank,
1:32:25 who gave us an,
1:32:26 an overview of
1:32:28 the models that are uh available.
1:32:30 Also in their presentation,
1:32:31 you will find links to
1:32:33 the modernization of the AHME Institute,
1:32:35 the University of Basel,
1:32:37 to Patrick Walker and Professor
1:32:39 Azragani for
1:32:41 uh their overview of this influential model,
1:32:44 which is the one.
1:32:44 which we've all have based our
1:32:47 fears and sort of conclusions,
1:32:49 and thank you also for the
1:32:51 sobering and
1:32:53 humble
1:32:55 approach that you're taking to the mobilization.
1:32:58 Thank you to David Wilson,
1:32:59 Madli Gargens,
1:33:01 because
1:33:01 they helped us reflect
1:33:03 about
1:33:04 potential like these strategies,
1:33:06 and thanks to all of you for your interest,
1:33:08 enthusiasm,
1:33:09 and patience.
1:33:09 The fact that I got so many text messages about I can't listen to the.
1:33:13 Presentation,
1:33:14 I want to see it.
1:33:15 It means it was really
1:33:17 um providing an important um
1:33:20 added value for all of us
1:33:22 who are trying to think about how best to
1:33:25 suppress and stop the spread of this disease.
1:33:27 So thank you everyone,
1:33:29 and uh looking forward to the
1:33:31 uh
1:33:31 other upcoming uh great seminars that uh the Development Research Group is
1:33:36 putting together for us to understand better the impact of coronavirus.
1:33:40 Thank you very much.
1:33:41 Bye.
1:33:52 Thank you,
1:33:52 Patrick
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