PRESENTER: --you are continuing
to stay safe and healthy
in these challenging times.
We're delighted to have members
of the University of Chicago
community join us this evening
for a very topical webinar that
is being co-hosted by
the University of Chicago
Center in Delhi and the
International Innovation Corps.
The topic is the
evolution of pandemics.
And we will hear from Anup
Malani, the Lee and Brena
Freeman Professor at the
University of Chicago Law
School and a professor at the
Pritzker School of Medicine.
Anup has conducted extensive
research, not only in
India's slums,
economics, but the health
care system over
the last few years.
And his work has got funding
by a range of organizations,
including USAID, the Gates
Foundation, Dakar Trust
and Michael and Susan
Dell Foundation.
Many of you joining
this evening know him
as a co-founder and
the faculty director
for the International
Innovation Forum,
which is a very successful
social impact fellowship
program that runs as a
part of the University
of Chicago Trust and the
Harris School of Public Policy
based at the center in Delhi.
The program brings
young professionals
to government and not-for-profit
organization for a period
of one to two years.
Today, however, Anup
will focus his research
and speak on the
work that he's doing,
which is very important
in today's challenging
times, about the pandemic--
his understanding of how
the response to the pandemic
has evolved, and how
societies are responding
to it, both in terms of public
health and the overall economy.
Anup has set up a working group
which comprises of academics
from a number of universities--
MIT, Stanford,
Duke, among those.
And this came together
when the pandemic
struck to develop models and
do empirical work to support
the response to COVID in India.
Anup and his
colleagues were also
acknowledged for their work
through the Emergent Ventures
[INAUDIBLE] in June this year.
He's a very reputable voice
in the academic and policy
circles, both in
India and in the US.
Anup, congratulations again on
the well-deserved recognition
on the topical work that you
and colleagues are engaged in.
And thank you for
taking the time
to speak today to such a
close group of the U Chicago
community members.
I will just end before I
give the screen to Anup
to say if any of you
have any questions,
please do put them
up on the chat box.
And we will try and
have Anup answer as many
of them as possible.
Thank you.
And Anup, I will be sharing
the screen with you now.
ANUP MALANI: So
Aditi, first, thank
you very much for
the opportunity
to present the work
that we're doing.
I also want to thank the
university and the Delhi Center
for the support that they
gave in Delhi and India
to be able to conduct our work.
Obviously, it's very
difficult to do this remotely.
But having a local platform
has made a big difference
both in terms of
the policy advice
and our ability do the research.
So we're extremely grateful
for that, and we thank you.
I also want to thank
Shriya, who heads up
the International
[INAUDIBLE] in India.
She has done a spectacular
job in pivoting
from the existing
work that we're
doing to support the
Ministry of Health
in their pandemic response.
And I would be very happy to
actually just turn this over
to her and have her
and her team talk
about the ways in
which the ISE has
helped the Ministry of Health--
[INAUDIBLE] and
others in the center--
to address pandemic response.
I think you'll see a
lot more of the work
that the ISE is going to do on
COVID and COVID-related relief
in the months to come.
But here, let me focus
a little bit on the work
that we've been doing
aside from the ISE.
So if you permit me, I'm going
to go ahead and share my screen
and give a short presentation.
I assume everybody
can see my screen,
so if somebody could just give
me a thumbs up to make sure?
Anybody?
PRESENTER: Yes.
Anup, you are visible.
ANUP MALANI: OK, great.
So the title of this
presentation, I think,
was set to be a little
bit grander than what I'm
going to-- than the content.
But let me tell you that I
will focus first on my research
prior to the epidemic, and
then focus on the research
during the COVID epidemic.
So first, by way of
background, as it turns out,
fortuitously, a significant
amount of my research
prior to the epidemic was
actually on epidemics,
in particular the interaction--
the role that economics
can play to help understand
behavior and outcomes
during the epidemic.
So some time ago--
nearly 15 years ago--
I began my career doing work
on antibiotic resistance.
So this is bacteria becoming
resistant to antibiotics.
And we focused on how
you can incentivize
providers and other
health care participants
to limit their
use of antibiotics
so that they don't cause a
negative externality in others,
which is that their use
causes other people not
to be able to use antibiotics
because bacteria become
resistant.
And so we talked about
how you get people
to internalize that risk.
And then separately,
over the course
of a series of papers
that continue to today,
we've looked at the
incentives that countries
have to report disease--
often, infectious disease.
And the main punchline
here is that reporting
is going to be a
function of what
the consequences
of reporting are.
So in the context
of COVID, the issue
is governments that want to not
have a lockdown, for example,
might suppress reporting
of cases or deaths.
And you can have the opposite.
If there are going to be some
assistance that comes with it,
you might have people
exaggerate the risk.
For example, if a government
is committed to lockdown
and wants to convince the
population that it's necessary,
they may actually sample
the more severe cases.
And we've seen examples
of both around the world.
In the past, I've also looked
at how you get people to--
how you incentivize
people to actually comply
with infection-control methods.
And so I have this paper
with Maciej Boni and others,
talking about how you get
chicken farmers to comply
with controls during
the avian flu outbreaks
that we saw repeatedly,
starting in 1997 in East Asia.
I've also talked about how--
one of the big issues that
comes up in the COVID epidemic
is whether or not people
are compliant with things
like masks, whether
they socially
distance more generally.
Well, whether or not they
do those things strongly
affect the epidemic.
But whether they do them
depends on how serious they
think the disease risk is.
And so we explored in a
paper about five years ago
how it is that people form
beliefs about the infection
during the epidemic.
And our application was
actually SARS in Taiwan.
And we showed that
there was a lot
of herding, a lot of
group mentality, when it
came to forming these beliefs.
And in some cases, it can
lead to-- in many cases,
it can lead to not unbiased,
but irrational beliefs.
And I can unpack
that if somebody
wants to talk about it, but
basically, herd mentality.
In the case of SARS, it
actually-- in Taiwan, actually
helped hasten the
decline of the disease.
But it can go the
other way, too,
if people think that
there's not a bigger risk.
You can actually
exacerbate the situation.
And then more
recently, I've been
working with
epidemiologists again,
trying to understand
how infections evolve.
And in this context,
we are looking
at seasonal flu and how seasonal
flu evolves and, in particular,
how vaccination affects
the evolution of flu.
Our prior going in
was that vaccination,
like antibiotic use,
would accelerate
the evolution of the flu.
And in fact, surprisingly,
our simulation
suggests the opposite happens.
And that helps explain some
of the interesting puzzles
that we've seen over the
years, the most notable being
that the US and European strains
of the flu, seasonal flu,
seem to be displaced year
after year by the East
Asian variant of the flu.
It is possible
that, for example,
differential vaccination
rates can explain that.
This can also lead
to suggestions
for different policies
for vaccination,
in particular the importance of
vaccinating around the world,
and not in particular places.
But that's a recent paper with
Sarah Kobe and Frank Wynn.
So this was the prior
research that we've done
going into the COVID crisis.
In COVID, we've basically
have been working
in five different
tracks, I would
say, to help in
efforts against COVID.
The first, obviously,
I stressed earlier,
which is the International
Innovation Corps.
In fact, the credit
goes to the IIC teams
that are there and
particularly those
that were working at
the Ministry of Health
and that were repurposed and
went into work every day,
despite the lockdown
and requirements
that everybody
else stay at home.
So they basically became
essential workers.
So that's very important.
I think that's worth a
seminar in and of itself.
So I'll focus on the
remaining portion.
The second thing
that we focused on--
second thing that
we did was based
upon the IIC's early,
early experience,
we realized it was
essential for the government
to obtain support from
the private sector.
And so I worked with Reuben
Abraham with the IDFC Institute
to organize a task force
of top leaders, which
I'll talk about in a slide.
And then we also
organized, as Aditi
said, a task force of academics
from around the world,
but particularly US and India,
to work on modeling COVID
and analyzing data about COVID.
So I'll talk a little
bit about that.
In addition, I've been involved
in some other organizations
that have tried to
take efforts on COVID.
So one is the World Economic
Forum-South Asia Action
Group on COVID, which includes
a number of prominent government
leaders and CEOs,
basically to understand
how to recover from
the COVID crisis.
And then more recently,
the Asian Development Bank
asked me to join a
high-level advisory panel
to address COVID response and
recovery in Southeast Asia.
And that has led
to some work that's
starting now on Indonesia
and some other states--
some other governments.
So let me focus on
those two task forces.
Those were items two and
three in the previous list.
So as I said, the
IDFC Task Force--
IDFC Institute is
based in Mumbai,
but everything's
virtual these days.
So this is very easy to
organize in some sense.
So we organized
a task force that
had top leaders in
business, medicine,
health care, international
organizations, and finance,
basically, again, to provide
nongovernmental broadly
construed support for the
government's response.
And the focus really was
on practical questions.
So one of the key
things that we did
is-- and this was
formed in the week
before the first
lockdown on March 24th.
So the first thing we did
was when the lockdown went
into place, a number
of essential services
had been cut.
And so we worked with--
we had a number of
logistics companies,
including organizations
such as Amazon,
that would go and call--
would organize the trucks
that would be coming in,
but then correspond with
the local police departments
to make sure that food
supplies could come through.
So that was a critical thing.
We developed an
e-pass system that
was subsequently
used and adopted
by a number of
state governments.
We had a number of
health-care professionals
on, such as Lance Pinto,
who helped develop
medical protocols,
propagate medical protocols,
especially based
on recent research,
which has been quite helpful.
We did a number
of efforts to try
to promote testing, some of
which I'm going to talk about,
as that's one of the areas that
I focused on the task force.
We also did really practical
things like in slums,
there was a shortage of
water because utilities
were cut off, too.
And the question was, when
you have a shortage of water,
how do you get people
to wash their hands?
We also needed to
spread information
in slums, where there
was really a lot of worry
early on that the epidemic
would spread very quickly.
Turned out to be a true belief,
but talking about how we could
encourage social distancing
where feasible in slums,
tell people about the
epidemic to discourage panic.
And in addition to
those practical things,
we also had
information seminars,
where we bring in
people to come speak,
whether it was about what
to do during the epidemic
or post-recovery reforms.
So how do we get the
economy back on track?
And so those are some
of the activities
that I was also engaged in.
Then on the COVID task force--
which is a separate task
force, but related in the sense
that there was a lot of
dialogue back and forth.
The COVID Task Force, as I
said, was an academic task force
that I put together with folks
such as John Gruber and Manoj
Mohanan.
John is from MIT.
Manoj is from Duke.
But we had people from
Stanford, like Ashish Goel,
Bhramar Mukherjee from
University of Michigan,
Sam Asher from Hopkins,
people from Dartmouth,
et cetera-- a number of
universities that have
come together to tackle COVID.
And what was very
interesting about this was
it was an
interdisciplinary effort
that included epidemiologists
such as Bhramar;
included economists,
such as John.
It included computer
scientists such as Ashish Goel;
physicists, such
as David Kaiser.
I mean, these are very top
minds that basically dropped
everything else they were
doing to be able to assist
with response in India.
And one of the things
that made this possible
was that we had the task force.
And so we knew a lot
of the recommendations
that we were going
to make were not
purely academic-- that they
would be taken seriously
both by governments, because of
the connections that the IDFC
Task Force had, and also by
corporations because there were
a number of CEOs, a
large number of CEOs,
on the IDFC Task Force.
Now, the task force,
the COVID task force,
actually focused on practical
questions as well, but again,
where academic or deeper
analysis was required.
And our output was mainly memos
to the different governments.
So we had four or five
different state governments
and the center
government talking to us.
And so we would often
take their questions,
write a memo in response.
In cases where we had
to speak to a number
of different
governments, we would
write op-eds that were
targeted at those governments
and a common question
that they had.
So let me focus a
little bit on testing
and get into substance
a bit, if that's OK.
So a lot of the work
we did was actually
just writing short reports.
Time was of the essence,
so we didn't convert them
at first to academic papers.
The main goal was to get a
one to three-page report back
to governments, typically
state governments.
But very often,
like I said, we'd
get a request from the center.
I won't give you the
details of the names
because one of the
challenges here obviously
is having outside organizations
support the government is
always a sensitive matter.
So I try to omit the names.
But it is very reassuring that
there is a dialogue going on
and that we do see
the responsiveness
on the part of the
government, various units--
not all of them--
but various units
to what we say.
And the way the
approach works is
that if once we're
asked a question,
we answer the question.
If we see that the other side
comes back with more questions
or is responsive, we
continue the engagement.
And that's proven to be
relatively successful for us.
So what are some of the examples
of things we did on testing?
So the first thing we
did at the very beginning
was helping states
develop testing protocols.
In fact, I would go back
a little bit before,
and at the very beginning, the
goal was how to procure tests.
And this is where the IDFC
Task Force was critical,
because we had a number of folks
that were working logistics,
such as [INAUDIBLE],, who could
help identify sources for tests
and reagents and even
PPE in other countries,
including China, and
then actually arranged
the flights for them to come
in, despite the lockdown.
So that was a really
intense period,
but that was the first step.
There was actual
action to get tests in.
But once they were
in, a lot of states
found themselves
with very few tests.
And we had to tell them, OK,
given that you very few tests,
who should you
allocate the tests to,
and what inferences should
you make from the tests.
And so there were a number
of states where we did that.
Other states that
had a lot more tests
and could actually think
about doing both track and--
contact tracing,
and could also think
about community transmission.
We tried to set up
protocols for them
to do that analysis,
that sort of testing.
An important issue that
came up in this context
is how to deal with
the quality of tests.
Very often, the
manufacturer-reported quality
differed from the quality that
was observed in the field.
Sometimes, the stuff observed
in the field was incorrectly
tested, and so we went back
to the manufacturer's results.
But other times, they were
imperfect tests, in which case
we stressed very
often that there
were known statistical formulas
that could very often correct
for the errors so long as the
prevalence wasn't too low.
And so we did a lot of
that statistical advice
early on as well.
Another thing that we
did, because we knew early
on that COVID--
that testing was difficult.
It was both difficult
to get tests, difficult
to get high-quality tests,
and it was difficult to
logistically roll out tests.
That was a very key issue
that we had deal with.
We talked about to
a number conference
how you indirectly track COVID.
Can we get it from
medical claims data
from health insurance?
What other sicknesses
can we look
at that might be masking COVID?
And this often veered
into medical protocols--
whether or not you should only
treat people that have certain
types of symptoms or do
testing before you treat them--
things like that.
So that became a big issue
that we dealt with early on.
Another thing is, once
the testing was done,
there were a number of
earlier suggesting there
was no community transmission.
And so we wrote
actually an op-ed,
but also an internal
memo, explaining to people
that when prevalence
is low, unless you
do a very large number of tests,
there is a reasonable chance
that you're going to
find zero prevalence,
even though prevalence is
positive and significant.
And so that was a warning
that we gave early on
that we could underestimate the
disease, especially the spread
in the community if we
take too seriously the zero
findings that we had early on.
Finally, we've been working
a lot with governments.
There's a difference
between the--
there's a lot of political
pressure and practical reasons
to test people that show up
at hospitals or travelers,
things like that, which is
not necessarily representative
of the population.
And so one question
is, if you can't
do representative sampling
for whatever reason,
what inferences
can you make from
non-representative sampling?
And so we've been
spending a lot of time
advising governments on
that and writing papers.
And we're beginning
some work on this--
not beginning, but
we're beginning
to write up the academic version
of some of these early papers
or memos that we wrote.
Then another important
thing is that we actually
did active surveillance in a
number of places, some of which
you may have heard of.
So the first is we worked
with the government of Bihar
in what is now a
publicly posted paper,
looking at what is the
prevalence around India
if we just test labor migrants?
So the story here
was after May 4th,
there was a release
from lockdown.
A number of labor
migrants had left cities
and returned to their rural
homes, ancestral homes.
One of the largest
recipients of this was Bihar.
They got over two
million people.
And Bihar, when we
were talking to them,
we encouraged them to do
testing, in particular,
random testing-- and they did--
of labor migrants coming in.
They also tracked
where they came from.
And so we had a
unique opportunity
to see what is the
prevalence throughout India
based upon labor migrants
that came into Bihar.
And so what we found is we had
underestimated prevalence--
and this is active
infections-- by about 2%,
2 percentage points.
And so that was
quite significant.
And we found another
very notable result was
that there was not a very high
correlation between officially
reported results and the
results of our random testing
of Bihar migrants,
meaning the correlation
between different
states' official reports
of testing positive rates and
the actual random sampling
results based upon migrants
from those states, which
led us to understand
that selection was
a big issue in-- or
non-representativeness
was a big issue in the state
sampling-- in the state
testing.
Then the second is the
Mumbai Serological Survey,
which I'll talk about a little
bit in another slide, which
is a survey where we, in
the first two weeks of July,
went out to three wards.
They're representative of the
three major zones in Mumbai--
so the city, west
suburbs, east suburbs.
We went out and we surveyed
slum and non-slum areas
in a representative sample--
geographically representative
sample.
So that was an extensive effort.
We are now beginning the
second round of that this week,
actually-- tomorrow.
And so you'll be able to
see two time points-- what
has happened to prevalence?
I think that the results of
that really surprise people.
At the same time, we
had a survey ongoing.
It's still ongoing.
It should end in about
two weeks in Karnataka,
where we went statewide and
got a representative sample.
And so we are excited to report
that in two to three weeks.
We will be looking at both
rural samples and urban samples.
We did both surveillance
for active infection
as well as antibodies, which
allows us to not only know
what is going on to the number
of infected and recovered,
but also we'll get both
recovered and future recovered,
because you can add
the two up together
to get a prediction
of what's going
to happen in terms of
the recovered population
down the road.
So that'll be, I think,
an interesting result.
And then more recently,
we've begun doing work
on trying to understand
how long-- once you have
prior exposure, and
once you've recovered,
how valuable is that immunity?
And how long does it last?
And so we're beginning a project
that's still in development
to track a bunch of
front-line workers over time--
ones that are serologically
positive and serologically
negative-- and see how their
viral loads change over time
to see if they've been exposed
to infection and whether or not
they actually get infected.
We'll also track their
antibody count and hopefully
their T cell count, too.
So we'll be able to see
when antibodies decline
and if T cell immunity
continues beyond that point.
So I want to quickly
break and just
talk to you about the
Mumbai Serological Study.
So I think people [INAUDIBLE]
this was reported out
as EMC press release and a
TIFR study in collaboration
with [INAUDIBLE] Kasturba
Hospital, TH, STI, and others.
We're co-investigators, Manoj
Mohanan and I, on this project.
And I think the thing
is that it's really,
I believe, changed the debate
in India on COVID response
because that's notable results.
And most importantly,
in slums, you
had a prevalence
that was close 57%--
serological prevalence.
That means 57% of population
tested positive for antibodies.
Assuming perfect
sensitivity for the test,
that means that 57% of the
people have already recovered
and should be in
the immunity phase.
If you account for
imperfect sensitivity,
these numbers are
actually higher.
There's a full report that
will be coming out, hopefully
within the week, on that.
Also notable is that the
non-slums which are in the same
ward actually have [INAUDIBLE]
more seroprevalance--
15%-- which I think
was a surprise
that there's a big gap
between these two areas.
This has really changed
or, I would say,
lit a fire under the--
in the literature
on modeling COVID.
People are now really
taking very seriously
that contact rates
may vary across people
and across communities.
And they can have meaningful
impacts on both the progression
of the epidemic, but also
the threshold required
to achieve herd immunity.
It has also led to very
interesting discussions
about what to do with the
lockdown going forward.
Should we allow
people from slums
to be released and mix with
the rest of the population?
Will that actually
reduce the risk
in the rest of the
population or not?
So I think those are very
important policy debates that
are going on.
So let me just return to this.
So that's the Mumbai--
that's the testing.
Now, I want to turn to modeling.
So just as important as getting
information on the disease
is knowing how to interpret
that for the purposes
of policy-making,
projections, things like that.
So the very first thing we
did when we started work here
for the COVID group,
we got a request
in from the central
government asking us,
look, we've received a
large number of studies.
And they all predict models.
And they all predict very
dramatically different results
in terms of what fraction of
the population will be infected,
what are the death rates,
and things like that.
So the very first
thing we did is
we tasked a bunch of MIT folks
to analyze all their models,
meaning we take
all the models in.
We try to replicate them,
produce the exact same results.
And we learned something
very, very important.
First is many models were
not transparent about what
they did, and so it's very
hard to even replicate them.
So they're a black box, and
they could not be assessed.
A number of models
that did reveal
things-- it's very
important that they did--
we found some errors in
the coding or differences
between what was written and
what was coded, I would say.
And that was very important.
The third thing
we found is often,
these models, in
a way that was not
entirely obvious to readers,
led to particular results
because they rigged the
models to get those results.
And that was a little
bit problematic.
The fourth thing is
that all the models--
all the models we evaluated--
we were able to evaluate--
we're very sensitive to
parameters, meaning whatever--
a model is a simulation.
You take a model that says
there are three types of people
in the population, let's
say-- susceptibles, infected,
and recovered.
People move between those three
categories in a particular way.
These are called
compartmented models.
But the rate at
which people move--
those are regulated
by parameters.
Some people call them the
contact rate, the recovery
rate, things like that.
Well, those are
critical parameters,
and you have to estimate them.
Now, if you move them a little
bit, the results in the model
can change dramatically.
And your predictions can vary
dramatically as a result.
And so the parameter
sensitivity is something
that a lot of the models
hadn't dealt with.
So that was the first
thing we came back with,
which is we said we can
explain why the models generate
different results.
And we have no reason
to think that one
is better than the other.
The key lesson that we
take away is twofold.
First is used as a
simple model as possible.
Don't try to write a complicated
model just because it's
complicated,
because if you don't
have the data to estimate those
parameters, it's [INAUDIBLE]..
The second thing is that it's
critical to constantly update
your estimates of the parameter
based upon the information
you're getting from the real
world about COVID prevalence.
And so this updating of
reproductive rates is critical.
So those are things that we did.
And when we did
our own modeling,
we incorporated
those two features.
That's the third item
on the slide, which I'll
talk about in just a second.
This is our particular
model of the epidemic
and the used dynamic
control techniques
to help guide policy
in controlling it.
Then the third thing we
did was actually, recently,
we've begun modeling
containment zones,
which is controlled
zones that are
smaller than the level of a
district, perhaps even a ward.
They're at the level
of either a building
or a block or a community.
And we tried to look at exactly
how valuable these things are.
It's a little bit complicated
because containment zones
have two effects.
On the one hand, they stop
interactions between people
inside the zone and
outside the zone, which
can be helpful if the
people inside the zone
have a little outbreak.
But the flip side
is within the zone,
it increases contact rates.
So it's accelerating
the epidemic.
And so those people are bearing
the burden of a containment
zone.
And one of the things that's
particularly confusing
beyond that is that the time
at which you release people
from the containment
zones makes a big impact.
If you release
them too early, you
will have accelerated
the number of infected.
And then if you
release too early,
you'll actually increase the
amount of infected people
that are roaming around
in the general population.
The flip side is
that if you keep them
for a long period of time,
they'll become recovered.
They become a positive
benefit to society.
And then you want to
maybe release them
so that they can
benefit everybody else.
But let me turn back
to adaptive control.
As I said, the main thing
that we do in adaptive control
is a simple model with
updating the reproductive rate,
constantly estimating
new parameters.
But the other thing
that we do is we apply
this idea of dynamic control.
In our context, that is
having gradual changes
in your social-distancing
policies,
doing that at the local level--
so district level, ward level.
And then the third thing is
changing your social distancing
gradually in
response to triggers.
And the triggers
can be something
like what is your
reproductive rate locally?
It could be what is the
trajectory of the death rate?
It could be your excess
hospital capacity.
But the key things-- define a
trigger and respond to that.
And so we did that, and we
showed through simulations--
we warn when we do
these simulations
that future projections are
very surely going to be off.
So what you want to do is
do a lot of robustness,
try a number of
different simulations
with different
results, and compare
what the performance of
different policies are.
And we consistently found
that with adaptive control,
you were going to get a lower
peak and an earlier decline
than you would
with, for example,
extending the lockdown
for a little bit
longer and then releasing fully
later or immediately releasing,
which are not really
surprising results,
if you understand the underlying
logic of these policies.
Let me stop there.
I've gone on for
quite some time.
I'm sure that there
are questions.
So again, I want to say thank
you to Aditi for hosting.
And thank you to the ISE for
making all of this possible.
PRESENTER: Thank
you, Anup, for that.
And I think that that
was just fascinating.
Every time I hear
you talk, I get
more and more impressed with all
the work that's been happening.
And whenever you do know
about the immunity survey,
I think there would be
just a lot of people
interested in knowing
because unfortunately, we
do have a lot of ISE
and U Chicago folks
that have had COVID as well.
We do have a question here
in the question box that's--
maybe it's a very long one.
Can you read that?
But just trying to understand
the difference in the modality
and severity rates,
and then why we're
seeing such a difference between
the US and Italy versus India.
ANUP MALANI: So let's
talk about mortality
because this is very much at
the front of my mind these days.
And we're about to post another
manuscript on the IFR rate
in India.
So a few things--
first, let's talk about how IFR
typically starts high and ends
up a little bit lower over time.
And I think one of the
reason that happens
is because the very
first people that we see
are people that are
in the hospital.
The very first
people that we test
are people that are
in the hospital, that
showed up with symptoms.
So in a disease where
there's both high-symptom
and low-symptom people or
symptomatic and asymptomatic
people, the first people we
see the symptomatic people.
Those people are selected
not only for being positive,
but they're selected for being
positive and being really
seriously affected.
And so if we look at
death rates early on,
they're really high
because of that.
But as we improve
our surveillance,
we begin to sample people
with less severe symptoms.
And as we do so, the mortality
rate, implied mortality rate,
typically falls.
And so we've seen
that in every country.
It's true in India as well.
A second thing that
is very interesting
is that there's heterogeneity
in the infection fatality
rate within a country
in different places.
So if you compare
the United States,
the infection fatality
rate in New York
is much higher than in the
rest of the country, even
in cities like Chicago.
If you compare the United
States, and you compare India,
India has a much lower
infection fatality rate.
So if you, for example,
take Mumbai as an example,
some back-of-the-envelope
calculations that I want to be
careful with because they rely
on official death statistics.
But if you take roughly
5,000 deaths in Mumbai,
and you divide by the fraction
of the population that
are implied to be
positive in the past
according to Mumbai's
Serological Study that we did,
you're going to get an IFR,
Infection Fatality Rate.
The benchmark we usually
use is something like 0.2%
because we think that's
what it is for flu.
Although if anybody
wants to ask about that,
that's not a very
reliable number.
But 0.2% has been
this magical number.
Well, we're getting numbers
that are closer to 0.1, maybe
slightly below 0.1%.
So that's an issue which
then primes the question--
and then, by the way, you're
seeing this trajectory down
also-- not as low,
but when we look
at the trend in the
infection fatality rate
over time as reported
by official statistics
in India, same sort of thing.
You're getting a
very low number--
below 0.5%.
Mumbai, below 0.1%.
A question is, why?
Why is India so low?
So there are three explanations
that I think we're focusing on.
The first one is age.
And so what we really need
is we need the age profile.
And we're actively
using data from a number
of places in India
now that have finally
made data available by
age to look at this.
And surely, age
explains a portion.
I don't think it
explains all of it,
because when we tried to do this
early with official Maharashtra
data, we were not able to find
that age explained everything.
But it is an important driver.
We have a young population,
and particularly, slums
have a young population.
A second theory is that there
is some cross-reactivity, which
is to say people in
India have been exposed
to coronaviruses before and
that the immunity built up
there has some imperfect but
non-zero positive benefit,
protective benefit,
against COVID.
And what that means
is that you are
likely to have lower symptoms.
You're likely to have
lower mortality rates.
So that's a possibility.
That's a very difficult
thing to investigate.
There's no studies that I know
of that have investigated this,
other than lab studies
showing there's
cross-reactivity in
tests that are targeted
at COVID versus not COVID.
And related in this
category, by the way,
are these stories
about prior BCG
vaccine, dengue exposure,
things like that.
I put that all in
that same bucket,
so some cross-reactivity.
A third possibility
is that there
are multiple strains of
the virus circulating,
and that there's high-mortality
strains and low-mortality,
or high-virulence and
low-virulence strains.
And India,
fortunately, has gotten
the low-virulence strain.
That's something
worth exploring.
We are actually trying.
One of our collaborators,
Map My Genome,
we're working with
them to try to assemble
a sample from private labs,
a number of virus samples
from around the country, to see
what's prevalent in each city.
So that's work that's in
very early stages right now.
But the idea is maybe it's
the version that's low-IFR.
These are not the
only explanations
that are out there, but
they are the big ones.
I think one thing we
should always remember
is just to say thank you.
I mean, just imagine if the
actual IFR were meaningfully
higher.
And that leads me
to another issue.
To some extent, as bad as
COVID is, it's a trial run.
SARS was the trial run
for Taiwan and Canada
and places like that.
This is India's trial run.
There's a meaningful probability
that there will be a COVID 2.0.
I don't know what
the disease will be.
But hopefully, we
will learn this time.
And we know that it's
possible because Kerala
had this exact same issue
a few years ago when
it had an epidemic.
And it responded, I think,
a little bit better.
Nipah helped it.
And hopefully, we'll
learn from COVID.
And the thing that we
want to keep in mind
is the next one may
have a higher IFR.
PRESENTER: Thank you, Anup.
There's another question
asking, "Once an excellent set
of simulations has been
generated for the government,
a lot of actions are beholden
to the machinery on ground.
Is there an element of adjusting
for the most convenient part,
that overwhelmed front-line
workers will presumably follow?
ANUP MALANI: OK, so that's
a complicated question.
So let me try this.
So first, whenever you're doing
planning, the key thing is--
so there are a few lessons.
The first one is what I call
it the "shower principle."
I have a shower.
And when I turn it on, if
the temperature's not right,
I am always tempted--
if it's too hot,
I tend to turn it down.
But the problem is it takes
some time between when
I adjust the shower and
when the water changes.
So I move it down, and
then the water's too cold.
And then I move it up,
and the water's too hot.
This lag is a big issue.
That same thing
happens with COVID.
Because the thing is,
when you set policy today,
it's going to have an impact
on cases in two weeks,
in four weeks.
So that's the issue.
So that means if you look
at what you're doing now,
you won't-- you're not
really seeing the impact.
So you might over-respond and
under-respond in the short run.
So to solve this,
what you need to do
is you need to project
out a week or two.
And I think that models
can get good enough
to be able to do that.
And there are ways
to test models
to make sure they become
better and better predictors.
Updating estimates
of RT is one way.
Back-testing is another way.
So we do these things
to try to generate
short-term model--
short-term projections.
And off that, you should
generate your policy.
Then once you
generate your policy,
the amount of social
distancing required,
it's important to
remember, I think,
that extremes are
not a great thing.
Full lockdown can go too far in
terms of restricting activity
to the point where there's
diminishing returns.
And you have huge economic
consequences, especially
for the poor.
Likewise, a full
release is a bad idea.
I think it's hard to achieve
full release because people
will voluntarily
social distance.
And you have to think
about that as well.
But I think that that's
not the right answer.
Something in the
middle is right.
However-- and here's
the big caveat--
enforcement is very,
very hard, right?
And compliance is
very, very hard.
You can't have a
complicated rule
because your police
officers are not
going to be able to enforce it.
It might be too complicated
to convey to the community,
so they can't even
be compliant with it.
So you have to stick
to simple rules.
And so when you saw all
these different litany
of things that could be open
and closed and things like that,
the first thing
you're going to think
is there's going to be
a lot of non-compliance
because it's hard for people
to know even what's allowed
and what's not allowed.
And the frequent corrections--
that's not a good idea,
either, because people
don't know which of the--
don't see the corrections.
So simple rules are
going to be critical.
And let me illustrate some of
the issues that come up here.
So we have a low--
India has low per capita
police force relative
to other countries in the world.
It's very low.
I don't know the statistic
off the top my head,
but it's on the far left of
the distribution for countries.
Now, what does that mean?
That means that police can be
quite effective at a lockdown--
a very, very severe lockdown.
Why?
Because it's very easy to find
people who are non-compliant.
If the streets are supposed
to be empty, whoever is there,
the very few people,
are all noncompliant.
And so a small number
of police officers
can enforce that sort of thing.
But then once you get to an
intermediate level of lockdown,
you say, OK, no, now, only
people in certain industries
are allowed on the
streets, or only
people of certain ages or
people without comorbidities.
Now, the problem the
limited police force has is
of the thousands of people
you see on the street,
find the small number
that are non-compliant.
That's much more difficult to
do when you have a small police
force.
And so you need to
think about that.
The extremes are somewhat easy.
Extremes are somewhat easy.
These other ones are hard.
You want to think about
rules that are easy to see.
So for example, you can
enforce mask-wearing.
So even if there's 1,000 people
on the streets, if you say,
you have to wear
masks in public,
like when you're outside of a
car, or when you're in a shop.
That is easy to do because
you can see very quickly.
So you've got to think
about enforceability
very much when you're
proposing these policies.
The other thing is that there's
a counterintuitive element
to this.
The goal with
slowing an epidemic
is to create fewer contacts.
It's not just to stop
activity or to restrict
where the activities occurs.
Restrict activities
in a way that
reduces the number of contacts.
So the classic example I
use is limiting store hours.
People think limiting store
hours reduces contacts,
and that's not true.
It often has the
opposite effect.
And the reason is if
demand doesn't fall,
if you cut the
store hours in half,
but demand is not
cut in half, then
that means all the
people are going
show up in half the hours.
You're going to double
the contact rate.
And so that's not
a very good idea.
So you need to think about
those restrictions, which
restrictions help.
In fact, counterintuitively,
it might be better
to have shops open 24/7--
to know the demand
doesn't grow dramatically,
but that the people are
spreading out their contact.
It's better to,
for example, have
limits on the number of people
that can be inside the shop,
so long as you can stop massive
queuing outside the shop.
So these are the
things that, I think,
are counterintuitive to
people, and you really
have to think about when you
think about this response.
I hope that was all responsive.
I don't know if I fully
answered the question.
PRESENTER: --Anup, but the next
question is kind of related
and, I guess, based on the
serological survey in Mumbai.
So it's showing that
57% of the population
has already been infected,
and we didn't really
see a big load coming on to the
hospitals, then the lockdown
that India had-- how
do you justify that?
I guess that's the question.
ANUP MALANI: So it's a
little bit complicated.
So I want to be--
so first, I want to point out
that it's a sensitive issue.
I think that there is a--
for some-- so I'll be
as blunt as I can be.
We have to work in collaboration
with the government.
Shriya knows this very well.
When working in collaboration
with the government,
one must think about
constructive criticism rather
than just blunt criticism.
Both people are
trying to help out.
And the goal is to try
to provide feedback
in a way that is likely to
induce the other side to take
steps that are positive.
So in that context, I
want to be a little bit
careful about lockdown.
So in some sense, the
advantage of lockdown--
I mean, I think if you think
about it, the burden of COVID
is going to be,
and the benchmark
for the burden of COVID, should
be other first-time zoonoses
events, meaning other diseases,
the first year they show up.
So the right comparison
for COVID is not
is it as bad as seasonal flu?
It should look at the
Hong Kong flu in 1968,
which is the first time
you see H3N2 come out.
That's the real comparison.
How many people died there?
How many people died here
or are likely to die here?
I think if we look at it
from that perspective,
this is a significant event.
This is not your
typical pandemic.
It's worldwide in scope.
The total number
of deaths is big.
It doesn't have to be 1918.
It doesn't have to
be historic in terms
of the fraction of the world
population, but it is very big.
It is a once-in-a-century
type situation
or once-in-a-half-century
type situation.
Given that, the
lockdown was very
helpful if used constructively.
So the key thing
to do in a lockdown
is to stop everything, gather
information, prepare supplies,
and act.
Now, I do think that that might
have been the initial goal
for the government.
But the difficulty is that
India faces logistical issues.
It doesn't have
great information
on hospital capacity
to begin with.
ISE was helping tremendously
in trying to get that up
and running.
It doesn't have great
information on surveillance.
The infrastructure
is not that great
as it is for existing
diseases, like trying
to fix that very quickly.
And also, it has a
low police force.
These are all things running
up against the government.
And so that, plus
the fact that humans
are humans, so there
ends up being politics
in a lot of this
stuff, especially
since there are differential
costs of a lockdown
in different groups of people.
I think that made
it so that we didn't
take as much advance
of the lockdown
as we could have for
reasons that we can't easily
solve, to be fair.
We could have done better,
but it's not obvious
that it would been
tremendously better.
But then once we acquire
that information,
if we could then switch from
an extreme version to something
that was more graduated.
And we did that to
some extent in May.
Perhaps it could have
done a little bit earlier.
I think we were still learning.
It would've been great if
we had better information
infrastructure to
know that we could
have eased in different places
like in rural areas before
and after.
I think that would have
been something that would
have been very helpful to do.
And now, going
forward, I think again,
getting to the core of the issue
is we want to ask ourselves,
what happened in Mumbai?
And I think the debate on
lockdown is complicated.
On the one hand,
you could say, look,
the disease would have spread
more in the non-slum areas
but for the lockdown.
And so instead of
saying 57 and 15,
maybe you would have seen
something like 45 everywhere--
something like that.
I'm making that up.
I want to be very clear.
But it's unclear what the
counterfactual would have been.
It's worth debating.
But a second time, it
does make us think,
is a continued lockdown
helpful for a few reasons.
First is, the very
low-hanging fruit
is, if you've got a very high
recovered rate in the slums,
would it be helpful
for them to circulate
in the rest of the population
so that there is actually
less probability that
any infected person
is going to interact
with a susceptible
person in the non-slums?
They're more likely on
the margin to now interact
with some recovered person?
So that's a very helpful thing.
But the other thing is
that it also tells us
that we could release--
we could reduce the economic
costs of the lockdown
on the very poor, perhaps
by releasing early.
The only thing we
need to remember,
though, is there are
other things that
are going on in
society, which is
why that's not a complete
endorsement of ending
the lockdown.
I do believe easing the lockdown
probably makes a lot of sense.
But one of the things that
we want to think about
is the following.
There's this totally separate
phenomena ongoing in society
that we need to keep in mind.
Remember the Bihar study?
So in the Bihar study,
what we learned was--
and everybody knew this.
You could read the
newspapers, and you
would see, as soon
as it became obvious
that a lockdown was
going to happen,
some people left the city to
go back to their rural homes.
But when the lockdown
happened, that stopped.
And then when we released
from lockdown on May 4th,
there was this massive exodus.
When we did our serological
survey in slums,
we saw lots of homes
that were locked up.
And we talked to community
folks, NGOs in the community,
and they said there are lots
of homes that are locked up.
Even my research projects
that were going on that
were non-COVID-related,
but linked to slums,
there was a kind of
door locks, which
tells me a lot of people left.
And so what we were
worried about initially
was that there's this
massive de-urbanization that
was going on, especially
amongst the poor, which
is problematic from a
long-term perspective.
But then, we were surprised.
People that went
back to rural areas
realized, yeah, there was
no work in the cities,
but there's also no
work in the rural areas.
And so that wasn't very good.
And I think their beliefs
on maybe COVID had changed.
At first, they
were fleeing COVID.
And now, it's not clear.
And we saw this meaningful
reverse migration point.
So we called this a
reverse-reverse migration.
And we suggested, I
think in an op-ed,
that you could actually end up
with fueling the epidemic again
in cities.
So that's the real issue,
is that if we release
from lockdown now, and we have
this massive reverse migration
that's continuing--
that is to say, reverse-reverse
coming from rural areas
of susceptibles to urban areas--
you could feed the infection
without a lockdown.
So that's the only thing
I'd want to keep in mind.
And I'd want to model
those two things out.
But strict lockdown, I
think, is a harder argument
to maintain now, given how high
a level of immunity you have.
Sorry for the long answer,
but hopefully, that
revealed a few things.
PRESENTER: You
know, Anup, I think
that's fine because that's
one of the debates that's
been happening frequently
at both groups.
So it was great to clarify that.
Anup, [INAUDIBLE]
also wants to know
how you think different
individual states in India
are doing.
ANUP MALANI: OK, before
I answer that question,
I want to point out something.
I am being purposefully vague.
It's not that we
can't get an answer,
and we can't simulate
possible alternatives.
It's that we want to be
careful about the criticism
at this point, so that we can
continue to having engagement.
And I think that that's
a healthy way to proceed.
But I can reveal some of
the analytic insights.
Now, how are the
different states doing?
Look, there's a
lot of variation.
We knew that beforehand.
You can take any
particular outcome,
there's variation in India
in terms of what is your--
in the health context.
You know, what is your
health care capacity?
What is the health
of the population?
What is the financial
situation of the state
in being able to provide for
financial support for health
conditions?
And you know, the
classic division
is between south states
and north states.
But it's actually
somewhat more complicated.
There's some
well-performing states.
Take a state like Gujarat.
It's going to perform
differently than, we'll say--
pick another state,
like Jharkhand
or something like that.
We saw a little bit of
that being replicated here.
So that's an important
source of variation.
I would say the states
that have been remarkable--
first is I would put
Kerala on the list.
Their experience with
nipah had led them
to become very, very proactive.
And I know that
they don't always
get along with the center, but
they did do a lot of things
that I think we
could learn from.
And my hope is that
India for COVID 2.0
will be a lot more like Kerala.
That would be great.
But I also want to highlight
Mumbai and Maharashtra are
a little bit complicated.
It's like the
debate in New York.
Did they do a good job
or a really bad job?
And in some sense, I want to say
if they had such a high density
and such a high burden,
you do want to pause
and say maybe they didn't
do such a terrible job.
I think the area where we could
have seen a lot more work is
trying to build up health-care
capacity, which is not--
which is tough to do.
But I think that for a country--
for a city as rich as Mumbai,
it should have been possible.
The one thing I will tell you
is that politics really makes
it hard for people to respond.
I don't like the politics.
If I could just get away
with the politics-- put away
the politics, that
would've been great.
But I do get the sense
that politics really
undermined the response.
It caused people
to pay attention
to political outcomes rather
than just COVID outcomes.
So I would put Maharashtra
somewhere in the medium grade.
I think some states that
I worry didn't take COVID
as seriously--
Telangana comes to mind--
hasn't actually experience
necessarily a huge hit.
And that might be because
they were fortuitous.
But the testing rates
were particularly low.
And that was an issue.
Some states that I don't really
blame so much because they
don't have the
capacity, and they
weren't able to get the
tests, for being a little bit
behind the curve were
places like Assam.
Just not getting access to
tests makes a huge deal,
so you can't really ramp
up your testing efforts.
Now, so that's the second issue.
A third issue is just,
if you're a rural state,
you did better than if
you're an urban state.
Any urban state was hit harder.
And that had
nothing do with you.
It had the fact
that you were dense.
And so that's going to be a
big driver of what you see.
And that has to be filtered out.
So let me stop there.
I think that that's what I would
put the state responses at.
PRESENTER: So Anup, before
I go to the other questions,
there's a question from me.
I've always wondered-- so
we saw Europe hit badly
a couple of months ago,
before we even came to India.
But then it kind of
leveled down, right?
And then they stop.
And there's barely
any case of it
in, say, Italy today,
versus our trajectory
seems to keep going up.
US kind of kept going up.
So what did Italy,
Spain, et cetera
do that just stopped
having more cases?
ANUP MALANI: First, comparing
Italy and US is a bad idea.
And the reason is because the
US is a huge population that's
all of Europe, basically.
So if you want to compare Italy,
pick a state to compare it to--
Italy versus New York State.
So that's a very
important thing,
is that you can't-- these
nation-state boundaries
for measuring COVID
is a terrible idea.
You should look at populations.
If your average population, you
want to say 40 million people--
whatever number it is--
25 million people.
You should look
at units that big.
So I don't think that's a
healthy way to compare it.
And the way to see this is
that epidemics are very local.
You can see that
in Mumbai, right?
The epidemic played
out differently
in slums and in non-slums
in the same ward.
So surely, it can
play out differently
in Maharashtra and Tamil Nadu--
picking two states.
So you should think
about it that way.
So when it's the sum
of local epidemics,
that really helps you understand
what those disease curves are.
So if I took the disease
curve for all of Europe,
it would look like
it goes up and then
stays flat for a long period
of time, then comes down.
And the reason is because it's
a series of epidemic peaks
that are happening in different
places at different times.
So it goes up and down
in one place, then
up and down in another place,
up and down in another place.
But the upper outer envelope
looks like a flatter line.
That's what the United
States data are, right?
It's different states getting
this at different times.
Florida got it much
later than New York.
And so that's the way I think
one ought to think about it.
That's also how one ought
to think about it in India.
The aggregated results--
it's of 1.4 billion people.
That's like if you took
the United States, Europe,
and Africa, and put
them all together,
and said let's look at
COVID and how it's working.
That's not very constructive.
So that's one thing
to keep in mind.
A second thing to keep in
mind is there are two--
what we care about
is social distancing.
Social distancing is effective.
So what we want to
look at is not just
official rules on
social distancing,
but private rules on
social distancing.
Sweden is the
classic example here.
There's going to be some places
where the governments are
very, very strict.
And then if they weren't
strict, the people
wouldn't have done anything.
They would go out and go to
parties and things like that.
But in other places where
even if the government weren't
strict, people would
have done that privately.
So we want to look at
those two different inputs
into social distancing.
So what I really think
is more important
is look at the level of the
epidemic in a particular place.
Look at the amount of social
distancing or precaution
that you see.
And then there are two
potential inputs into this--
official laws and
private behavior.
And that's the construct
that I use to understand.
And they're substitutes
for each other.
So you don't always need hard
rules to understand this.
A third thing that's,
I think, very important
is to understand that controls
and timing of controls
really have an impact.
So for example, let's suppose
that only rules matter.
People's private behavior-- as
soon as you release the rules,
they do everything.
In that context, when
the epidemic is going up,
and then you put a temporary
lockdown in, is you'll stop it.
It'll slow down.
It will begin to come down.
But then if you release
this, it will just
resume its normal path.
And so you'll see a second peak.
So that's something
that we have to keep
in mind-- that that's possible.
And the thing that I think
beyond that that's important
is that international
travel is going to be
a critical portion of this.
So if you're New Zealand, and
you've really controlled this,
but then you open up to
trade, or you open up
to international
tourism, and Indonesia--
I don't want to
pick on Indonesia.
Let's pick on another country,
some other country like--
India hasn't
controlled it as much.
And some tourist
comes from India.
You can re-seed the infection.
So in some sense, this is
a global phenomenon where
national controls,
as soon as you--
if you release the travel bans--
you could have a
readmission of the disease.
So in some sense, the least
common denominator country
is going to drive
the global outcomes.
The last thing I'll
say is we're still
learning about the immunology.
My prior is that there's a
big immune benefit from COVID,
not because I have great
direct data on COVID,
but because I look at the
range of other diseases
for which prior
exposure matters.
And I look at what we know
about the rate of mutation.
This isn't like seasonal
flu, as far as we know.
There's some mutation,
but maybe not as dramatic.
So my prior belief
is that you're
going to get a lot of
immunological protection
from prior exposure
and hopefully then
also the vaccine.
But the thing is, we don't know.
And so it's important to do
these studies to understand
what is the antibody profile,
what the T cell profile, what
is the reinvention risk, so
we can get a sense of how
secure we are going forward.
Actually, the most
important thing we'll learn
is what is the herd
immunity threshold?
And then after that, we'll
learn how long it'll last.
And then when we
get to vaccines,
we'll learn do we have
to have booster shots?
Do we just do it once?
Or does every
year, two years, we
have to get a booster
shot in vaccines?
I think those are
things to keep in mind.
That's going to affect how we
interpret Europe, I believe.
PRESENTER: Thank you, Anup.
I think we're at time, but
that was absolutely fantastic.
And I'm sure everybody
really enjoyed it.
I think we have a
few more questions,
and we'll try to see if you can
maybe answer them over email,
and we can get them back to
the people who asked them.
But thank you so much for this.
And whenever you do have
the next set of your survey
findings, please do we share,
so we can share them back
with everyone.
ANUP MALANI: We'd be happy to.
Thank you very
much for having me.
I appreciate it.
PRESENTER: Thank you.
ANUP MALANI: OK, bye.
