00:00
Hello. In this video, we'll tackle
that all important topic
of the R nought (R0),
the basic reproduction number.
00:06
Also the R(t),
the effective reproduction number.
00:10
And from it, we'll learn about the
herd immunity threshold.
00:13
All these important topics,
and terms you probably heard
a lot about in the news lately.
00:19
We'll learn about the limitations
of the R nought,
a little bit about
how to compute it,
and learn as well about some of the
basic reproduction numbers
and herd immunity thresholds
for some of the famous diseases
throughout history.
00:32
I hope you enjoy it.
00:34
The basic reproduction number,
the R nought,
as it sometimes called R0
is used to measure
the transmission potential
of a disease.
00:45
How likely it is
to find fast penetration
through a society or population.
00:51
You see average number
of new infections
produced by an existing infection.
00:56
And the zero
in R0 and nought
refers to the fact
that at this point,
zero people are immune
in the population.
01:06
Therefore, everybody
is susceptible.
01:10
We compute the R nought
by dividing the number of new cases
by the number of existing cases
within the duration
of the subjects infection.
01:19
Now, what does that mean
duration of subjects infection?
You're infectious,
so long as you can pass
the disease on to somebody else.
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And you're no longer infectious
if you've recovered,
or if you're dead.
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So, the duration of infection
is not the same as calendar time.
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It will vary from
individual to individual.
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And technically, there are a handful
of different and exotic ways
of computing the R nought
for a population.
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But this is the general idea
of how the math is done.
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For example, if the R0 for COVID-19,
in a given population is 3,
we'd expect every existing case,
every existing person
with the disease to produce
3 new secondary infections.
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This assumes,
of course,
that everybody around
the infected people are susceptible,
and that the disease is
penetrating homogeneously
through the population,
which isn't always true.
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R0 excludes the new cases
produced by the secondary cases.
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We only care about the people
that the existing people
will directly infect.
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We don't count the people
that are subsequently infected
by the new people who are infected,
or else this goes on forever.
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We only care about
the first layer of infections.
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So, let's look at some scenarios.
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Consider if two people are infected
at the beginning of an outbreak.
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How does that look if the R0
is two, one, or less than one?
So, if R0 is 2
at the beginning of the outbreak,
we have 2 infected people,
shown here as 2 green individuals.
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At Time 1, those 2 individuals
have stopped being infectious.
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They've stopped being infectious
because they've either recovered
or they're dead.
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Neither case they're not
infecting people anymore.
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But before that happened,
they managed to infect
2 people each.
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So, now, we have a total of
4 people who are infected.
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At Time 2,
those 4 people have infected
an additional 8 people.
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But those four people are now
either recovered or dead.
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At Time 3, those 8 people
have now infected 16 people,
and the original 8
are now recovered or dead.
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And at Time 4, those 16 people
have now infected 32 individuals.
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And now the epidemic
is out of control.
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As you can see,
the R0 is 2
in 4 time intervals,
we now have
a very high number
of infected people.
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Consider now if the R0 were 1.
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So, we start out with
2 infected people,
shown here again,
with our 2 green people.
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At Time 1, those 2 people
are no longer infectious.
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They're either recovered
or they're dead,
but they have each infected
1 new people.
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So we still have 2 infected people
in the population.
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At Time 2, of those 2 people are now
either recovered or they're dead.
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They have infected
2 more people.
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At Time 3, we have 2 people.
At Time 4, we have 2 people.
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In other words,
when the R0 is 1,
we always have the same number of
infected people in the population.
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We have steady state.
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If the R0 is less than 1,
let's say 0.5.
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What happens there?
Well, at Time 0, we have
2 people again who are infected,
but on average
those 2 people infect 1 person.
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So, 0.5 each gives us 1 person.
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And the original 2 are now
either recovered or dead.
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At Time 2, that 1 person is not
capable of infecting 0.5 people
because 0.5 is not a person,
so the epidemic stops.
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If R0 is less than 1,
the epidemic goes away.
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Huge differences
in these three scenarios.
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So we watch the reproduction
number very carefully
to determine
how likely an infection is
to really take off in a population.
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So again, with the R0 is 2,
we start out with 1 person,
who gives it to 2,
who gives it to 4,
who gives it to 16,
who gives it to 32.
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It takes off quickly.
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if R0 is greater than 1,
we technically have
exponential growth,
which is a serious consideration.
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So, in this slide,
this summarizes what we know.
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If R0 is greater than 1,
the epidemic is worsening.
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If it's equal to 1,
it's holding steady.
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And if it's less than 1,
it's diminishing.
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It's affected by several factors:
Course, the rate of contacts
in the population.
05:59
So, if you can't
get access to people,
you can't infect them.
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And this is an
important observation
because that gives us
a tool to stop transmission
prevent contact,
you prevent transmission.
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What's the probability of infection
being transmitted during contact?
So, some diseases
are less likely than others
to actually find purchase
in your body,
if you're exposed to them.
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So that matters.
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And the duration
of infectiousness.
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So, the more time
you have to infect people,
the more likely you are
to infect them.
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If you're only infectious
for a few hours,
well, you probably won't encounter
a lot of people in those few hours.
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But if you're infectious
for many days or weeks,
you're going to encounter
a lot of people
and have many opportunities
to transmit this infection.