Hi. It’s me again,
Rebecca Christofferson from LSU.
In Part 1, I talked to you about arbovirology
and the multiple facets that go into transmission.
And in Part 2, I’m gonna talk to you about
how we measure that,
and how we can take laboratory experiments
and put them into a quantitative framework to sort of ask questions
about how mosquito-borne viruses will spread in certain populations.
So, this framework that I’m talking about
is vectorial capacity.
It’s a measure of the transmission potential
of vector-borne diseases, especially arboviruses.
And it is a function of viral, vector, and ecological properties.
Vectorial capacity is influenced by
any number of things, including viral genetics,
vector genetics, co-infection,
and even the microbiome, or larval and adult habitats.
Vectorial capacity is generally thought about
as the number of subsequent bites that infect people,
given that we introduced a single infected mosquito
into a naive population.
In this way, it’s somewhat analogous
to the basic reproductive number,
which is an epidemiological parameter
that we use to describe the transmissibility of a pathogen
-- any pathogen -- into a naive population.
So, vectorial capacity is... here.
This is the equation.
And m is the mosquito density,
so that’s the relative abundance of mosquitoes to people
in a certain system.
a is the daily man biting rate
of the mosquito population.
p is the probability of daily survival
for the mosquito.
And p is raised to the N,
which represents the extrinsic incubation period,
and b is the vector competence,
which is the proportion of mosquitoes that are transmitting,
given exposure,
at a certain extrinsic incubation period.
Now, in a previous talk, I introduced a concept of EIP50
to represent vector competence.
Again, EIP is the extrinsic incubation period,
and so, EIP50 is the time it takes
for 50% of the mosquitoes to become infectious,
given exposure.
And we can directly put that into the vectorial capacity framework
by setting b equal to 50%,
or 0.5,
and changing out n for E...
for the EIP50 that you then determine.
How we interpret VC,
which is vectorial capacity,
is that when vectorial capacity is less than 1,
then we’re considering this an inefficient system,
and this is not likely to support sustained transmission.
When vectorial capacity is greater than or equal to 1,
this is a system that has the potential to,
then, spark an outbreak,
or to sustain transmission.
Also in the previous talk, I showed you viral-vector interactions
and how the environment may affect these.
And that was to demonstrate that when we take into account
not only transmission time
but the lifespan of the mosquito as well,
then we sort of see how these differences in transmission
fall out.
So, here again, we have a minimum time transmission of 27 days
at lower temperature.
When we couple that with the mosquito life span of 32.4 days,
we have a total maximum of 5.5 potential days of transmission.
At a higher temperature, an optimal temperature of 28 degrees Celsius,
we have a minimum time to transmission of 14 days,
but we couple that with the average lifespan of 28.5... 28.5 days,
and we have 14.5 days of maximum potential transmission.
And so, just these little changes
in both the extrinsic incubation period
and the mosquito life traits,
this is how it falls out to affect
what we think about as transmission potential.
And so, what we wanted to do was determine trans...
if we can use the vectorial capacity equation
to sort of integrate all these mosquito life traits
that are currently not really being considered
in estimating transmissibility of an arbovirus.
So, how can we put all these into the framework?
Well, we’re gonna use a system
of Aedes aegypti and Zika to make the point.
These are colony mosquitoes from my laboratory,
which means they’ve been...
they’ve been just propagated over time
-- many, many generations in captivity --
and they’ve sort of been trained to be
really good at biting and feeding.
And then Zika virus, we're using.
So, Zika virus, as you probably know,
emerged in the Americas in 2015-2016
and sparked a really, really big outbreak.
And this is when we really attributed
the microcephaly to Zika virus.
And so Zika virus is a public health-important virus.
But the reason that we chose this system in particular
is that my colony mosquitoes are not particularly good
-- or not particularly competent --
for Zika virus.
And we wanted to take this inefficient system
and determine whether or not when we take into account
a whole lot of different mosquito life traits...
does that perception of this system change?
And so, that’s what we’re gonna talk about today.
Zika virus was first identified in the mid-20th century
in Uganda.
And in the early 21st century,
there were several outbreaks in the Pacific region.
It emerged in the Americas
into a completely susceptible population in 2015,
and so that’s why it was sort of this explosive outbreak.
It was just... everybody was available for Zika to infect.
And this was when we had, again,
the newly associated clinical manifestations
in this very large outbreak.
And again, we’re gonna Aedes aegypti
and my colony mosquitos.
So, the first thing we wanted to determine was,
when we look at the age structure of mosquitos,
does the timing of the infectious bloodmeal
-- whether they get it earlier or youn... young or old --
affect the within-mosquito kinetics of that virus?
Are the infection, dissemination, and transmission rates
different given a mosquito got the same virus at a young age
versus got it as an older mosquito?
And then, we also have this kind of two bloodmeal control,
because there’s some...
there was some evidence that multiple bloodmeals
can change the competency of a vector population.
And so, what we wanted to see was the EIP50
as well as the infection dissemination rates,
which is, does the mosquito get infected at all?,
and does that infection get out of the midgut?
And then, we also want to see, is there a difference in mortality?
So, the first question was,
does it affect mortality?
So, these are the three treatments,
and this is... these are the mortality curves.
The answer is not really.
We saw some significant difference
between the older one bloodmeal group
and the older two bloodmeal group,
but this was a difference of about...
less than half a day.
And so, when we talk about statistical significance,
we also have to talk about biological significance.
And so, in our opinion,
that half a day really wasn’t biologically relevant enough to,
like, change the way we were thinking about this mortality.
And so, what we really did was
determine that the average time to death was about 25 days.
And so, that translates to an average daily survival rate
of 0.86.
So, if you remember p being part of our vectorial capacity equation,
this is how we would usually represent it,
as 0.86, the average daily survival.
We also wanted to look at whether or not
the treatment would affect their biting habits,
and it really, really didn’t.
So, we used a new kind of method
to look at biting habits,
not really looking at biting rate per se,
but it was a good proxy for us to determine
whether an age was really just...
are they willing to bite?
Are they willing to feed?
And we found that, no, the treatments
don’t have a lot of difference,
but you can really see this really kind of
age dependence here.
So, on the x axis, I have age of the mosquitoes,
and this is just whether or not they were willing to feed.
The last thing we wanted to look at was the infection rate.
So, when a mosquito takes a bloodmeal,
it infects her midgut,
and that’s when we have infection.
After it infects the midgut,
it either gets out of the midgut and disseminates to other tissues.
That’s a disseminated infection.
And then, after that, it may get to the salivary glands,
at which point the mosquito will transmit.
And so, each of these things is a different step
in the virus getting back out of the mosquito,
and it all kind of describes the kinetics of the virus within the vector.
So, we didn’t see any difference,
as you can see, in the infection rates.
This x axis, it’s important to note,
is days post exposure.
So, it doesn’t matter what the mosquito age is.
They all were at 5, 8, and 11 days post exposure,
and there’s no difference in infection or dissemination.
When we looked at the transmission rates
and we looked at days post exposure,
we also didn’t see much difference in their transmission rates
when you looked at it as a function of days post exposure.
However, we did notice that there was
an age distribution.
We found out that the EIP50 was approximately 18 days.
And so, we’re going to then take all of this information
and put it into our vectorial capacity equation
the way that we usually do.
So, we have... we’re going...
we just set density equal to 1,
because, again, that’s a field-measured parameter.
We have a biting rate that we measured to be approximately
an average of 1.21 per day.
And then we have our average
probability of daily survival.
And our EIP50, again, goes in for extrinsic incubation.
And then we’re holding our vector competence at 50%
to accommodate our EIP50.
So, we look at that, and we put it all together,
and then we get a vectorial capacity of 0.3.
So, yay. It’s less than 1.
We’re safe. This is wonderful.
But... are we though?
We started thinking, if we look at it in an average way,
yes, we are safe.
But maybe that doesn’t give us the whole picture.
So, again, if you notice this biting rate...
it had a really pronounced age effect.
As the mosquitoes get older,
they just don’t feel like doin’ anything.
They just kind of want to hang around.
They just look at you like, no, not today.
And then, if we look at this same transmission data, right?,
as a... as a function of age, now,
and not as a function of days post exposure,
you see that the older mosquitoes
didn’t have a chance to live long enough
to get to the point where they were contributing
to the transmission cycle,
here with a 60% transmission rate.
So, how can we put that into a quantitative framework
like vectorial capacity
to sort of make this point of age structure,
and that it matters when determining viral system efficiency?
So, graphically, let’s... let’s go over it.
This is how we usually think of things.
A mosquito gets a virus,
there’s some time in between,
and then it transmits a virus.
That’s how we usually think about it.
But, what we’re talking about in... now is,
what if that mosquito got it later?
Right?
She might be less likely to bite,
or she might not live long enough to actually transmit.
Or what if she got it earlier?
Then she’s got plenty of time to transmit,
and she might actually be more likely to bite.
Or, what happens if she gets it early enough
that she bites more than once
after the extrinsic incubation period?
Or what happens if she gets it when she’s older,
and she only bites with a probability of 45%?
Because the probability of biting is not even in vectorial capacity at this point.
And then, what if she gets it really early,
and she’s just a voracious biter,
and she bites everybody all the time?
These are things that we really cannot account for
in the traditional vectorial capacity equation.
So, what we did was we took our data of predicted...
we took our data, which are the black dots,
and we fit a distribution to them,
which is the green lines,
and we got predictions of
daily probability of survival over time
as a function of mosquito age, now,
not days post infection.
We did the same thing for the biting rate
or willingness to bite.
And then we also looked at the probability of biting,
because one of the things we noticed
was that not all the mosquitoes bit all the time. S
o, there was a definite age distribution
in whether or not a mosquito even felt like biting -- if she bites.
So, we have all of these things
-- predicted probabilities over every single possible age of the mosquito.
How do we put that into the framework?
So, again, what do we usually do?
We usually account for...
the mosquito gets the virus,
there’s some average time to transmission,
and then the mosquito transmits the virus.
We have some nebulous time over here
that we assume doesn’t matter.
And then we have some nebulous time over here that doesn’t matter,
or we assume that doesn’t matter.
So, what we really wanted to look at was
changing these two nebulous times into...
this is some age at which the mosquito gets the infection,
and then this is the leftover time after the infection
that she has to transmit.
And so, we need to define those two tails
of this life cycle
to really understand what’s going on with that virus.
And the way that we did that was we took...
we decoupled things like biting rate
and probability of daily survival
from the average to...
we let it breathe over the entire distribution of the age.
So, we have, now...
if you recall, age was...
biting rate -- I’m sorry -- was squared.
That’s because biting rate has to account
for the bite where the mosquito gets the virus
and the bite where she’d transmit it.
So, what we did is we decoupled those two bites,
and we said, well,
the bite at which she gets the virus is gonna be...
the biting rate at the age at which she gets the virus
is gonna be different than the biting rate
at which she transmits the virus,
depending on the EIP
and the age of the mosquito at both times.
And we also added this parameter z,
which is the probability of her biting.
So, given that she bites or not,
what is the biting rate at her age of acquisition
versus the biting rate at the age of transmission?
And the age of transmission, now, is...
instead of being EIP,
it’s EIP plus this tail over here.
So, it becomes the age at which she acquires the virus
plus the EIP.
And now you have the age at which she transmits the virus.
We also decoupled the mortality rate,
or the average survival rate of the mosquito,
from the EIP
and made it a function of age.
And the way that we did that was we just said,
okay, we’re gonna take the daily probability of survival
at each day... so, instead of the average,
we’re gonna say she can have a probability of...
really, really high at a young age
and go all the way to really, really low, depending on the age.
And we’re gonna put that into
this really fancy math equation that’s gonna tell us,
basically, her cumulative probability of survival
at the age of transmission,
given whatever time she takes the virus.
And what that shows us is that this inefficient system
that we deemed probably wouldn’t account for a lot of transmission
is age dependent.
If the mosquito takes a bite
within the first 7 days of life
and gets the virus, this actually has
a vectorial capacity of greater than 1.
And so, what we’ve shown is that this inefficient system,
on average,
actually can contribute quite a lot to the transmission system
in an age-dependent manner.
So, aside from being really, really cool,
what’s the field relevance of this?
Because, so far, it’s really hard to
tell the age of a mosquito in the field.
But, recent reports from Aedes albopictus,
and also from Anopheles mosquitoes,
is that people are trying to develop technology,
using infrared spectroscopy,
to do just that, to age mosquitoes in the field.
And so, while this technology may not be on point
and be able to tell us the exact age of a mosquito right now,
they are working on it.
This is something people want to do.
And so, when we have this technology
and it’s super ready to go,
we now have a framework that we can take
the age distribution of a mosquito,
plug it into this VC age distribution,
and really get a good sense of what the...
what the age-structured transmission potential
for this population is.
So, where else can we use this framework?
Well... we’re gonna use it in everything,
just because that’s what we do in lab.
You make something and then you just...
you break it... just hammer it home.
So, we’re gonna use it to assess different viral phenotypes
within the mosquito population.
So, different viruses of the same species or the same type
will have different phenotypes,
which is often measured as vector competence,
which we will measure as EIP50.
So, in that case, the EIP50
will really kind of go into judging the age of transmission,
or determining the age of transmission.
We will also use it to compare across mosquito populations,
because if we use the same virus
and we look at three different populations of mosquitoes,
we might get three different transmission profiles.
And then, we can also use it to assess the role of environmental factors
such as temperature, which we’ve already talked about,
on the vector-virus system,
because temperature affects not only the viral kinetics
within the mosquito
but many of these life traits or not...
if not all of them, that we’ve talked about,
and that we’ve now put into our vectorial compacity age-structured framework.
So, what have we learned?
We learned that vectorial capacity
is a good measure of transmission
but that we need to also account
for these relevant pieces of the puzzle
to be accounted for, like life traits of the mosquito,
mortality rates, biting rates.
These things all need to go into a framework
for us to really make very nuanced comparisons.
We also learned that if a mosquito takes a bloodmeal early enough,
transmission systems that were usually deemed as inefficient
may actually have some capacity to transmit...
to contribute to transmission in a significant way.
But more importantly this also points...
this also points to our need for a better understanding
of all the temporal distributions of vector life traits,
vector-virus interactions,
and the environmental impacts on all of these things.
And there’s a lot of labs around the country, including mine,
looking at this, and I’m just excited to see
where that literature goes in the next few years.