WEBVTT

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Hi everyone. I'm Ryan Calder,

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Assistant Professor in

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the Department of
Population Health Sciences

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at Virginia Tech
here to report on

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the work by my colleagues
and myself investigating

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the interactions between
COVID-19 and the food-

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energy-water nexus.

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Both to share
findings so far and

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our opinions on priority
areas for future work.

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Very quickly in the pandemic,

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it became clear that
COVID was going

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to be experienced not only as

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an infectious disease but was

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going to be felt in many
aspects of society,

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including at the
food-energy-water nexus .

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In the first weeks and months,

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we saw headlines like these.

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People whose incomes
had been disrupted,

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unable to pay for
energy and water bills,

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and facing utility
disconnections,

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wildly forecasted
blackouts due to higher

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than usual residential
occupancy in the summer months.

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To our knowledge,
this didn't really

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come to pass because there
were, in most cases,

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other factors that led to
net decreases in demand

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and so transmission
systems were able

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to weather that stress.

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Early in the pandemic when
there was a lot of uncertainty

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around principal
transmission pathways,

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hand washing was very
heavily promoted as

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a health intervention and it

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became clear that in

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very water-stressed
parts of the world,

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there was a real
trade off between

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vastly increasing the frequency

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of hand-washing and
water availability

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for other purposes, including
agricultural purposes.

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This is in addition, of course,

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to the widely
publicized outbreaks

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in meat processing plants

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and other industrial
food facilities.

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This has had really bad
impacts for food workers,

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of course, and their
health, to some extent,

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is everyone's health because

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food workers are
responsible for maintaining

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the integrity of the
food system and for

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ensuring nutritional sufficiency

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in the general population.

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When COVID hit, we
had recently begun

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a SESYNC pursued under
the leadership of

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Rebecca Muenich from Arizona
State and Rebecca Hale from

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Idaho State with the goal of

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developing a typology of food,

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energy, water stresses across

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the United States in order

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to more systematically identify

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social and other drivers of

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those stresses and to
identify opportunities for

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policy transfer based on

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commonalities in
physical systems at

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play and other factors.

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So when COVID struck,

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we decided to devote a
fraction of our effort to

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understanding the impacts on

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the food-energy-water system.

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Understandably, the
vast majority of

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public health
attention has focused

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on the spread of COVID-19

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and the impact of COVID-19
infection on people's health,

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so fatalities, hospital
admissions, and so on.

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Conversely, we
wanted to understand

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how COVID-19 disrupted food,

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energy, water processes,

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and how food, energy,

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and water insecurities affected

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health outcomes as an
indirect impact of

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COVID-19 or at least

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inventory what research was
ongoing in that direction.

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Finally, we wanted to understand
how the food, energy,

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water nexus was in turn

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accelerating or attenuating
the spread of COVID-19.

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For instance, we already
saw that example

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of outbreaks in meat
processing plants.

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Graphical models
are being used in

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more and more fields to justify
decision-making by being

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explicit about assumptions about

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the natural or social variables

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that drive some
system of interest,

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and they go by different names,

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such as theory of change,

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pathways of change,
logic models, etc.

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They're very useful tools,

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in my opinion, for organizing,

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thinking about
what are sometimes

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very complex systems around
key causal dependencies.

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We set out to make a
graphical model for

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COVID-19 and the interaction
with the food, energy,

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water nexus by scraping
journal articles and

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news and media articles that

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had come out in
roughly the first year

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of the pandemic by coding

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the causal
relationships asserted

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or studied by other authors

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and then by identifying

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system dynamics that
were either observed in

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COVID-19 or could emerge in

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a future pandemic if
certain variables changed,

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for instance, if
a future pandemic

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had much worse impacts
on working-age people,

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for instance, than COVID-19 did.

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This actually applied
methods that we developed

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previously to make unified
graphical models out

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of disparate research by

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authors working at different
levels of detail in

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different settings and so on.

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One of the big
challenges of making

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comprehensive graphical models
that integrates research

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carried out at
different levels of

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spatial and temporal detail is

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that as the relationships you

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represent become more
mechanistically explicit,

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that's what we're representing
here with network depth,

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the number of concepts,

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the number of individual
relationships

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represented increases
exponentially,

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which starts to pose
a real barrier to

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interpretability for
decision-making or

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usability of the model.

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So there's a trade-off between

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mechanistic detail and
usability in some sense.

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We describe that in more
detail in this paper

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with the full citation details
on the previous slide.

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Pretty early on in our work,

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we figured out that there were

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only so many lessons to be
drawn from previous pandemics.

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Even though the 20th century had

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at least three major respiratory
virus pandemics with

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direct death tolls comparable

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to COVID-19 effects on
other parts of society,

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including effects on what
today we would call the food-

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energy-water nexus,
were very different.

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For starters, the most recent

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major respiratory virus pandemic

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prior to COVID-19,

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the H3N2 outbreak of
1968-69 occurred at

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a time when many of

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the interventions that we've
taken during COVID-19,

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such as widespread work from
home and social distancing,

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were not as feasible.

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A lot of the worst-case
outcomes that we

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as a society had been working so

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diligently to avoid in COVID-19,

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such as hospitals
running out of capacity,

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did indeed occur in
the context of H3N2,

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and they seem to
have been viewed

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with a greater amount
of inevitability or

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maybe fatalism than we

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would seem to find

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acceptable today in the
context of COVID-19.

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Newspaper articles from this
era are very interesting

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and illustrative of difference

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in attitude around
those outcomes.

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Second, the
food-energy-water-space

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and the food system
in particular today,

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are much more susceptible
to disruptions

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from outbreaks than they were

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even in the '60s because of

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the really substantial
centralization and

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consolidation of food production

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into a smaller number
of physical locations.

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That has really reduced

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the redundancy and the
resiliency of the food system,

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in particular, the
viral pandemics.

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I would argue that that has

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both increased the
indirect impacts of

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viral pandemics on human health

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by creating the supply side
risks for food security,

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and has also made
interventions like

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social distancing and lock downs

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of non-essential workers.

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More important now than
maybe they were in

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the '60s because
food-energy-water infrastructure

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seems to be more vulnerable

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to breakdown from
viral outbreaks.

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These differences are really

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important because as we'll see

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several of the major impacts of

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COVID-19 on the
food-energy-water

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space are not due to morbidity
and mortality per se,

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but rather due to
economic disruptions,

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especially in the early
months of the pandemic,

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when certain sectors of
the economy were suddenly

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closed without much of

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a follow-up plan and

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how to support workers in
the immediate aftermath.

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This work culminated in a
paper that came out in E,

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S, and T letters in July.

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This schematic shows how we
represent it at a high level,

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the system dynamics that
we haven't covered.

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First, of course, COVID-19
directly affects human health

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via viral infection and spread,

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but it also affects labor inputs

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to the food-energy-water system.

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For example, when
workers get sick or when

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consumers whose
incomes have been

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disrupted can't make
utility payments which

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have affected energy and water
sectors particularly hard.

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Meanwhile, risks to the
food-energy-water system

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from supply-side breakdowns

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and demand-side barriers.

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Also heavy human health
impact, unfortunately,

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particularly among the
very people who are

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also most at risk from COVID-19,

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and those interactions are
not very well understood.

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Finally, like we saw

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earlier large
centralized industrial

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food operations have played

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a major role in

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spreading COVID-19 and keeping
the whole thing going.

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This is a more granular
representation

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of causal relations with

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the solid lines reflecting
relationships that we

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did identify in the
peer-reviewed literature,

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and dashed lines representing

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relationships that
we hypothesized,

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but didn't identify
where that we've found

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alluded to in news
and media articles,

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but didn't find investigated

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in peer-reviewed
articles so far.

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The pluses represent
positive associations

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and the minuses represent
negative associations.

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Our overarching interest
in constructing

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this framework was
in understanding how

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the net impacts of burden
of disease of all of

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these interacting processes and

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whether the system
dynamics and the

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food-energy-water spaces

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tend to attenuate or
amplify these impacts.

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We use this framework
to identify

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feedbacks and trade-offs and
potential gaps and research

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needs that we go into in
greater detail in the article.

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Then I'll have time
to do in this talk.

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I guess also the advantage of

00:11:36.160 --> 00:11:37.510
this pre-recorded video is

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that people who
are really curious

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and the nuts and
bolts of this can

00:11:41.455 --> 00:11:45.100
pause and zoom in
before moving on.

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One of our biggest observations.

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This is probably no
surprise is that

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COVID-19 has presented us with

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many trade-offs
for which there is

00:11:54.610 --> 00:11:58.120
actually very little
quantitative modeling capacity.

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For example, water
use optimization

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between hand-washing
and other things.

00:12:03.385 --> 00:12:05.620
For COVID-19,
hand-to-hand transmission

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turned out not to be a
very important pathway,

00:12:07.855 --> 00:12:10.390
but a future disease might make

00:12:10.390 --> 00:12:13.405
that optimization
exercise more urgent,

00:12:13.405 --> 00:12:16.120
or a more widely discussed
trade-off around

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mental health impacts of
extended physical distancing,

00:12:18.910 --> 00:12:21.235
particularly among
populations with

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lower case fatality rates.

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This is not a
food-energy-water thing,

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but it's probably
the most urgent.

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To our knowledge, there is
no quantitative analysis

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of the net impacts on
different populations.

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This is partially due to

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a lack of data so
far, but hopefully,

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as more data are reported,

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more quantitatively, explicit
comparisons can be drawn.

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In COVID-19,

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to a large extent,

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I would argue that
we have been lucky

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that case fatality rates among

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younger people have been quite

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low because

00:12:55.680 --> 00:12:59.000
probably averted major
supply-side breakdowns.

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That was not at all
guaranteed to be the case.

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If you compare
COVID-19, for example,

00:13:04.990 --> 00:13:09.085
to the 1918 H1N1 pandemic,

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the relative case fatality rate

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between younger people and

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older people was almost
exactly opposite.

00:13:17.740 --> 00:13:19.495
In the United States,

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case fatality rate among
18-29 year-olds was about 130

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times lower than those
among 65-74 year-olds.

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This is, of course,
prior to vaccination.

00:13:31.090 --> 00:13:35.590
In the H1N1 pandemic of 1918,

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it was the opposite with

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case fatality rates
among 15-44 year-olds,

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120 times higher than
among over 65 year-olds.

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It is alarmingly easy to

00:13:53.020 --> 00:13:56.845
imagine a much worse
outcome on the supply side.

00:13:56.845 --> 00:13:58.540
This led us to ask,

00:13:58.540 --> 00:14:01.510
what would the effects on
the food-energy-water nexus

00:14:01.510 --> 00:14:03.280
be if COVID-19 had looked more

00:14:03.280 --> 00:14:05.965
like the 1918 H1N1 pandemic,

00:14:05.965 --> 00:14:08.365
at least in terms
of age effects?

00:14:08.365 --> 00:14:10.480
The food system in particular,

00:14:10.480 --> 00:14:12.550
is much more
vulnerable than it was

00:14:12.550 --> 00:14:15.010
to viral outbreaks given

00:14:15.010 --> 00:14:16.840
the consolidation
of activities in

00:14:16.840 --> 00:14:20.170
a smaller number of physical
locations like we saw.

00:14:20.170 --> 00:14:25.180
However, existing
food-energy-water models

00:14:25.180 --> 00:14:26.950
have very little ability

00:14:26.950 --> 00:14:33.430
to evaluate the effect
of labor shortfalls.

00:14:33.430 --> 00:14:38.710
That seems like a major gap
that needs to be bridged in

00:14:38.710 --> 00:14:44.395
order to build resiliency
against future pandemics.

00:14:44.395 --> 00:14:46.270
With that, I would like to thank

00:14:46.270 --> 00:14:48.610
my co-authors and collaborators.

00:14:48.610 --> 00:14:53.110
The maroon box is the
co-authors on the

00:14:53.110 --> 00:14:54.880
E, S, and T letters paper.

00:14:54.880 --> 00:14:56.530
The orange box is

00:14:56.530 --> 00:14:57.970
the broader SESYNC team

00:14:57.970 --> 00:15:00.550
that I've been working
with since 2019.

00:15:00.550 --> 00:15:03.430
Rebecca Hale and Becca
Meunich, like I say,

00:15:03.430 --> 00:15:10.450
are the PIs on the SESYNC team
without whose leadership,

00:15:10.450 --> 00:15:13.310
this work would
not have occurred.

00:15:13.710 --> 00:15:17.740
This is where in normal life
I would take questions,

00:15:17.740 --> 00:15:19.510
but instead here
I'll invite you to

00:15:19.510 --> 00:15:21.565
email me or track me down

00:15:21.565 --> 00:15:26.810
at AGU in New
Orleans. Thank you.