The
COVID-19
pandemic
has
been
a
period
where
time-series
of
disease
statistics,
such
as
the
number
cases
or
vaccinations,
have
intensively
used
by
public
health
professionals
to
estimate
how
their
region
compares
others
and
what
future
could
look
like
at
home.
Conventional
visualizations
are
often
limited
in
terms
advanced
comparative
features
supporting
forecasting
systematically.
This
paper
presents
visual
analytics
approach
support
data-driven
prediction
based
on
search-analyze-predict
process
comprising
multi-metric,
multi-criteria
search
method
technique.
These
supported
visualization
framework
for
comprehensive
comparison
multiple
time-series.
We
inform
design
our
getting
iterative
feedback
from
experts
globally,
evaluate
it
both
quantitatively
qualitatively.
2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct),
Год журнала:
2023,
Номер
unknown, С. 97 - 102
Опубликована: Окт. 16, 2023
Situated
Visualization
is
an
emerging
field
that
unites
several
areas
-
visualization,
augmented
reality,
human-computer
interaction,
and
internet-of-things,
to
support
human
data
activities
within
the
ubiquitous
world.
Likewise,
dashboards
are
broadly
used
simplify
complex
through
multiple
views.
However,
only
adapted
for
desktop
settings,
requires
visual
strategies
situatedness.
We
propose
concept
of
AR-based
situated
present
design
considerations
challenges
developed
over
interviews
with
experts.
These
aim
directions
opportunities
facilitating
effective
designing
authoring
dashboards.
IntechOpen eBooks,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 23, 2024
This
paper
offers
a
retrospective
history
of
the
early
development
stages
EnsembleDashVis,
visualization
dashboard
specifically
crafted
to
support
modelers
in
interpreting
simulation
model
utilized
forecast
COVID-19
trends.
The
volunteer
effort
behind
this
was
collaboratively
contributed
with
Scottish
Response
Consortium
(SCRC),
objective
enabling
an
enhanced
comprehension
complex
dynamics
pandemic
through
modeling
data
collected
by
NHS
Scotland
during
first
wave
outbreak.
chronicles
design
and
journey
system,
guided
feedback
from
domain
experts,
all
taking
place
amidst
exceptional
circumstances
unprecedented
pandemic.
outcome
work
is
streamlined
relationship
discovery
process
between
sets
input
parameters
their
respective
outcomes,
which
leverages
power
information
visual
analytics
(VIS).
We
hope
that
will
serve
as
insightful
resource
for
future
effort,
VIS
emergency
responses
promote
mutually
beneficial
engagement
scientific
communities.
BMC Infectious Diseases,
Год журнала:
2024,
Номер
24(1)
Опубликована: Дек. 1, 2024
Abstract
Mathematical
models
are
established
tools
to
assist
in
outbreak
response.
They
help
characterise
complex
patterns
disease
spread,
simulate
control
options
public
health
authorities
decision-making,
and
longer-term
operational
financial
planning.
In
the
context
of
vaccine-preventable
diseases
(VPDs),
vaccines
one
most-cost
effective
response
interventions,
with
potential
avert
significant
morbidity
mortality
through
timely
delivery.
Models
can
contribute
design
vaccine
by
investigating
importance
timeliness,
identifying
high-risk
areas,
prioritising
use
limited
supply,
highlighting
surveillance
gaps
reporting,
determining
short-
long-term
benefits.
this
review,
we
examine
how
have
been
used
inform
for
10
VPDs,
provide
additional
insights
into
challenges
modelling,
such
as
data
gaps,
key
vaccine-specific
considerations,
communication
between
modellers
stakeholders.
We
illustrate
that
while
policy-oriented
response,
they
only
be
good
them.
IEEE Transactions on Visualization and Computer Graphics,
Год журнала:
2022,
Номер
unknown, С. 1 - 11
Опубликована: Янв. 1, 2022
Computational
modeling
is
a
commonly
used
technology
in
many
scientific
disciplines
and
has
played
noticeable
role
combating
the
COVID-19
pandemic.
Modeling
scientists
conduct
sensitivity
analysis
frequently
to
observe
monitor
behavior
of
model
during
its
development
deployment.
The
traditional
algorithmic
ranking
sensitivity
different
parameters
usually
does
not
provide
with
sufficient
information
understand
interactions
between
outputs,
while
need
large
number
runs
order
gain
actionable
for
parameter
optimization.
To
address
above
challenge,
we
developed
compared
two
visual
analytics
approaches,
namely:
xmlns:xlink="http://www.w3.org/1999/xlink">algorithm-centric
visualization-assisted
,
xmlns:xlink="http://www.w3.org/1999/xlink">visualization-centric
algorithm-assisted
.
We
evaluated
approaches
based
on
structured
analysis
tasks
as
well
feedback
domain
experts.
While
work
was
carried
out
context
epidemiological
modeling,
this
are
directly
applicable
variety
processes
featuring
time
series
can
be
extended
models
other
types
outputs.
SoftwareX,
Год журнала:
2023,
Номер
unknown, С. 101416 - 101416
Опубликована: Май 1, 2023
The
COVID-19
pandemic
generated
large
amounts
of
diverse
data,
including
testing,
treatments,
vaccine
trials,
data
from
modeling,
etc.
To
support
epidemiologists
and
modeling
scientists
in
their
efforts
to
understand
respond
the
pandemic,
there
arose
a
need
for
web
visualization
visual
analytics
(VIS)
applications
provide
insights
decision-making.
In
this
paper,
we
present
RAMPVIS,
an
infrastructure
designed
range
observational,
analytical,
model-developmental,
dissemination
tasks.
One
main
features
system
is
ability
"propagate"
one
source
similar
ones,
allows
user
quickly
visualize
data.
addition
COVID
RAMPVIS
software
may
be
adapted
used
with
different
rapid
other
emergency
responses.
The
COVID-19
pandemic
has
been
a
period
where
time-series
of
disease
statistics,
such
as
the
number
cases
or
vaccinations,
have
intensively
used
by
public
health
professionals
to
estimate
how
their
region
compares
others
and
what
future
could
look
like
at
home.
Conventional
visualizations
are
often
limited
in
terms
advanced
comparative
features
supporting
forecasting
systematically.
This
paper
presents
visual
analytics
approach
support
data-driven
prediction
based
on
search-analyze-predict
process
comprising
multi-metric,
multi-criteria
search
method
technique.
These
supported
visualization
framework
for
comprehensive
comparison
multiple
time-series.
We
inform
design
our
getting
iterative
feedback
from
experts
globally,
evaluate
it
both
quantitatively
qualitatively.
