Citation
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010

Material Information

Title:
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010
Creator:
Stewart-Ibarra, Anna M.
Muñoz, Angel G.
Ryan, Sadie J.
Ayala, Efrain Beltran
Borbor-Cordova, Mercy J.
Finkelstein, Julia L.
Publisher:
BioMed Central (BMC Infectious Diseases)
Publication Date:
Language:
English

Subjects

Subjects / Keywords:
Dengue ( jstor )
Climate models ( jstor )
Rain ( jstor )

Notes

Abstract:
Background: Dengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS. Methods: The following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran’s I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak. Results: We found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak. Conclusions: Our findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk. Keywords: Dengue fever, Aedes aegypti, GIS, Social-ecological, Climate, Spatial, Temporal, Wavelet analysis, Ecuador, Early warning system
General Note:
Stewart-Ibarra et al. BMC Infectious Diseases 2014, 14:610 http://www.biomedcentral.com/1471-2334/14/610; Pages 1-16
General Note:
doi:10.1186/s12879-014-0610-4 Cite this article as: Stewart-Ibarra et al.: Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010. BMC Infectious Diseases 2014 14:610.

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University of Florida
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University of Florida
Rights Management:
© 2014 Stewart-Ibarra et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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RESEARCHARTICLEOpenAccessStewart-Ibarraetal.BMCInfectiousDiseases2014,14:610http://www.biomedcentral.com/1471-2334/14/610RESEARCHARTICLEOpenAccessStewart-Ibarraetal.BMCInfectiousDiseases2014,14:610http://www.biomedcentral.com/1471-2334/14/610
Spatiotemporal
clustering,
climate
periodicity,
and
social-ecological
risk
factors
for
dengue
during
an
outbreak
in
Machala,
Ecuador,
in
2010


Anna
M
Stewart-Ibarra1*,
�ngel
G
Mu�oz2,3,
Sadie
J
Ryan1,4,5,
Efra�n
Beltr�n
Ayala6,7,
Mercy
J
Borbor-Cordova8,
Julia
L
Finkelstein9,10,
Ra�l
Mej�a11,
Tania
Ordo�ez6,
G
Cristina
Recalde-Coronel8,11
and
Keytia
Rivero11



Abstract


Background:
Dengue
fever,
a
mosquito-borne
viral
disease,
is
a
rapidly
emerging
public
health
problem
in
Ecuador
and
throughout
the
tropics.
However,
we
have
a
limited
understanding
of
the
disease
transmission
dynamics
in
these
regions.
Previous
studies
in
southern
coastal
Ecuador
have
demonstrated
the
potential
to
develop
a
dengue
early
warning
system
(EWS)
that
incorporates
climate
and
non-climate
information.
The
objective
of
this
study
was
to
characterize
the
spatiotemporal
dynamics
and
climatic
and
social-ecological
risk
factors
associated
with
the
largest
dengue
epidemic
to
date
in
Machala,
Ecuador,
to
inform
the
development
of
a
dengue
EWS.


Methods:
The
following
data
from
Machala
were
included
in
analyses:
neighborhood-level
georeferenced
dengue
cases,
national
census
data,
and
entomological
surveillance
data
from
2010;
and
time
series
of
weekly
dengue
cases
(aggregated
to
the
city-level)
and
meteorological
data
from
2003
to
2012.
We
applied
LISA
and
Moran�s
I
to
analyze
the
spatial
distribution
of
the
2010
dengue
cases,
and
developed
multivariate
logistic
regression
models
through
a
multi-model
selection
process
to
identify
census
variables
and
entomological
covariates
associated
with
the
presence
of
dengue
at
the
neighborhood
level.
Using
data
aggregated
at
the
city-level,
we
conducted
a
time-series
(wavelet)
analysis
of
weekly
climate
and
dengue
incidence
(2003-2012)
to
identify
significant
time
periods
(e.g.,
annual,
biannual)
when
climate
co-varied
with
dengue,
and
to
describe
the
climate
conditions
associated
with
the
2010
outbreak.


Results:
We
found
significant
hotspots
of
dengue
transmission
near
the
center
of
Machala.
The
best-fit
model
to
predict
the
presence
of
dengue
included
older
age
and
female
gender
of
the
head
of
the
household,
greater
access
to
piped
water
in
the
home,
poor
housing
condition,
and
less
distance
to
the
central
hospital.
Wavelet
analyses
revealed
that
dengue
transmission
co-varied
with
rainfall
and
minimum
temperature
at
annual
and
biannual
cycles,
and
we
found
that
anomalously
high
rainfall
and
temperatures
were
associated
with
the
2010
outbreak.


Conclusions:
Our
findings
highlight
the
importance
of
geospatial
information
in
dengue
surveillance
and
the
potential
to
develop
a
climate-driven
spatiotemporal
prediction
model
to
inform
disease
prevention
and
control
interventions.
This
study
provides
an
operational
methodological
framework
that
can
be
applied
to
understand
the
drivers
of
local
dengue
risk.


Keywords:
Dengue
fever,
Aedes
aegypti,
GIS,
Social-ecological,
Climate,
Spatial,
Temporal,
Wavelet
analysis,
Ecuador,
Early
warning
system



*
Correspondence:
stewarta@upstate.edu
1Department
of
Microbiology
and
Immunology,
Center
for
Global
Health
and
Translational
Science,
State
University
of
New
York
Upstate
Medical
University,
750
East
Adams
St,
Syracuse,
NY
13210,
USA
Full
list
of
author
information
is
available
at
the
end
of
the
article
�
2014
Stewart-Ibarra
et
al.;
licensee
BioMed
Central
Ltd.
This
is
an
Open
Access
article
distributed
under
the
terms
of
the
Creative
Commons
Attribution
License
(http://creativecommons.org/licenses/by/4.0),
which
permits
unrestricted
use,
distribution,
and
reproduction
in
any
medium,
provided
the
original
work
is
properly
credited.
The
Creative
Commons
Public
Domain
Dedication
waiver
(http://creativecommons.org/publicdomain/zero/1.0/)
applies
to
the
data
made
available
in
this
article,
unless
otherwise
stated.



Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
2
of
16
http://www.biomedcentral.com/1471-2334/14/610


Background


Dengue
fever
is
the
most
significant
mosquito-borne
viral
disease
globally,
and
has
rapidly
increased
in
incidence,
geographic
distribution,
and
severity
in
recent
decades
[1-3].
The
disease
is
caused
by
four
distinct
dengue
virus
serotypes
(DENV
1-4)
that
are
transmitted
primarily
by
the
female
Aedes
aegypti
mosquito,
with
Aedes
albopictus
as
a
secondary
vector.
Common
disease
manifestations
range
from
asymptomatic
to
moderate
febrile
illness,
with
a
smaller
proportion
of
patients
who
progress
to
severe
illness
characterized
by
hemorrhage,
shock
and
death
[4].
Integrated
vector
control
and
surveillance
remain
the
principle
strategies
for
disease
prevention
and
control
in
endemic
regions,
as
no
vaccine
or
specific
medical
treatment
are
yet
available.
Macro
social
and
environmental
drivers
have
facilitated
the
global
spread
and
persistence
of
dengue,
including
growing
vulnerable
urban
populations,
global
trade
and
travel,
climate
variability,
and
inadequate
vector
control
[5-8].
However,
we
have
a
limited
understanding
of
the
relative
effects
of
these
drivers
at
the
local
level,
restricting
our
ability
to
predict
and
respond
to
site-specific
dengue
outbreaks.


Early
warning
systems
(EWS)
for
dengue
and
other
climate-sensitive
diseases
are
decision-support
tools
that
are
being
developed
to
improve
the
ability
of
the
public
health
sector
to
predict,
prevent,
and
respond
to
local
disease
outbreaks
[9,10].
An
EWS
incorporates
environmental
data
(e.g.,
climate,
altitude,
sea
surface
temperature),
epidemiological
surveillance
data,
and
other
social-
ecological
data
in
a
spatiotemporal
prediction
model
that
generates
operational
disease
risk
forecasts,
such
as
seasonal
risk
maps.
Previous
studies
have
demonstrated
the
utility
of
this
approach
for
vector-borne
diseases,
including
for
dengue
[11-13],
malaria
[14-16]
and
rift
valley
fever
[17].
Maps
and
other
model
outputs
are
linked
to
an
epidemic
alert
and
response
systems,
triggering
a
chain
of
preventive
interventions
when
an
alert
threshold
is
reached.


One
of
the
first
steps
in
developing
an
EWS
is
to
characterize
the
spatiotemporal
dynamics
and
the
covariates
associated
with
historical
disease
transmission.
This
is
often
done
by
developing
GIS
base
maps
of
epidemiological,
environmental,
and
social
data
to
identify
risk
factors;
and
through
time
series
analyses
of
epidemiological
and
climate
data.
These
analyses
require
cross-
institutional
integration
of
expertise
and
data,
including
epidemiological
and
entomological
data
from
ministries
of
health,
climate
information
from
national
institutes
of
meteorology,
and
social-ecological
spatial
data
from
national
census
bureaus.
Previous
studies
indicate
that
associations
among
climate,
socioeconomic
indicators
and
dengue
risk
vary
by
location
and
time,
indicating
the
need
for
analyses
of
dengue
risk
that
consider
the
local
context
to
explain
transmission
mechanisms
[18-24].


Importantly,
these
analyses
also
need
to
consider
the
spatial
and
temporal
scales
of
ongoing
data
collection
and
surveillance
activities
to
ensure
that
the
model
outputs
can
support
an
operational
EWS.


The
National
Institute
of
Meteorology
and
Hydrology
(INAMHI)
of
Ecuador
is
coordinating
efforts
with
the
Ministry
of
Health
(Ministerio
de
Salud
P�blica
�
MSP)
to
develop
an
operational
dengue
EWS
for
coastal
regions
of
Ecuador,
where
the
disease
is
hyper-endemic
[25].
Our
previous
studies
in
southern
coastal
Ecuador
demonstratedthe
potentialtodevelop
adengueEWS
that
incorporates
climate
and
non-climate
information.
We
found
that
the
magnitude
and
timing
of
dengue
outbreaks
were
associated
with
anomalies
in
local
climate,
the
El
Ni�o
Southern
Oscillation
(ENSO),
the
virus
serotypes
in
circulation,
and
vector
abundance
[26].
Local
field
studies
showed
that
dengue
risk
also
depended
on
household
risk
factors
(e.g.,
access
to
piped
water
infrastructure,
demographics,
water
storage
behaviors,
housing
conditions)
[22].
Our
recent
advances
in
seasonal
climate
forecasts
indicate
that
the
forecasts
in
this
region
have
considerable
skill
(i.e.,
predictive
ability)
[27,28].
Building
on
these
previous
studies,
this
study
was
conducted
to
characterize
the
spatiotemporal
dynamics,
climatic
and
social-ecological
risk
factors
associated
with
the
largest
dengue
epidemic
(2010)
on
record
in
the
coastal
city
of
Machala,
Ecuador,
an
important
site
for
dengue
surveillance
in
the
region.


Methods


Study
Area


Machala,
El
Oro
Province,
is
a
mid-sized
coastal
port
city
(pop.
241,606)
[29]
located
in
southern
coastal
Ecuador,
70
kilometers
north
of
the
Peruvian
border
and
186
kilometers
south
of
the
city
of
Guayaquil
(the
epicenter
of
historical
dengue
outbreaks
in
the
region).
Dengue
is
an
emerging
disease
in
this
region,
with
the
first
cases
of
dengue
hemorrhagic
fever
(DHF)
reported
in
2005.
The
disease
is
now
hyper-endemic,
with
year
round
transmission
and
co-circulation
of
all
four
serotypes.
Recent
multi-country
studies
showed
that
Machala
had
the
highest
Ae.
aegypti
larval
indices
of
ten
sites
in
other
countries
in
Latin
America
and
Asia
[30,31]
Given
the
high
burden
of
disease,
the
high
volume
of
people
and
goods
moving
across
the
Ecuador-Peru
border,
and
proximity
to
Guayaquil,
Machala
is
a
strategic
location
to
monitor
and
understand
dengue
transmission
dynamics.


In
2010,
DENV-1
caused
the
largest
dengue
epidemic
to
date,
with
over
4,000
cases
reported
in
El
Oro
province
[32]
(Figure
1A).
In
Machala,
there
were
2,019
cases
of
dengue
fever
(and
77
DHF)
or
an
incidence
of
84
dengue
cases
(and
3
DHF
cases)
per
10,000
population
per
year,
compared
to
25
dengue
cases
per
10,000
population
per
year
from
2003
to
2009.
The
greatest
burden



Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
3
of
16
http://www.biomedcentral.com/1471-2334/14/610



Figure
1
Time
series
of
dengue
and
local
climate
conditions
in
2010
and
historically
in
Machala,
Ecuador.
(A)
Weekly
reported
cases
of
dengue
in
2010
and
weekly
average
cases
from
2003
to
2012;
(B)
weekly
averages
of
rainfall
and
minimum
air
temperature
(Tmin)
in
2010
compared
to
the
climatology
(1986
to
2013
average
conditions).



of
disease
(58%)
during
the
epidemic
was
among
individuals
under
20
years
of
age
(Figure
2).
The
number
of
cases
in
older
adults
(i.e.,
11%
of
cases
reported
by
people
over
50)
indicated
a
strong
force
of
infection,
and
that
dengue
was
a
relatively
new
disease
in
the
population.
The
epidemic
occurred
during
a
wetter
than
average
year,
with
elevated
vector
indices.
Cumulative
rainfall
from
January
to
April
2010
was
56%
above
the
1986
to
2009
average.
The
percent
of
households
with
Ae.
aegypti
juveniles
(House
Index)
was
21.7
�
4.11
(mean
�
95%
CI)
in
2010
compared
to
14.3
�
4.70
from
2003
to
2009
[33].


Data
sources


The
following
data
from
Machala
were
included
in
analyses:
neighborhood-level
georeferenced
dengue
cases,
national
census
data,
and
entomological
surveillance
data
from
2010;
and
time
series
of
weekly
dengue
cases
(aggregated
to
the
city-level)
and
meteorological
data
from
2003
to
2012.
These
data
were
examined
to
identify
potential
social-ecological
and
climate
variables
associated
with
the
presence
of
dengue
fever
during
the
2010
outbreak
in
Machala,
Ecuador.
Epidemiological
data
were
provided
by
INAMHI
through
a
collaborative
project
with


the
MSP
that
was
sponsored
by
the
Ecuadorian
government.
Accordingly,
no
formal
ethical
review
was
required,
as
the
data
used
in
this
analysis
were
de-identified
and
aggregated
to
the
neighborhood-and
city-level,
as
described
below.


Epidemiological
data


INAMHI
provided
a
map
of
georeferenced
dengue
cases
from
Machala
in
2010,
de-identified
and
aggregated
to
neighborhood-level
polygons
(n
=
253)
to
protect
the
identity
of
individuals
[34].
This
map
was
generated
from
individual
records
of
clinically
suspected
cases
of
dengue
fever
and
DHF
(aggregated
as
total
dengue
fever)
reported
to
a
mandatory
MSP
disease
surveillance
system,
and
the
map
included
83%
of
all
dengue
cases
(n
=
1,674)
reported
in
2010.
Reported
dengue
cases
were
defined
based
on
a
clinical
diagnosis.
INAMHI
also
provided
data
for
weekly
dengue
cases
from
Machala
from
2003
to
2012
for
the
wavelet
analysis
described
below.


Social-ecological
risk
factors


We
extracted
individual
and
household-level
data
from
the
2010
Ecuadorian
National
Census
[29]
to
test
the



Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
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of
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Figure
2
Dengue
incidence
(per
10,000
population
per
year)
by
age
and
gender
for
(A)
dengue
fever
and
(B)
dengue
hemorrhagic
fever
in
Machala
in
2010.



hypothesis
that
social-ecological
variables
were
associated
with
the
presence
of
dengue
(Table
1).
We
calculated
a
composite
normalized
housing
condition
index
(HCI)
for
each
household
by
combining
variables
for
the
condition
of
the
roof
(CR),
condition
of
the
walls
(CW),
and
condition
of
the
floors
(CF)
(Equation
1).
Each
of
the
three
variables
ranged
from
1
to
3,
where
3
indicated
poor
condition.
When
summed,
the
values
of
the
composite
index
ranged
from
3
(min)
to
9
(max),
and
we
normalized
the
index
from
0
to
1,
where
1
indicated
the
worst
housing
condition.


HCI
�
��CR
�
CW
�
CF�
�
min]
=
�
max
�
min��1�


Using
individual
and
household
census
records,
we
recoded
selected
census
variables
and
calculated
parameters
as
the
percent
of
households
or
percent
of
the
population
per
census
sector
(n
=
558
census
sectors).
The
data
element
dictionary
of
recoded
variables
in
Spanish
is
presented
in
Additional
file
1:
Table
S1.
To
scale
the
sector-
level
polygon
data
to
neighborhood-level
polygons,
we
used
the
�isectpolypoly�
tool
in
Geospatial
Modeling
Environment
[35,36].
We
estimated
neighborhood
population


by
calculating
the
area-weighted
sum,
and
estimated
all
other
parameters
by
calculating
area-weighted
means.
The
neighborhood
population
estimates
were
also
used
to
calculate
neighborhood
dengue
prevalence
and
population
density
parameters.


Entomological
data


Vector
surveillance
data
for
Ae.
aegypti
from
2010
was
obtained
from
the
National
Service
for
the
Control
of
Vector-Borne
Diseases
of
the
MSP,
and
included
quarterly
House
Indices
(percent
of
households
with
Ae.
aegypti
juveniles)
and
Breteau
Indices
(number
of
containers
with
Ae.
aegypti
juveniles
per
100
households).
The
average
Breteau
Index
during
the
first
two
quarters
of
2010
(January
to
June),
was
the
vector
index
that
was
most
strongly
associated
with
dengue
presence
(1)
or
absence
(0)
(Pearson
correlation,
r
=
0.2,
p
=
0.001)
and
this
period
corresponded
with
the
peak
of
the
epidemic;
accordingly,
we
selected
this
variable
to
test
in
the
multivariate
model
(Table
1).


Climate
data


Daily
meteorological
data
(rainfall
and
minimum
air
temperature)
during
the
study
period
were
provided
by



Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
5
of
16
http://www.biomedcentral.com/1471-2334/14/610


Table
1
Social-ecological
parameters
(mean
and
standard
deviation
-SD)
tested
in
logistic
regression
models
to
predict
dengue
presence
(1)
and
absence
(0)
at
the
neighborhood
level
in
Machala
in
2010


Parameter
Mean
SD
Population
density
More
than
four
people
per
bedroom
(%
households)
14.6%
6.4%
Population
density
(people
per
square
kilometer)
10,864
5,302
More
than
one
other
household
sharing
the
home
(%
households)
2.2%
1.6%
People
per
household
3.88
0.52
Demographics
Receive
remittances
(%
households)
10.8%
3.2%
People
emigrate
for
work
(%
households)
2.2%
1.2%
Mean
age
of
the
head
of
the
household
(years)
45.2
3.0
Head
of
the
household
has
primary
education
or
less
(%
households)
35.9%
12.9%
Afro-Ecuadorian
(%
population)
9.6%
6.9%
Head
of
the
household
is
unemployed
(%
households)
23.0%
5.3%
Head
of
household
is
a
woman
(%
households)
30.3%
4.5%
Housing
conditions
Housing
condition
index
(HCI),
0
to
1,
where
1
is
poor
condition
0.29
0.10
No
access
to
municipal
garbage
collection
(%
households)
8.0%
12.3%
No
piped
water
inside
the
home
(%
households)
34.4%
18.7%
No
access
to
sewerage
(%
households)
22.4%
27.6%
No
access
to
paved
roads
(%
households)
26.7%
22.3%
People
drink
tap
water
(%
households)
32.8%
11.7%
Rental
homes
(%
households)
24.6%
10.6%
Other
variables
Average
distance
to
the
central
hospital
(km)
2.36
1.27
Average
Breteau
Index
during
the
first
two
quarters
of
2010
28.6
2.15


the
Granja
Santa
Ines
weather
station
located
in
Machala
(3�17'16�
S,
79�54'5�
W,
5
meters
above
sea
level)
and
operated
by
INAMHI.
The
weekly
climatology
(19852013)
and
weather
during
the
study
period
are
shown
in
Figure
1B.
Weekly
average
rainfall
and
minimum
temperature
from
2003
to
2012
were
included
in
the
wavelet
analysis,
since
it
has
been
shown
that
these
two
climate
variables
explain
an
important
part
of
the
total
variance
of
dengue
cases
in
coastal
Ecuador
[26].


Statistical
analyses


Exploratory
spatial
analysis


We
applied
Moran�s
I
with
inverse
distance
weighting
(ArcMap
10.1)
to
epidemiological
dengue
data
from
2010
to
test
the
hypothesis
that
dengue
cases
were
randomly
distributed
in
space.
Moran�s
I
is
a
global
measure
of
spatial
autocorrelation,
that
provides
an
index
of
dispersion
from
-1
to
+1,
where
-1
is
dispersed,
0
is
random,
and
+1
is
clustered.
We
identified
the
locations
of
significant
dengue
hot
and
cold
spots
using
Anselin
Local
Moran�s
I
(LISA)
with
inverse
distance
weighting
(ArcMap
10.1).
The
LISA
is
a
local
measure
of
spatial


autocorrelation
[37]
that
identifies
significant
clusters
(hot
or
cold
spots)
and
outliers
(e.g.,
nonrandom
groups
of
neighborhoods
with
above
or
below
the
expected
dengue
prevalence).
Previous
studies
have
used
Moran�sI
and
LISA
to
test
the
spatial
distribution
of
dengue
transmission
[38],
including
in
Ecuador
[39],
allowing
for
comparison
between
studies.


Social-ecological
risk
factors


Census
data
aggregated
to
the
neighborhood-level
were
examined
to
identify
potential
social-ecological
variables
associated
with
the
presence
of
dengue
fever,
including
population
density,
human
demographic
characteristics,
and
housing
condition
(Table
1).
We
hypothesized
that
the
presence
or
absence
of
dengue
was
associated
with
one
or
more
of
these
factors;
each
factor
was
presented
as
a
suite
of
census
variables,
representing
testable
variable
ensemble
hypotheses
in
a
model
selection
framework
-a
modeling
strategy
that
has
been
previously
described
[22].
Variables
for
the
average
distance
to
the
public
hospital
(Teofilo
Davila
Hospital,
the
provincial
hospital
located
in
the
city
center)
and
the
Breteau



Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
6
of
16
http://www.biomedcentral.com/1471-2334/14/610


Index
were
also
tested
in
the
model
to
assess
geographic
differences,
potential
underreporting,
and
other
factors
(e.g.,
microclimate,
vector
control)
not
captured
by
the
census
variables.


We
centered
all
variables
and
selected
the
best-fit
models
(GLM,
family
=
binomial,
link
=
logit)
using
glmulti,
an
R
package
for
multimodel
selection
[40].
All
possible
unique
models
were
tested
and
ranked
based
on
Akaike�sInformation
Criterion
(AIC)
modified
for
small
sample
sizes
(AICc)
(Equation
2).
We
compared
the
top
ranked
model
to
the
global
model,
which
included
all
proposed
variables
as
model
parameters.


AIC
�2k-
ln
L

��
2kk
�1�
2�

��

AICc
�AIC
�


n-k-1


Where
k
is
the
number
of
parameters
in
the
model,
n
is
the
sample
size,
and
L
is
the
maximized
likelihood
function
for
the
model.


Parameter
estimates
and
95%
confidence
intervals
(CI)
were
calculated
for
variables
in
the
top
ranked
model
(Table
2
Model
A).
Variance
inflation
factors
(VIF)
were
calculated
to
assess
multi-colinearity
and
model
dispersion.
We
found
that
inclusion
of
the
parameters
for
housing
condition
and
piped
water
together
led
to
over-
dispersion
and
highly
inflated
variance
in
the
best-fit
model
(Table
2
Model
B).
Based
on
the
strong
linear
correlation
between
housing
condition
and
access
to
piped
water
(Figure
3),
we
replaced
these
variables
with
the
residuals
of
housing
condition
regressed
on
access
to
piped
water,
and
re-ran
glmulti
to
identify
the
best-
fit
model.
The
inclusion
of
this
variable
enabled
testing


for
the
effect
of
housing
condition
beyond
that
which
was
explained
only
by
access
to
piped
water.


Wavelet
analysis


To
understand
the
time-frequency
variability
of
dengue
and
climate
during
the
2010
epidemic,
we
conducted
a
wavelet
analysis
of
a
10-year
time
series
of
weekly
incident
dengue
cases
(2003-2012),
rainfall
and
minimum
temperature
(Figure
4A).
Wavelet
analyses
are
ideal
for
noisy,
non-stationary
data,
such
as
dengue
cases
data,
which
demonstrate
strong
seasonality
and
interannual
variability
(yearly
changes)
[41,42].
These
analyses
identify
significant
temporal
scales
(i.e.,
defined
here
as
periods
whose
associated
wavelet
power
is
statistically
significant
for
at
least
two
continuous
years;
Figure
4)
over
time
for
a
given
variable,
such
as
2-year
cycles
or
annual
seasonal
cycles
of
dengue
transmission.
Cross
wavelet
and
wavelet
coherency
allowed
us
to
compare
two
time
series,
such
as
climate
and
dengue,
and
to
identify
synchronous
periods
or
signals.


The
pre-processing
of
the
time
series
data
for
Machala
followed
a
two-step
methodology
described
elsewhere
in
detail
[27,28,43].
First,
we
quality-controlled
the
time
series
using
a
standard
R
package
[44]
to
identify
outliers
and
inconsistent
values
(e.g.,
minimum
temperatures
>
maximum
temperatures,
negative
precipitation
values
or
negative
frequency
of
dengue
cases).
Outliers
were
defined
as
data
points
at
least
three
standard
deviations
above
or
below
the
mean.
To
account
for
real
outliers
(e.g.,
not
artifacts
produced
by
human,
instruments,
or
transmission
errors),
we
compared
suspicious
values
with
data
from
nearby
climate
stations.
Entries
that
we


Table
2
The
parameters
included
in
the
best-fit
logistic
regression
models
to
predict
the
presence
(1)
or
absence
(0)
of
dengue
in
neighborhoods
in
Machala
in
2010


Parameter
Estimate
95%
CI
SE
VIF
P
value
Model
A.
Intercept
0.75
0.46
�
1.05
0.15
<
0.001
Head
of
household
is
a
woman
7.77
0.73
�
15.17
3.67
1.14
0.034
Age
of
head
of
household
0.10
0.00
�
0.21
0.05
1.06
0.051
Residual
of
HCI
regressed
on
households
with
no
access
to
piped
water
inside
the
home
9.04
3.98
�
14.37
2.64
1.05
<
0.001
Distance
to
central
hospital
-0.0005
-0.0007
�
0.0
0.0001
1.20
<
0.001
Model
B.
Intercept
-7.59
-14.24
�
-1.32
3.28
0.021
Head
of
household
is
a
woman
7.30
0.11
�
14.83
3.74
1.18
0.051
Age
of
head
of
household
0.14
0.002
�
0.26
0.07
1.60
0.052
No
piped
water
inside
the
home
-3.18
-6.08
�
-0.4
1.44
3.64
0.027
HCI
9.16
4.06
�
14.56
2.66
3.11
0.001
Distance
to
central
hospital
-0.001
-0.001
�
0.0
0.0
1.31
<0.001


(Model
A.)
The
best
fit
model,
which
included
the
residual
of
the
HCI
regressed
on
the
variable
for
no
access
to
piped
water
inside
the
home.
(Model
B.)
The
same
model
is
shown
with
separate
parameters
for
the
HCI
and
no
access
to
piped
water
inside
the
home
to
indicate
the
direction
of
the
effects
of
the
parametersin
the
model.
VIF
values
indicate
a
high
degree
of
multicollinearity
in
model
B
compared
to
model
A.
High
values
of
HCI
indicate
poor
housing
condition.



Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
7
of
16
http://www.biomedcentral.com/1471-2334/14/610



Figure
3
Scatter
plot
and
linear
fit
for
the
composite
normalized
housing
condition
index
(HCI)
versus
the
percentage
of
households
with
no
piped
water
inside
the
home,
for
neighborhoods
with
cases
of
dengue
(red
circle)
and
without
dengue
(black
triangle)
in
Machala
in
2010.


deemed
to
be
uncorrectable
were
flagged
as
missing
values.
Then
we
used
the
R
package
�RHTestsV4�
[45-48]
to
detect
and
correct
temporal
inhomogeneities
in
these
variables.
The
climate
time
series
did
not
need
substantial
corrections.
Weekly
dengue
case
data
were
transformed
to
weekly


incidence
using
a
linear
interpolation
of
local
population
data
from
the
2001
and
2010
national
censuses.
The
final
step
in
data
pre-processing
involved
the
normalization
of
the
three
variables
to
constrain
variability.
Dengue
incidence
and
rainfall
time
series
had
non-normal
probability
density
functions,
thus
they
were
percentile-transformed
[34].


The
wavelet
analysis
of
dengue
and
climate
data
enabled
us
to
identify
common
periodicity
patterns
(e.g.,
annual
or
biannual
signals)
and
anomalous
climate
conditions
during
the
2010
outbreak.
We
used
Morlet
wavelets
in
Matlab


[49]
to
compute
the
time
series�
wavelet
power
spectrum
and
to
identify
significant
periods
for
each
variable,
cross-
wavelet
power
to
identify
periods
where
dengue-rainfall
and
dengue-temperature
had
high
common
power,
and
the
coherence
spectra
to
identify
local
co-variability
of
dengue-rainfall
and
dengue-temperature
[50].
Significance
testing
(p
=
0.05)
was
conducted
using
an
AR1
background
noise
for
the
first
two
spectra,
and
a
Monte
Carlo
approach
to
compute
the
significance
levels
in
the
coherence
spectrum.
Statistically
significant
regions
are
displayed
enclosed
by
a
solid
black
line
in
the
wavelet
plots;
and
cones
of
influence
(COI),
where
edge
effects
increase
the
uncertainty
of
the
analysis,
are
shown
as
a
lighter
shaded
region
(Figures
4,
5
and
6).
The
arrows
represent
the
relative
phase,
which
is
indicative
of
the
lags
between
the
two
time
series,
as
determined
by
frequency
and
time
[51].The
direction
of
the
arrows
can
be
used
to
quantify
the
phase
relationship:
arrows
pointing
horizontally
to
the
Figure
4
Dengue
and
climate
wavelets.
(A)
Normalized
time
series
of
weekly
dengue
incidence,
rainfall,
and
minimum
temperature
from
20032012,
and
the
wavelet
power
spectrum
for
(B)
dengue
incidence,
(C)
rainfall,
and
(D)
minimum
temperature.
The
black
box
indicates
weeks
1-15
in
2010,
when
75%
of
the
cases
from
the
epidemic
were
reported.
Statistically
significant
regions
are
displayed
enclosed
by
a
solid
black
line
in
the
wavelet
plots;
and
cones
of
influence
(COI),
where
edge
effects
increase
the
uncertainty
of
the
analysis,
are
shown
as
a
lighter
shaded
region.




Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
8
of
16
http://www.biomedcentral.com/1471-2334/14/610



Figure
5
As
in
Figure
4,
but
for
the
cross
wavelet
power
spectrum
for
(A)
dengue-rainfall
and
(B)
dengue-minimum
temperature.


Arrows
pointing
horizontally
to
the
right
(left)
indicate
that
the
two
variables
are
in
(anti-)
phase.



right
(left)
indicate
that
the
two
variables
are
in
(anti-)
phase.
When
the
signals
of
two
time
series
are
in
phase,
their
maximum
amplitudes
occur
simultaneously.


Results


Spatial
analyses
and
social-ecological
risk
factors


Average
neighborhood
dengue
incidence
in
2010
was


76.7
�
14.3
(95%
CI)
per
10,000
population
(range:
0
to
775.8)
(Figure
7A).
The
distribution
was
heavily
left
skewed,
with
35%
of
neighborhoods
(n
=
89)
reporting
zero
cases
(Additional
file
2:
Figure
S1).
Dengue
cases
during
the
epidemic
were
significantly
clustered
(Moran�s
I
=
0.03,
p
<0.001).
Findings
from
the
LISA
analysis
indicated
that
there
were
significant
dengue
hotspots
(n
=
15
high-high
neighborhoods)
in
west-central
Machala,
and
a
smaller
number
of
significant
outliers
(n
=
2
high-low
neighborhoods,
n
=
5
low-high
neighborhoods)
(p
<0.05,
Figure
7b).



Figure
6
As
in
Figure
5
but
for
wavelet
coherence
spectrum
for
(A)
dengue-rainfall
and
(B)
dengue-minimum
temperature.




Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
Page
9
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Figure
7
The
spatial
distribution
of
dengue
transmission
in
Machala,
Ecuador
in
2010:
(A)
Dengue
incidence
(cases
per
10,000
population
in
2010)
per
neighborhood,
and
(B)
significant
hot
spots
(high-high)
and
outliers
(high-low
and
low-high)
identified
through
LISA
analysis
(p
=0.05).



The
top
ranked
model
to
predict
the
presence
of
dengue,
identified
through
the
multimodel
selection
process,
was
a
better
fit
than
the
global
model
that
included
all
of
the
proposed
social-ecological
variables
(global
model
AICc
=
311,
top
ranked
model
AICc
=
291.3,
.AICc
=
19.7).
The
top
ranked
model
included
the
residual
of
the
HCI
regressed
on
no
access
to
piped
water,
distance
from
the
central
hospital,
and
demographics
of
the
heads
of
households
(i.e.,
older
age,
female
gender)
(Table
2
Model
A,
Figure
8,
Additional
file
3:
Figure
S2).
We
also
presented
the
model


with
the
HCI
and
access
to
piped
water
as
separate
variables,
to
indicate
the
direction
of
the
effect
of
each
variable
(Table
2
Model
B).
Neighborhoods
were
more
likely
to
report
dengue
if
they
had
poor
housing
condition
and
had
greater
access
to
piped
water
inside
the
home.
When
we
compared
neighborhoods
with
similar
housing
conditions,
neighborhoods
were
more
likely
to
report
dengue
cases
if
they
had
greater
access
to
piped
water
inside
the
home
(Figure
3).
Inclusion
of
the
residual
variable
in
the
model
(Table
2
Model
A)
reduced
multicollinearity,
as
indicated
by
the
low
VIFs.



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Figure
8
Social-ecological
parameters
from
the
2010
census
that
were
included
in
the
top
logistic
regression
model
to
predict
the
presence
of
dengue,
clockwise
from
top
left:
the
average
age
of
the
head
of
the
household,
the
housing
condition
index,
distance
to
the
central
hospital,
the
percent
of
households
with
a
female
head
of
household,
the
percent
of
households
with
no
piped
water
inside
the
home.




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2014,
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Multiple
best-fit
models
were
within
the
predetermined
threshold
criteria
of
.AICc
=2
of
the
top
model
and
weights
greater
than
1.5%
(Additional
file
4:
Table
S2).
In
addition
to
the
parameters
included
in
the
top
model,
the
following
variables
were
included
in
competing
best-fit
models:
Breteau
Index,
population
density,
households
with
people
who
emigrate
for
work,
and
households
without
access
to
paved
roads.


Temporal
climate
analyses


We
found
that
multiple
temporal
scales
were
involved
in
local
dengue
transmission
dynamics,
as
shown
in
the
wavelet
power
spectrum
for
dengue
incidence
(Figure
4B).
In
wavelet
analyses,
strong
significant
signals
at
a
certain
frequency
are
associated
with
persistent
(quasi)
periodic
cycles
in
the
time
series
(e.g.,
a
1-year
band
indicates
presence
of
annual
cycles).
There
was
a
strong
and
significant
signal
for
the
~2-year
periodic
band
for
dengue
incidence.
There
was
also
a
significant
signal
for
the
~1-year
periodic
band,
although
it
was
less
frequent
(e.g.,
2003,
2006,
and
2011).
Signals
around
and
above
the
4-year
periodic
band
were
not
considered,
as
they
fell
inside
of
the
COI
(Figure
4B).
These
results
suggest
that
dengue
periodicity
in
this
locality
is
not
only
annual
(~1
year),
but
that
there
is
also
an
important
biannual
cycle
(~2
year),
that
may
reflect
typical
time
scales
of
extrinsic
(e.g.,
climate)
and
intrinsic
(e.g.,
immunologic)
processes
involved
in
theoccurrenceofdenguefor
this
region.


The
rainfall
and
minimum
temperature
spectra
in
Figure
4C,D
demonstrated
a
strong
annual
signal
(1-year
periodic
band),
in
agreement
with
the
annual
dengue
cycle
in
the
region.
There
was
no
evidence
of
relevant
changes
in
variability
for
minimum
temperature;
the
corresponding
signal
in
the
power
spectrum
is
continuous
around
the
annual
band.
In
contrast,
there
were
fluctuations
in
the
~1-year
band
for
precipitation,
likely
associated
with
periods
of
low
precipitation.
Beginning
around
2006,
both
climate
variables
demonstrated
significant
power
around
the
2-year
band,
a
feature
that
is
most
noticeable
in
the
rainfall
data,
particularly
in
recent
years.


The
cross-wavelet
power
spectra
for
dengue
and
rainfall
(Figure
5A),
and
dengue
and
minimum
temperature
(Figure
5B)
showed
regions
in
the
time-frequency
space
with
high
common
power
in
the
1-year
and
2-year
bands,
suggesting
a
relationship
between
climate
and
dengue
incidence
at
both
time
scales.
The
corresponding
wavelet
coherence
spectra,
however,
indicated
that
the
dengue
and
rainfall
co-vary
mostly
in
the
2-year
band
(Figure
6A),
while
dengue
and
minimum
temperature
co-vary
mostly
in
the
1-year
band
(Figure
6B).
This
suggests
that
temperature
and
rainfall
have
well-differentiated
roles
in
dengue
transmission.
The
directions
of
the
arrows
in
the
plots
indicate
a
slow
change
of
phase
in
the
co-variability
of
dengue
and
rainfall
in
the
2-year
band,
approaching
in-phase
behavior


in
late
2009,
and
we
observed
synchronized
co-variability
for
the
1-year
band
in
2009
and
at
the
start
of
2010.
These
results
highlight
the
distinct
roles
of
these
climate
variables
in
dengue
transmission
at
different
temporal
scales,
and
the
importance
of
the
phase
and
timing
of
climate
variables
with
respect
to
dengue
transmission.


We
found
that
the
2010
epidemic
episode
could
be
characterized
by
a
combination
of
annual
and
bi-annual
signals
in
dengue
transmission
and
climate
variables.
The
outbreak
was
characterized
by
a
combination
of
in-phase
variability
of
above
normal
minimum
temperatures,
and
quasi-in-phase
above
normal
rainfall
episodes
associated
with
the
late
2009
to
early
2010
moderate
El
Ni�o
event
(see
arrows
pointing
right
along
the
1-year
band
at
the
bottom
of
Figures
5
and
6).
The
times
series
(1995-2010)
of
monthly
anomalies
in
dengue
cases
from
El
Oro
province,
ENSO,
temperature
and
rainfall
have
been
previously
described
(See
Figure
2
in
Stewart
Ibarra
&
Lowe
2013)
[26].
This
analysis
demonstrated
that
the
observed
effect
(quasi-simultaneity
in
the
variability
of
dengue,
temperature
and
rainfall)
was
present
in
early
2010,
but
not
in
any
other
year
of
the
period
under
study
(Figures
5
and
6).


Discussion


Dengue
is
the
most
important
mosquito-borne
viral
disease
globally,
and
has
increased
in
incidence
and
distribution
despite
ongoing
vector
control
interventions
during
the
last
three
decades
[1-3].
To
date,
we
have
a
limited
understanding
of
the
spatiotemporal
dynamics
of
dengue
transmission,
particularly
at
the
local
scale,
due
to
the
complex,
non-stationary
relationships
among
dengue
infection,
climate,
vector,
and
virus
strain
dynamics
[41,52-54];
and
the
geographic
and
temporal
variation
in
the
social-ecological
conditions
that
influence
risk
[18-21].
More
robust
analysis
tools,
such
as
wavelet
analyses
and
multimodel
inference,
and
the
increasing
availability
of
geospatial
epidemiological,
climate,
and
social-ecological
data
have
increased
our
ability
to
explore
these
dynamics.
Studies
such
as
this
provide
critical
information
to
improve
disease
surveillance
and
to
develop
an
EWS
and
other
evidence-based
interventions.


In
this
study,
we
found
that
neighborhoods
with
certain
social-ecological
conditions
were
more
likely
to
have
cases
of
dengue
during
the
largest
outbreak
to
date
in
El
Oro
Province.
Dengue
cases
were
clustered
in
neighborhood-
level
transmission
hotspots
near
the
city
center
during
the
epidemic.
Risk
factors
included
poor
housing
condition,
greater
access
to
piped
water
inside
the
home,
less
distance
to
the
central
hospital,
and
demographics
of
the
heads
of
households
(i.e.,
older
age,
female
gender).
In
analyses
of
10
years
of
weekly
epidemiological
and
climate
data,
we
found
that
dengue,
rainfall
and
minimum
temperature
co-varied
and
had
common
power
at
1-year



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and
2-year
cycles,
with
quasi-synchronized
higher
than
average
rainfall
and
minimum
temperatures
likely
contributing
to
the
2010
dengue
outbreak.
This
study
contributes
to
ongoing
efforts
by
INAMHI
and
the
MSP
of
Ecuador
to
develop
a
dengue
prediction
model
and
early
warning
system.
Findings
from
this
study
will
inform
the
development
of
dengue
vulnerability
maps
and
climate-
driven
dengue
seasonal
forecasts
that
provide
the
MSP
with
information
to
target
high-risk
regions
and
seasons,
allowing
for
more
efficient
use
of
scarce
resources
[9].


Spatial
dynamics
and
social-ecological
risk
factors


In
Machala,
a
relatively
small
and
heterogeneous
city,
there
was
evidence
of
unequal
exposure
or
unequal
reporting
of
dengue.
During
the
epidemic,
dengue
transmission
was
focused
in
hotspots
in
the
west-central
urban
sector,
a
middle-to
low-income
residential
area
with
moderate
access
to
urban
infrastructure.
Although
people
had
access
to
basic
services,
our
previous
studies
suggest
that
dengue
control
in
these
communities
may
be
limited
by
the
cost
of
household
vector
control,
lack
of
social
cohesion,
and
limited
engagement
with
local
institutions
[55].
Previous
studies
that
used
spatial
clustering
statistics
also
found
evidence
of
significant
clustering
of
dengue
transmission
across
the
urban
landscape
[18,56-58].
A
previous
study
in
Guayaquil,
Ecuador,
identified
neighborhood-level
dengue
hot
and
coldspots,
and
found
that
the
location
of
hotspots
shifted
over
the
5-year
period,
highlighting
the
spatially
dynamic
nature
of
dengue
risk
and
the
importance
of
multiyear
studies
[39].
Longitudinal
field
studies
in
Thailand
found
evidence
of
fine-scale
spatial
and
temporal
clustering
of
dengue
virus
serotypes
and
transmission
at
the
school
and
household
levels
[59,60].
Focal
transmission
patterns
are
likely
associated
with
the
limited
flight
range
of
the
Ae.
aegypti
mosquito.
Recent
studies
in
Peru
demonstrated
the
importance
of
human
movement
patterns
in
determining
spatial
dengue
transmission
dynamics
within
an
urban
area
[61,62].
At
a
regional
scale,
dengue
outbreaks
are
likely
influenced
by
human
movement
north
and
south
along
the
Ecuador-Peru
border.
Future
studies
should
continue
to
investigate
the
regional
effects
of
cross-border
movement
of
people
and
goods,
and
the
local
effects
of
intra-urban
movement
between
work,
school,
and
home
to
better
understand
the
spatial
dynamics
of
dengue
transmission.


We
found
that
the
combination
of
HCI
and
access
to
piped
water
was
the
most
important
risk
factor
for
dengue
transmission,
as
indicated
by
the
magnitude
of
the
best-fit
model
parameter
estimate
(Table
2
Model
A);
this
parameter
was
also
a
significant
variable
in
all
other
top
models
(Additional
file
4:
Table
S2).
Neighborhoods
were
more
likely
to
report
dengue
if
they
had
poor
housing
conditions
(likely
associated
with
lower
income)
and
greater


access
to
piped
water
inside
the
home
(likely
associated
with
older,
established
communities
with
access
to
urban
infrastructure).
This
apparent
paradoxical
relationship
suggests
that
household
water
storage
behaviors
played
an
important
role
in
the
2010
dengue
outbreak.
In
our
experience
low-income
households
in
Machala
with
access
to
piped
water
tend
to
store
water
in
containers
in
the
patio
as
a
secondary
water
source,
since
water
supply
interruptions
are
common.
These
secondary
water
containers
are
often
uncovered,
and
the
containers
become
ideal
Ae.
aegypti
larval
habitat
during
the
rainy
season.
In
contrast,
low-income
households
without
access
to
piped
water
are
likely
to
store
water
in
containers
as
their
primary
water
source
(e.g.,
55
gallon
drums),
frequently
filling
and
emptying
the
containers
and
thus
preventing
Ae.
aegypti
from
developing
into
adult
mosquitoes.


Neighborhoods
were
also
at
greater
risk
of
dengue
if
they
were
closer
to
the
central
hospital,
reflecting
either
spatially
biased
reporting
and/or
a
true
increase
in
transmission
near
the
city
center.
This
variable
was
also
significant
in
all
of
the
top
models
(Additional
file
4:
Table
S2).
Given
the
small
size
of
the
city
of
Machala
(~5
km
across)
and
easy
access
to
low-cost
public
transportation,
travel
time
to
the
hospital
was
not
likely
to
be
a
limiting
factor.
However,
people
from
lower
income
communities
may
be
less
likely
to
seek
medical
care
due
to
the
cost
of
medicine
and
the
high
cost
of
missing
work,
leading
to
underreporting
from
the
urban
periphery.
It
is
also
possible
that
people
residing
near
the
city
center
in
Machala
were
at
greater
risk
because
they
may
have
been
less
willing
to
cooperate
with
vector
control
technicians


(E.
Beltran,
pers.
comm.),
due
in
part
to
the
misconception
that
dengue
is
a
problem
of
poor
communities
at
the
urban
periphery
[55].
Households
in
these
areas
may
also
be
at
greater
risk
because
they
store
water
as
a
secondary
water
source,
as
described
above.
These
findings
highlight
the
complexity
of
the
cultural
and
behavioral
factors
influencing
dengue
risk
and
the
importance
of
local-level
studies
that
consider
the
social
context.
Our
findings
are
consistent
with
a
previous
longitudinal
field
study
of
household
risk
factors
for
Ae.
aegypti
in
Machala,
where
it
was
found
that
poor
housing
condition
and
access
to
piped
water
inside
the
home
were
positively
associated
with
the
presence
of
Ae.
aegypti
pupae
[22].
This
prior
study
found
that
Ae.
aegypti
were
more
abundant
in
the
central
urban
area
that
had
better
access
to
infrastructure
than
in
the
urban
periphery
[22].
Interestingly,
the
same
risk
factors
emerged
in
the
study
presented
here
and
the
prior
field
study
despite
differences
in
rainfall
(i.e.,
the
field
study
was
conducted
one
year
after
the
epidemic,
during
a
drier
than
average
year)
and
differences
in
spatial
scale
(i.e.,
household-versus
neighborhood
level).
These
findings
indicate
that
high-risk
households
could
be
identified
and
targeted
using
a
combination
of
census
data
and



Stewart-Ibarra
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2014,
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a
locally
adapted
rapid
survey
of
housing
conditions,
similar
to
the
Premise
Condition
Index,
an
aggregate
index
measuring
house
condition,
patio
condition,
and
patio
shade,
which
has
been
validated
in
other
countries
[63,64].
The
HCI
and
the
combined
HCI-water
access
variables
developed
in
this
study
should
be
explored
and
validated
as
dengue
predictors
in
future
studies
in
this
region.


Ae.
aegpyti
juvenile
indices
were
included
in
two
of
the
top
seven
best-fit
models
to
predict
the
presence
of
dengue
in
neighborhoods
(Additional
file
4:
Table
S2).
A
previous
study
in
El
Oro
Province
found
that
Ae.
aegypti
indices
(House
Index)
were
positively
associated
with
dengue
outbreaks
at
the
province
level
[26].
Although
pupal
or
adult
indices
are
considered
better
predictors
of
dengue
risk
than
larval
indices
[65],
our
findings
suggest
that
larval
indices
may
have
some
predictive
power
in
this
region.
In
Ecuador,
entomological
surveillance
is
limited
to
larval
indices,
and
neighborhoods
are
rarely
sampled
in
consecutive
periods
in
a
given
year
due
to
limited
resources.
These
findings
highlight
the
need
for
additional
studies
of
the
vector-dengue
dynamics
in
this
region
and
local
evaluations
of
the
robustness
of
vector
abundance
measures
in
order
to
strengthen
cost-effective
entomological
surveillance
systems.


Climate
and
dengue
periodicity


The
wavelet
analysis
in
this
study
provided
a
nuanced
understanding
of
the
relationships
among
local
dengue
transmission
and
climate
variables
at
multiple
temporal
scales.
The
analysis
of
10
years
of
weekly
epidemiological
and
climate
data
from
Machala
provided
evidence
of
significant
1-year
and
2-year
cycles
in
dengue,
rainfall
and
minimum
temperature.
The
1-year
cycles
of
minimum
temperature
and
rainfall
likely
contributed
to
the
annual
dengue
cycles
observed
in
the
power
spectrum.
This
finding
was
expected,
as
previous
studies
have
documented
significant
annual
dengue
cycles
in
this
region
[26].
Interestingly,
we
also
found
evidence
of
2-year
cycles
in
the
rainfall
wavelet
power
spectrum
that
were
likely
associated
with
biannual
cycles
of
dengue
transmission,
a
pattern
that
was
previously
undocumented
in
Ecuador.


Indeed,
our
analyses
suggest
that
the
2010
dengue
epidemic
could
be
related
to
a
timely
coincidence
of
above
normal
minimum
temperatures
and
above
normal
rainfall
episodes
during
the
moderate
2009
to
2010
El
Ni�o
event.
Previous
studies
in
this
region
have
shown
that
Ae.
aegypti
abundance
is
associated
with
rainfall
and
minimum
temperature
[22].
In
2010,
rainfall
from
February
and
March,
the
peak
of
dengue
season,
was
almost
double
the
long-term
average
(89%
and
81%
above
average,
respectively),
likely
increasing
the
availability
of
mosquito
larval
habitat.
Temperature
and
temperature
fluctuations
influence
rates
of
mosquito
development
and
virus
replication
[66-71].
The
slow
rate
of
climate
phase
change
observed


in
this
analysis
suggests
the
potential
to
monitor
the
climate
in
this
region
to
identify
future
time
periods
with
synchronous
climate
conditions
similar
to
2010,
that
may
increase
the
risk
of
a
dengue
outbreak.


Our
results
indicate
that
the
2-year
band
in
precipitation
is
an
important
component
in
the
co-variability
of
dengue
incidence
for
the
period
under
study,
although
its
role
in
the
2010
dengue
epidemic
requires
further
investigation.
This
periodic
band
is
not
unique
to
Machala.
Two
to
three-year
cycles
of
dengue
transmission
have
been
reported
in
other
parts
of
the
world
[41,53],
particularly
in
years
associated
with
El
Ni�o
events.
The
statistically
significant
2-year
band
is
present
in
the
dengue
power
spectrum
for
the
entire
time
series.
This
is
not
true
for
rainfall
or
minimum
temperature,
whose
variability
in
the
region
is
strongly
associated
with
El
Ni�o-Southern
Oscillation
(ENSO).
This
suggests
that
although
ENSO
has
a
strong
influence
in
the
occurrence
of
dengue
epidemics
in
coastal
Ecuador,
other
variables
(e.g.,
immunity)
are
also
involved
in
the
process
and/or
that
there
is
a
persistent
mechanism
for
the
climate�sbiannual
contribution
in
the
dengue
spectrum.
It
is
interesting
to
note
that
similar
2-year
cycles
have
been
reported
for
dengue
and
malaria
in
mountain
locations
in
Peru
[72],
but
not
along
the
Peruvian
Coast
or
Amazon
[73].
We
hypothesize
that
the
biannual
signal
found
in
Peru
and
Machala
is
related
to
an
additional
climate
mode
present
over
the
Andes
in
this
region
[28]
in
addition
to
ENSO.
Machala
may
be
uniquely
situated
to
capture
climate
signals
from
ENSO
and
the
so-called
[28]
Andean
mode,
given
its
proximity
to
the
Andean
foothills
and
the
strong
coupled
climate-
ocean
system
(i.e.,
teleconnections)
present
in
the
region.


Limitations


Although
this
study
revealed
patterns
of
climate
and
social-
ecological
conditions
as
important
drivers
of
dengue
transmission,
this
study
has
some
limitations.
It
should
be
noted
that
non-climate
factors
that
were
undocumented
in
this
study
(e.g.,
population
immunity,
vector
control
interventions)
are
also
key
drivers
of
interannual
variability
in
dengue
[26,74,75]
and
most
likely
influenced
the
2010
outbreak.
The
1-year
of
spatially
explicit
epidemiological
data
constrained
our
ability
to
assess
whether
the
social-
ecological
factors
associated
with
the
spatial
distribution
of
dengue
transmission
were
consistent
in
time.
The
10-year
time
series
of
weekly
dengue
data
was
not
available
at
the
appropriate
spatial
scale
for
this
analysis.
With
multiple
years
of
data,
we
could
evaluate
whether
dengue
transmission
at
the
beginning
of
the
dengue
season
or
at
the
beginning
of
an
epidemic
is
more
likely
to
begin
in
neighborhoods
with
similar
characteristics,
to
assess
whether
there
are
persistent
high-risk,
hotspot
neighborhoods
that
trigger
outbreaks.
The
analyses
were
also
limited
by
a
lack
of
laboratory
confirmation
for
cases
or
information
about



Stewart-Ibarra
et
al.
BMC
Infectious
Diseases
2014,
14:610
http://www.biomedcentral.com/1471-2334/14/610


the
immune,
nutritional,
or
health
status
of
the
population.
We
are
currently
collaborating
with
the
MSP
to
improve
dengue
diagnostic
infrastructure
in
the
region
and
to
reduce
the
time
lag
between
epidemiological
reporting
and
vector
control
interventions.
Importantly,
the
MSP
is
undergoing
a
reorganization
and
decentralization
process
to
merge
the
health
and
vector
control
divisions
at
the
local
level,
with
the
goal
of
improving
information
flows
and
linking
responses
to
evidence-based
interventions.


Conclusions


Theresults
of
this
studyhighlight
theimportanceofincorporating
climate
and
social-ecological
information
with
georeferenced
and
clinically
validated
epidemiological
data
in
a
dengue
surveillance
system.
Investigators
in
Ecuador
are
exploring
the
development
of
web-based
GIS
for
national
dengue
surveillance
using
open-access
software.
GIS
is
an
effective
tool
to
integrate
diverse
data
streams,
such
as
dynamic,
real-time
epidemiological
and
climate
data
with
static
vulnerability
maps
generated
from
census
data.
Open
access
tools
are
especially
important
in
resource-limited
settings,
and
analysis
packages
targeted
to
dengue
are
becoming
available
[76].
Web-based
GIS
tools
have
been
developed
for
global
dengue
surveillance,
such
as
the
CDC�s
DengueMap,
and
for
local
dengue
surveillance
research
projects
[77,78].
National-level
dengue
GIS
initiatives
have
been
developed
in
countries
such
as
Mexico
[79],
where
Ministry
of
Health
practitioners
and
software
developers
jointly
designed
the
software
platform.
This
collaborative
approach
to
integrate
diverse
data
streams
will
ideally
provide
public
health
decision-makers
with
information
to
assess
intervention
programs,
allocate
resources
more
efficiently,
and
provide
the
foundation
for
an
operational
dengue
EWS.


Additional
files



Additional
file
1:
Table
S1.
Spanish
dictionary
of
census
variables


evaluated
in
the
multivariate
model
to
predict
the
presence
of
dengue.


Additional
file
2:
Figure
S1.
Histogram
showing
the
density
distribution


of
neighborhood
dengue
incidence
in
Machala,
2010
(n
=
253).


Additional
file
3:
Figure
S2.
Scatter
matrix
of
parameters
included
in


the
top
logistic
regression
model
to
predict
the
presence
of
dengue
in


neighborhoods
in
Machala
in
2010.


Additional
file
4:
Table
S2.
Top
competing
logistic
regression
models


(.AICc
<
2
or
Weight
>
1.5%)
from
multi-model
selection
to
predict
the


presence
(1)
and
absence
(0)
of
dengue
at
the
neighborhood
level
in


Machala
in
2010.



Competing
interests


The
authors
declare
that
they
have
no
competing
interests


Authors�
contributions


AMSI,
AGM,
MJBC,
and
RM
conceived
of
the
investigation.
EBA,
TO,
GCRC
and
KR
compiled
the
data
used
in
analyses.
AMSI,
AGM,
and
SJR
conducted
analyses
and
drafted
the
manuscript.
All
co-authors,
AMSI,
AGM,
MJBC,
JLF,
RM,
TO,
GCRC,
KR,
assisted
with
interpretation
of
the
data,
provided
feedback
for
this
manuscript,
and
read
and
approved
the
final
manuscript.


Page
14
of
16


Acknowledgements


Many
thanks
to
colleagues
at
the
MSP
and
INAMHI
for
supporting
ongoing
climate�health
initiatives
in
Ecuador.
This
work
was
funded
by
the
National
Secretary
of
Higher
Education,
Science,
Technology
and
Innovation
of
Ecuador
(SENESCYT),
grant
to
INAMHI
for
the
project
�Surveillance
and
climate
modeling
to
predict
dengue
in
urban
centers
(Guayaquil,
Huaquillas,
Portovelo,
Machala),�
and
the
Global
Emerging
Infections
Surveillance
and
Response
System
(GEIS),
grant
#P0001_14_UN.
AGM
used
computational
resources
from
the
Latin
American
Observatory
of
Extreme
Events
(www.
ole2.org)
and
Centro
de
Modelado
Cient�fico
(CMC),
Universidad
del
Zulia.
The
following
institutes
that
participated
in
this
study
also
form
part
of
the
Latin
American
Observatory
partnership
(http://ole2.org):
International
Research
Institute
for
Climate
and
Society
(IRI),
Earth
Institute,
Columbia
University,
New
York,
NY,
USA;
and
Centro
de
Modelado
Cient�fico
(CMC),
Universidad
del
Zulia,
Maracaibo,
Venezuela;
Escuela
Superior
Polit�cnica
del
Litoral,
Guayaquil,
Ecuador;
National
Institute
of
Meteorology
and
Hydrology,
Guayaquil,
Ecuador.


Author
details


1Department
of
Microbiology
and
Immunology,
Center
for
Global
Health
and
Translational
Science,
State
University
of
New
York
Upstate
Medical
University,
750
East
Adams
St,
Syracuse,
NY
13210,
USA.
2International
Research
Institute
for
Climate
and
Society
(IRI),
Earth
Institute,
Columbia
University,
New
York,
NY,
USA.
3Centro
de
Modelado
Cient�fico
(CMC),
Universidad
del
Zulia,
Maracaibo,
Venezuela.
4Department
of
Geography,
Emerging
Pathogens
Institute,
University
of
Florida,
Gainesville,
FL,
USA.
5School
of
Life
Sciences,
College
of
Agriculture,
Engineering,
and
Science,
University
of
KwaZulu-Natal,
Durban,
South
Africa.
6The
National
Service
for
the
Control
of
Vector-Borne
Diseases,
Ministry
of
Health,
Machala,
El
Oro
Province,
Ecuador.
7Facultad
de
Medicina,
Universidad
T�cnica
de
Machala,
Machala,
El
Oro
Province,
Ecuador.
8Escuela
Superior
Polit�cnica
del
Litoral,
Guayaquil,
Ecuador.
9Division
of
Nutritional
Sciences,
Cornell
University,
Ithaca,
NY,
USA.
10Center
for
Geographic
Analysis,
Harvard
University,
Cambridge,
MA,
USA.
11National
Institute
of
Meteorology
and
Hydrology,
Guayaquil,
Ecuador.


Received:
12
June
2014
Accepted:
4
November
2014



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doi:10.1186/s12879-014-0610-4
Cite
this
article
as:
Stewart-Ibarra
et
al.:
Spatiotemporal
clustering,
climate
periodicity,
and
social-ecological
risk
factors
for
dengue
during
an
outbreak
in
Machala,
Ecuador,
in
2010.
BMC
Infectious
Diseases
2014
14:610.



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RESEARCHARTICLEOpenAccessSpatiotemporalclustering,climateperiodicity,and social-ecologicalriskfactorsfordengueduringan outbreakinMachala,Ecuador,in2010AnnaMStewart-Ibarra1*,ÁngelGMuñoz2,3,SadieJRyan1,4,5,EfraínBeltránAyala6,7,MercyJBorbor-Cordova8, JuliaLFinkelstein9,10,RaúlMejía11,TaniaOrdoñez6,GCristinaRecalde-Coronel8,11andKeytiaRivero11AbstractBackground: Denguefever,amosquito-borneviraldisease,isarapidlyemergingpublichealthprobleminEcuador andthroughoutthetropics.However,wehavealimitedunderstandingofthediseasetransmissiondynamicsin theseregions.PreviousstudiesinsoutherncoastalEcuadorhavedemonstratedthepotentialtodevelopadengue earlywarningsystem(EWS)thatincorporatesclimateandnon-climateinformation.Theobjectiveofthisstudywas tocharacterizethespatiotemporaldynamicsandclimaticandsocial-ecologicalriskfactorsassociatedwiththelargest dengueepidemictodateinMachala,Ecuador,toinformthedevelopmentofadengueEWS. Methods: ThefollowingdatafromMachalawereincludedinanalyses:neighborhood-levelgeoreferenced denguecases,nationalcensusdata,andentomologica lsurveillancedatafrom2010;andtimeseriesofweekly denguecases(aggregatedtothecity-level)andmet eorologicaldatafrom2003to2012.WeappliedLISAand Moran ’ sItoanalyzethespatialdistributionofthe2010denguecases,anddevelopedmultivariatelogisticregression modelsthroughamulti-modelselectionprocesstoidentifycensusvariablesandentomologicalcovariatesassociated withthepresenceofdengueattheneighborhoodlevel.Us ingdataaggregatedatthecity-level,weconducteda time-series(wavelet)analysisofweek lyclimateanddengueincidence(2003 -2012)toidentifysignificanttime periods(e.g.,annual,biannual)whenclimateco-variedwithdengue,andtodescribetheclimateconditionsassociated withthe2010outbreak. Results: WefoundsignificanthotspotsofdenguetransmissionnearthecenterofMachala.Thebest-fitmodelto predictthepresenceofdengueincludedolderageandfemalegenderoftheheadofthehousehold,greateraccessto pipedwaterinthehome,poorhousingcondition,andlessdistancetothecentralhospital.Waveletanalysesrevealed thatdenguetransmissionco-variedwithrainfallandminimumtemperatureatannualandbiannualcycles,andwe foundthatanomalouslyhighrainfallandtemperatureswereassociatedwiththe2010outbreak. Conclusions: Ourfindingshighlighttheimportanceofgeospatialinformationindenguesurveillanceandthepotential todevelopaclimate-drivenspatiotemporalpredictionmodeltoinformdiseasepreventionandcontrolinterventions. Thisstudyprovidesanoperationalmethodologicalframeworkthatcanbeappliedtounderstandthedriversoflocal denguerisk. Keywords: Denguefever, Aedesaegypti ,GIS,Social-ecological,Climate,Spatial,Temporal,Waveletanalysis,Ecuador, Earlywarningsystem *Correspondence: stewarta@upstate.edu1DepartmentofMicrobiologyandImmunology,CenterforGlobalHealthand TranslationalScience,StateUniversityofNewYorkUpstateMedical University,750EastAdamsSt,Syracuse,NY13210,USA Fulllistofauthorinformationisavailableattheendofthearticle ©2014Stewart-Ibarraetal.;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsofthe CreativeCommonsAttributionLicense(http://creativecommons.org/licenses/by/4.0),whichpermitsunrestricteduse, distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycredited.TheCreativeCommonsPublic DomainDedicationwaiver(http://creativecommons.org/publicdomain/zero/1.0/)appliestothedatamadeavailableinthis article,unlessotherwisestated.Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610 http://www.biomedcentral.com/1471-2334/14/610

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BackgroundDenguefeveristhemostsignificantmosquito-borneviral diseaseglobally,andhasrapidlyincreasedinincidence, geographicdistribution,andseverityinrecentdecades [1-3].Thediseaseiscausedbyfourdistinctdenguevirus serotypes(DENV1-4)thataretransmittedprimarilyby thefemale Aedesaegypti mosquito,with Aedesalbopictus asasecondaryvector.Commondiseasemanifestations rangefromasymptomatictomoderatefebrileillness,with asmallerproportionofpatientswhoprogresstosevere illnesscharacterizedbyhemorrhage,shockanddeath[4]. Integratedvectorcontrolandsurveillanceremainthe principlestrategiesfordiseasepreventionandcontrolin endemicregions,asnovaccineorspecificmedicaltreatmentareyetavailable.Macrosocialandenvironmental drivershavefacilitatedtheglobalspreadandpersistence ofdengue,includinggrowingvulnerableurbanpopulations,globaltradeandtravel,climatevariability,andinadequatevectorcontrol[5-8].However,wehavealimited understandingoftherelativeeffectsofthesedriversatthe locallevel,restrictingourabilitytopredictandrespondto site-specificdengueoutbreaks. Earlywarningsystems(EWS)fordengueandother climate-sensitivediseasesaredecision-supporttoolsthat arebeingdevelopedtoimprovetheabilityofthepublic healthsectortopredict,prevent,andrespondtolocal diseaseoutbreaks[9,10].AnEWSincorporatesenvironmentaldata(e.g.,climate,alti tude,seasurfacetemperature), epidemiologicalsurveillancedata,andothersocialecologicaldatainaspatiotemporalpredictionmodel thatgeneratesoperationaldiseaseriskforecasts,suchas seasonalriskmaps.Previousstudieshavedemonstrated theutilityofthisapproachforvector-bornediseases, includingfordengue[11-13],malaria[14-16]andrift valleyfever[17].Mapsandothermodeloutputsare linkedtoanepidemicalertandresponsesystems,triggeringachainofpreventiveinterventionswhenanalert thresholdisreached. OneofthefirststepsindevelopinganEWSisto characterizethespatiotemporaldynamicsandthecovariatesassociatedwithhistoricaldiseasetransmission.This isoftendonebydevelopingGISbasemapsofepidemiological,environmental,andsocialdatatoidentifyrisk factors;andthroughtimeseriesanalysesofepidemiologicalandclimatedata.Theseanalysesrequirecrossinstitutionalintegrationofexpertiseanddata,including epidemiologicalandentomologicaldatafromministries ofhealth,climateinformationfromnationalinstitutesof meteorology,andsocial-ecologicalspatialdatafrom nationalcensusbureaus.Previousstudiesindicatethat associationsamongclimate,socioeconomicindicators anddengueriskvarybylocationandtime,indicatingthe needforanalysesofdengueriskthatconsiderthelocal contexttoexplaintransmissionmechanisms[18-24]. Importantly,theseanalysesalsoneedtoconsiderthe spatialandtemporalscalesofongoingdatacollection andsurveillanceactivitiestoensurethatthemodeloutputscansupportanoperationalEWS. TheNationalInstituteofMeteorologyandHydrology (INAMHI)ofEcuadoriscoordinatingeffortswiththe MinistryofHealth(MinisteriodeSaludPública – MSP)to developanoperationaldengueEWSforcoastalregionsof Ecuador,wherethediseaseishyper-endemic[25].Our previousstudiesinsoutherncoastalEcuadordemonstratedthepotentialtodevelopadengueEWSthat incorporatesclimateandnon-climateinformation.We foundthatthemagnitudeandtimingofdengueoutbreakswereassociatedwithanomaliesinlocalclimate, theElNiñoSouthernOscillation(ENSO),thevirus serotypesincirculation,andvectorabundance[26].Local fieldstudiesshowedthatdengueriskalsodependedon householdriskfactors(e.g.,accesstopipedwaterinfrastructure,demographics,waterstoragebehaviors,housing conditions)[22].Ourrecentadvancesinseasonalclimate forecastsindicatethattheforecastsinthisregionhave considerableskill(i.e.,predictiveability)[27,28].Building onthesepreviousstudies,thisstudywasconductedto characterizethespatiotemporaldynamics,climaticand social-ecologicalriskfactorsassociatedwiththelargest dengueepidemic(2010)onrecordinthecoastalcityof Machala,Ecuador,animportantsitefordenguesurveillanceintheregion.MethodsStudyAreaMachala,ElOroProvince,isamid-sizedcoastalport city(pop.241,606)[29]locatedinsoutherncoastal Ecuador,70kilometersnorthofthePeruvianborderand 186kilometerssouthofthecityofGuayaquil(theepicenterofhistoricaldengueoutbreaksintheregion). Dengueisanemergingdiseaseinthisregion,withthe firstcasesofdenguehemorrhagicfever(DHF)reported in2005.Thediseaseisnowhyper-endemic,withyear roundtransmissionandco-circulationofallfourserotypes.Recentmulti-countrystudiesshowedthatMachala hadthehighest Ae.aegypti larvalindicesoftensitesin othercountriesinLatinAmericaandAsia[30,31]Given thehighburdenofdisease,thehighvolumeofpeopleand goodsmovingacrosstheEcuador-Peruborder,andproximitytoGuayaquil,Machalaisastrategiclocationto monitorandunderstanddenguetransmissiondynamics. In2010,DENV-1causedthelargestdengueepidemic todate,withover4,000casesreportedinElOroprovince[32](Figure1A).InMachala,therewere2,019cases ofdenguefever(and77DHF)oranincidenceof84 denguecases(and3DHFcases)per10,000population peryear,comparedto25denguecasesper10,000populationperyearfrom2003to2009.ThegreatestburdenStewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page2of16 http://www.biomedcentral.com/1471-2334/14/610

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ofdisease(58%)duringtheepidemicwasamongindividualsunder20yearsofage(Figure2).Thenumberof casesinolderadults(i.e.,11%ofcasesreportedby peopleover50)indicatedastrongforceofinfection,and thatdenguewasarelativelynewdiseaseinthepopulation. Theepidemicoccurredduringawetterthanaverageyear, withelevatedvectorindices.Cumulativerainfallfrom JanuarytoApril2010was56%abovethe1986to2009 average.Thepercentofhouseholdswith Ae.aegypti juveniles(HouseIndex)was21.7±4.11(mean±95% CI)in2010comparedto14.3±4.70from2003to2009 [33].DatasourcesThefollowingdatafromMachalawereincludedinanalyses:neighborhood-levelgeoreferenceddenguecases, nationalcensusdata,andentomologicalsurveillance datafrom2010;andtimeseriesofweeklydenguecases (aggregatedtothecity-level)andmeteorologicaldata from2003to2012.Thesedatawereexaminedtoidentify potentialsocial-ecologicalandclimatevariablesassociated withthepresenceofdenguefeverduringthe2010outbreakinMachala,Ecuador.Epidemiologicaldatawere providedbyINAMHIthroughacollaborativeprojectwith theMSPthatwassponsoredbytheEcuadoriangovernment.Accordingly,noformalethicalreviewwasrequired, asthedatausedinthisanalysiswerede-identifiedand aggregatedtotheneighbo rhood-andcity-level,as describedbelow.EpidemiologicaldataINAMHIprovidedamapofgeoreferenceddenguecases fromMachalain2010,de-identifiedandaggregatedto neighborhood-levelpolygons(n=253)toprotectthe identityofindividuals[34].Thismapwasgenerated fromindividualrecordsofclinicallysuspectedcasesof denguefeverandDHF(aggregatedastotaldenguefever) reportedtoamandatoryMSPdiseasesurveillance system,andthemapincluded83%ofalldenguecases (n=1,674)reportedin2010.Reporteddenguecases weredefinedbasedonaclinicaldiagnosis.INAMHI alsoprovideddataforweeklydenguecasesfrom Machalafrom2003to2012forthewaveletanalysis describedbelow.Social-ecologicalriskfactorsWeextractedindividualandhousehold-leveldatafrom the2010EcuadorianNationalCensus[29]totestthe Figure1 Timeseriesofdengueandlocalclimateconditionsin2010andhistoricallyinMachala,Ecuador.(A) Weeklyreportedcasesof denguein2010andweeklyaveragecasesfrom2003to2012; (B) weeklyaveragesofrainfallandminimumairtemperature(Tmin)in2010compared totheclimatology(1986to2013averageconditions). Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page3of16 http://www.biomedcentral.com/1471-2334/14/610

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hypothesisthatsocial-ecologicalvariableswereassociated withthepresenceofdengue(Table1).Wecalculateda compositenormalizedhousingconditionindex(HCI)for eachhouseholdbycombiningvariablesforthecondition oftheroof(CR),conditionofthewalls(CW),andconditionofthefloors(CF)(Equation1).Eachofthethreevariablesrangedfrom1to3,where3indicatedpoor condition.Whensummed,thevaluesofthecomposite indexrangedfrom3(min)to9(max),andwenormalized theindexfrom0to1,where1indicatedtheworsthousing condition.HCI ¼ CR þ CW þ CF ðÞ – min ½ = max – min ðÞð 1 Þ Usingindividualandhouseholdcensusrecords,we recodedselectedcensusvariablesandcalculatedparametersasthepercentofhouseholdsorpercentofthepopulationpercensussector(n=558censussectors).Thedata elementdictionaryofrecodedvariablesinSpanishispresentedinAdditionalfile1:TableS1.Toscalethesectorlevelpolygondatatoneighborhood-levelpolygons,we usedthe ‘ isectpolypoly ’ toolinGeospatialModelingEnvironment[35,36].Weestimatedneighborhoodpopulation bycalculatingthearea-weightedsum,andestimatedall otherparametersbycalculatingarea-weightedmeans.The neighborhoodpopulationestimateswerealsousedto calculateneighborhooddengueprevalenceandpopulation densityparameters.EntomologicaldataVectorsurveillancedatafor Ae.aegypti from2010was obtainedfromtheNationalServicefortheControlof Vector-BorneDiseasesoftheMSP,andincludedquarterlyHouseIndices(percentofhouseholdswith Ae. aegypti juveniles)andBreteauIndices(numberofcontainerswith Ae.aegypti juvenilesper100households). TheaverageBreteauIndexduringthefirsttwoquarters of2010(JanuarytoJune),wasthevectorindexthatwas moststronglyassociatedwithdenguepresence(1)or absence(0)(Pearsoncorrelation,r=0.2, p =0.001)and thisperiodcorrespondedwiththepeakoftheepidemic; accordingly,weselectedthisvariabletotestinthemultivariatemodel(Table1).ClimatedataDailymeteorologicaldata(rainfallandminimumair temperature)duringthestudyperiodwereprovidedby Figure2 Dengueincidence(per10,000populationperyear)byageandgenderfor(A)denguefeverand(B)denguehemorrhagic feverinMachalain2010. Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page4of16 http://www.biomedcentral.com/1471-2334/14/610

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theGranjaSantaInesweatherstationlocatedinMachala (3°17'16 ” S,79°54'5 ” W,5metersabovesealevel)and operatedbyINAMHI.Theweeklyclimatology(19852013)andweatherduringthestudyperiodareshown inFigure1B.Weeklyaveragerainfallandminimum temperaturefrom2003to2012wereincludedinthe waveletanalysis,sinceithasbeenshownthatthesetwo climatevariablesexplainanimportantpartofthetotal varianceofdenguecasesincoastalEcuador[26].Statisticalanalyses ExploratoryspatialanalysisWeappliedMoran ’ sIwithinversedistanceweighting (ArcMap10.1)toepidemiologicaldenguedatafrom 2010totestthehypothesisthatdenguecaseswererandomlydistributedinspace.Moran ’ sIisaglobalmeasure ofspatialautocorrelation,thatprovidesanindexof dispersionfrom-1to+1,where-1isdispersed,0is random,and+1isclustered.Weidentifiedthelocations ofsignificantdenguehotandcoldspotsusingAnselin LocalMoran ’ sI(LISA)withinversedistanceweighting (ArcMap10.1).TheLISAisalocalmeasureofspatial autocorrelation[37]thatidentifiessignificantclusters (hotorcoldspots)andoutliers(e.g.,nonrandomgroups ofneighborhoodswithaboveorbelowtheexpected dengueprevalence).PreviousstudieshaveusedMoran ’ sI andLISAtotestthespatialdistributionofdengue transmission[38],includi nginEcuador[39],allowing forcomparisonbetweenstudies.Social-ecologicalriskfactorsCensusdataaggregatedtotheneighborhood-levelwere examinedtoidentifypotentialsocial-ecologicalvariables associatedwiththepresenceofdenguefever,including populationdensity,humandemographiccharacteristics, andhousingcondition(Table1).Wehypothesizedthat thepresenceorabsenceofdenguewasassociatedwith oneormoreofthesefactors;eachfactorwaspresented asasuiteofcensusvariables,representingtestablevariableensemblehypothesesinamodelselectionframework-amodelingstrategythathasbeenpreviously described[22].Variablesfortheaveragedistancetothe publichospital(TeofiloDav ilaHospital,theprovincial hospitallocatedinthecitycenter)andtheBreteau Table1Social-ecologicalparameters(meanandstandarddeviation-SD)testedinlogisticregressionmodelsto predictdenguepresence(1)andabsence(0)attheneighborhoodlevelinMachalain2010Parameter MeanSD Populationdensity Morethanfourpeopleperbedroom(%households)14.6%6.4% Populationdensity(peoplepersquarekilometer)10,8645,302 Morethanoneotherhouseholdsharingthehome(%households)2.2%1.6% Peopleperhousehold 3.880.52 Demographics Receiveremittances(%households)10.8%3.2% Peopleemigrateforwork(%households)2.2%1.2% Meanageoftheheadofthehousehold(years)45.23.0 Headofthehouseholdhasprimaryeducationorless(%households)35.9%12.9% Afro-Ecuadorian(%population)9.6%6.9% Headofthehouseholdisunemployed(%households)23.0%5.3% Headofhouseholdisawoman(%households)30.3%4.5% Housingconditions Housingconditionindex(HCI),0to1,where1ispoorcondition0.290.10 Noaccesstomunicipalgarbagecollection(%households)8.0%12.3% Nopipedwaterinsidethehome(%households)34.4%18.7% Noaccesstosewerage(%households)22.4%27.6% Noaccesstopavedroads(%households)26.7%22.3% Peopledrinktapwater(%households)32.8%11.7% Rentalhomes(%households)24.6%10.6% Othervariables Averagedistancetothecentralhospital(km)2.361.27 AverageBreteauIndexduringthefirsttwoquartersof201028.62.15 Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page5of16 http://www.biomedcentral.com/1471-2334/14/610

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Indexwerealsotestedinthemodeltoassessgeographic differences,potentialunderreporting,andotherfactors (e.g.,microclimate,vectorcontrol)notcapturedbythe censusvariables. Wecenteredallvariablesandselectedthebest-fitmodels (GLM,family=binomial,link=logit)usingglmulti,anR packageformultimodelselection[40].Allpossibleunique modelsweretestedandrankedbasedonAkaike ’ sInformationCriterion(AIC)modifiedforsmallsamplesizes(AICc) (Equation2).Wecomparedthetoprankedmodeltothe globalmodel,whichincludedallproposedvariablesas modelparameters. AIC ¼ 2 k ln L ðÞ AICc ¼ AIC þ 2 kk þ 1 ðÞ n k 1 ð 2 Þ Where k isthenumberofparametersinthemodel, n isthesamplesize,and L isthemaximizedlikelihood functionforthemodel. Parameterestimatesand95%confidenceintervals(CI) werecalculatedforvariablesinthetoprankedmodel (Table2ModelA).Varianceinflationfactors(VIF)were calculatedtoassessmulti-colinearityandmodeldispersion.Wefoundthatinclusionoftheparametersfor housingconditionandpipedwatertogetherledtooverdispersionandhighlyinflatedvarianceinthebest-fit model(Table2ModelB).Basedonthestronglinear correlationbetweenhous ingconditionandaccessto pipedwater(Figure3),wereplacedthesevariableswith theresidualsofhousingconditionregressedonaccess topipedwater,andre-ranglmultitoidentifythebestfitmodel.Theinclusionofthi svariableenabledtesting fortheeffectofhousingconditionbeyondthatwhich wasexplainedonlybyaccesstopipedwater.WaveletanalysisTounderstandthetime-frequencyvariabilityofdengue andclimateduringthe2010epidemic,weconducteda waveletanalysisofa10-yeartimeseriesofweeklyincidentdenguecases(2003-2012),rainfallandminimum temperature(Figure4A).Waveletanalysesareidealfor noisy,non-stationarydata,suchasdenguecasesdata, whichdemonstratestrongseasonalityandinterannual variability(yearlychanges)[41,42].Theseanalysesidentifysignificanttemporalscales(i.e.,definedhereasperiodswhoseassociatedwaveletpowerisstatistically significantforatleasttwocontinuousyears;Figure4) overtimeforagivenvariable,suchas2-yearcyclesor annualseasonalcyclesofdenguetransmission.Cross waveletandwaveletcoherencyallowedustocompare twotimeseries,suchasclimateanddengue,andto identifysynchronousperiodsorsignals. Thepre-processingofthetimeseriesdataforMachala followedatwo-stepmethodologydescribedelsewherein detail[27,28,43].First,wequality-controlledthetime seriesusingastandardRpackage[44]toidentifyoutliers andinconsistentvalues(e.g.,minimumtemperatures> maximumtemperatures,negativeprecipitationvaluesor negativefrequencyofdenguecases).Outliersweredefinedasdatapointsatleastthreestandarddeviations aboveorbelowthemean.Toaccountforrealoutliers (e.g.,notartifactsproducedbyhuman,instruments,or transmissionerrors),wecomparedsuspiciousvalues withdatafromnearbyclimatestations.Entriesthatwe Table2Theparametersincludedinthebest-fitlogisticregressionmodelstopredictthepresence(1)orabsence(0) ofdengueinneighborhoodsinMachalain2010Parameter Estimate95%CISEVIF P value ModelA. Intercept 0.750.46 – 1.050.15<0.001 Headofhouseholdisawoman7.770.73 – 15.173.671.140.034 Ageofheadofhousehold0.100.00 – 0.210.051.060.051 ResidualofHCIregressedonhouseholdswithnoaccesstopipedwaterinsidethehome9.043.98 – 14.372.641.05<0.001 Distancetocentralhospital 0.0005 0.0007 – 0.00.00011.20<0.001 ModelB. Intercept 7.59 14.24 – 1.323.280.021 Headofhouseholdisawoman7.300.11 – 14.833.741.180.051 Ageofheadofhousehold0.140.002 – 0.260.071.600.052 Nopipedwaterinsidethehome 3.18 6.08 – 0.41.443.640.027 HCI 9.164.06 – 14.562.663.110.001 Distancetocentralhospital 0.001 0.001 – 0.00.01.31<0.001(ModelA.)Thebestfitmodel,whichincludedtheresidualoftheHCIregressedonthevariablefornoaccesstopipedwaterinsidethehome.(ModelB.)Th esame modelisshownwithseparateparametersfortheHCIandnoaccesstopipedwaterinsidethehometoindicatethedirectionoftheeffectsoftheparameter sin themodel.VIFvaluesindicateahighdegreeofmulticollinearityinmodelBcomparedtomodelA.HighvaluesofHCIindicatepoorhousingcondition.Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page6of16 http://www.biomedcentral.com/1471-2334/14/610

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deemedtobeuncorrectablewereflaggedasmissingvalues. ThenweusedtheRpackage ‘ RHTestsV4 ’ [45-48]todetect andcorrecttemporalinhomogeneitiesinthesevariables. Theclimatetimeseriesdidnotneedsubstantialcorrections.Weeklydenguecaseda taweretransformedtoweekly incidenceusingalinearinterp olationoflocalpopulation datafromthe2001and2010nationalcensuses.Thefinal stepindatapre-processinginvolvedthenormalizationof thethreevariablestoconstrainvariability.Dengueincidence andrainfalltimeserieshadno n-normalprobabilitydensity functions,thustheywerepercentile-transformed[34]. Thewaveletanalysisofdengueandclimatedataenabled ustoidentifycommonperiodicitypatterns(e.g.,annualor biannualsignals)andanomalousclimateconditionsduringthe2010outbreak.WeusedMorletwaveletsinMatlab [49]tocomputethetimeseries ’ waveletpowerspectrum andtoidentifysignificantperiodsforeachvariable,crosswaveletpowertoidentifyperiodswheredengue-rainfall anddengue-temperaturehadhighcommonpower,and thecoherencespectratoidentifylocalco-variabilityof dengue-rainfallanddengue-temperature[50].Significance testing( p 0.05)wasconductedusinganAR1backgroundnoiseforthefirsttwospectra,andaMonteCarlo approachtocomputethesignificancelevelsinthecoherencespectrum.Statisticallysignificantregionsaredisplayedenclosedbyasolidblacklineinthewaveletplots; andconesofinfluence(COI),whereedgeeffectsincrease theuncertaintyoftheanalysis,areshownasalighter shadedregion(Figures4,5and6).Thearrowsrepresent therelativephase,whichisindicativeofthelagsbetween thetwotimeseries,asdeterminedbyfrequencyandtime [51].Thedirectionofthearrowscanbeusedtoquantify thephaserelationship:arrowspointinghorizontallytothe Figure3 Scatterplotandlinearfitforthecompositenormalized housingconditionindex(HCI)versusthepercentageofhouseholds withnopipedwaterinsidethehome,forneighborhoodswith casesofdengue(redcircle)andwithoutdengue(blacktriangle)in Machalain2010. Figure4 Dengueandclimatewavelets.(A) Normalizedtimeseriesofweeklydengueincidence,rainfall,andminimumtemperaturefrom20032012,andthewaveletpowerspectrumfor (B) dengueincidence, (C) rainfall,and (D) minimumtemperature.Theblackboxindicatesweeks1-15in 2010,when75%ofthecasesfromtheepidemicwerereported.Statisticallysignificantregionsaredisplayedenclosedbyasolidblacklineinthe waveletplots;andconesofinfluence(COI),whereedgeeffectsincreasetheuncertaintyoftheanalysis,areshownasalightershadedregion. Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page7of16 http://www.biomedcentral.com/1471-2334/14/610

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right(left)indicatethatthetwovariablesarein(anti-) phase.Whenthesignalsoftwotimeseriesareinphase, theirmaximumamplitudesoccursimultaneously.ResultsSpatialanalysesandsocial-ecologicalriskfactorsAverageneighborhooddengueincidencein2010was 76.7±14.3(95%CI)per10,000population(range:0to 775.8)(Figure7A).Thedistributionwasheavilyleft skewed,with35%ofneighborhoods(n=89)reporting zerocases(Additionalfile2:FigureS1).Denguecases duringtheepidemicweresignificantlyclustered(Moran ’ s I=0.03, p <0.001).FindingsfromtheLISAanalysisindicatedthatthereweresignificantdenguehotspots(n=15 high-highneighborhoods)inwest-centralMachala,anda smallernumberofsignificantoutliers(n=2high-low neighborhoods,n=5low-highneighborhoods)( p <0.05, Figure7b). Figure5 AsinFigure4,butforthecrosswaveletpowerspectrumfor(A)dengue-rainfalland(B)dengue-minimumtemperature. Arrowspointinghorizontallytotheright(left)indicatethatthetwovariablesarein(anti-)phase. Figure6 AsinFigure5butforwaveletcoherencespectrumfor(A)dengue-rainfalland(B)dengue-minimumtemperature. Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page8of16 http://www.biomedcentral.com/1471-2334/14/610

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Thetoprankedmodeltopredictthepresenceofdengue, identifiedthroughthemultimodelselectionprocess,wasa betterfitthantheglobalmodelthatincludedallofthe proposedsocial-ecologicalvariables(globalmodelAICc= 311,toprankedmodelAICc=291.3, AICc=19.7).The toprankedmodelincludedtheresidualoftheHCIregressed onnoaccesstopipedwater,distancefromthecentral hospital,anddemographicsof theheadsofhouseholds(i.e., olderage,femalegender)(Table2ModelA,Figure8, Additionalfile3:FigureS2).Wealsopresentedthemodel withtheHCIandaccesstopipedwaterasseparatevariables, toindicatethedirectionoftheeffectofeachvariable(Table2 ModelB).Neighborhoodsweremorelikelytoreportdengue iftheyhadpoorhousingconditionandhadgreateraccessto pipedwaterinsidethehome.Whenwecomparedneighborhoodswithsimilarhousingconditions,neighborhoodswere morelikelytoreportdenguecasesiftheyhadgreateraccess topipedwaterinsidethehome(Figure3).Inclusionofthe residualvariableinthemodel(Table2ModelA)reduced multicollinearity,asindicatedbythelowVIFs. Figure7 ThespatialdistributionofdenguetransmissioninMachala,Ecuadorin2010:(A)Dengueincidence(casesper10,000 populationin2010)perneighborhood,and(B)significanthotspots(high-high)andoutliers(high-lowandlow-high)identified throughLISAanalysis( p 0.05). Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page9of16 http://www.biomedcentral.com/1471-2334/14/610

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Figure8 Social-ecologicalparametersfromthe2010censusthatwereincludedinthetoplogisticregressionmodeltopredictthe presenceofdengue,clockwisefromtopleft:theaverageageoftheheadofthehousehold,thehousingconditionindex,distanceto thecentralhospital,thepercentofhouseholdswithafemaleheadofhousehold,thepercentofhouseholdswithnopipedwater insidethehome. Stewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page10of16 http://www.biomedcentral.com/1471-2334/14/610

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Multiplebest-fitmodelswerewithinthepredetermined thresholdcriteriaof AICc 2ofthetopmodeland weightsgreaterthan1.5%(Additionalfile4:TableS2).In additiontotheparametersincludedinthetopmodel,the followingvariableswereincludedincompetingbest-fit models:BreteauIndex,populationdensity,households withpeoplewhoemigrateforwork,andhouseholds withoutaccesstopavedroads.TemporalclimateanalysesWefoundthatmultipletemporalscaleswereinvolvedin localdenguetransmissiondynamics,asshowninthe waveletpowerspectrumfordengueincidence(Figure4B). Inwaveletanalyses,strongsignificantsignalsatacertain frequencyareassociatedwithpersistent(quasi)periodic cyclesinthetimeseries(e.g.,a1-yearbandindicatespresenceofannualcycles).Therewasastrongandsignificant signalforthe~2-yearperiodicbandfordengueincidence. Therewasalsoasignificantsignalforthe~1-yearperiodic band,althoughitwaslessfrequent(e.g.,2003,2006, and2011).Signalsaroundandabovethe4-yearperiodic bandwerenotconsidered,astheyfellinsideoftheCOI (Figure4B).Theseresultssuggestthatdengueperiodicityinthislocalityisnoton lyannual(~1year),butthat thereisalsoanimportantbiannualcycle(~2year),that mayreflecttypicaltimescalesofextrinsic(e.g.,climate) andintrinsic(e.g.,immunologic)processesinvolvedin theoccurrenceofdengueforthisregion. Therainfallandminimumtemperaturespectrain Figure4C,Ddemonstratedastrongannualsignal(1-year periodicband),inagreementwiththeannualdenguecycle intheregion.Therewasnoevidenceofrelevantchanges invariabilityforminimumtemperature;thecorrespondingsignalinthepowerspectrumiscontinuousaround theannualband.Incontrast,therewerefluctuationsin the~1-yearbandforprecipitation,likelyassociatedwith periodsoflowprecipitation.Beginningaround2006, bothclimatevariablesdemonstratedsignificantpower aroundthe2-yearband,afeaturethatismostnoticeableintherainfalldata,pa rticularlyinrecentyears. Thecross-waveletpowerspectrafordengueandrainfall (Figure5A),anddengueandminimumtemperature (Figure5B)showedregionsinthetime-frequencyspace withhighcommonpowerinthe1-yearand2-yearbands, suggestingarelationshipbetweenclimateanddengue incidenceatbothtimescales.Thecorrespondingwavelet coherencespectra,however,i ndicatedthatthedengueand rainfallco-varymostlyinthe2-yearband(Figure6A),while dengueandminimumtemperatureco-varymostlyinthe 1-yearband(Figure6B).This suggeststhattemperature andrainfallhavewell-differentiatedrolesindenguetransmission.Thedirectionsofthearrowsintheplotsindicatea slowchangeofphaseintheco-variabilityofdengueand rainfallinthe2-yearband,a pproachingin-phasebehavior inlate2009,andweobservedsyn chronizedco-variability forthe1-yearbandin2009andatthestartof2010.These resultshighlightthedistinctrolesoftheseclimatevariables indenguetransmissionatdifferenttemporalscales,andthe importanceofthephaseandtimingofclimatevariables withrespecttodenguetransmission. Wefoundthatthe2010epidemicepisodecouldbe characterizedbyacombinationofannualandbi-annual signalsindenguetransmissionandclimatevariables.The outbreakwascharacterizedbyacombinationofin-phase variabilityofabovenormalminimumtemperatures,and quasi-in-phaseabovenormalrainfallepisodesassociated withthelate2009toearly2010moderateElNiñoevent (seearrowspointingrightalongthe1-yearbandatthe bottomofFigures5and6).Thetimesseries(1995-2010) ofmonthlyanomaliesindenguecasesfromElOro province,ENSO,temperatureandrainfallhavebeen previouslydescribed(SeeFigure2inStewartIbarra& Lowe2013)[26].Thisanalysisdemonstratedthatthe observedeffect(quasi-simultaneityinthevariabilityof dengue,temperatureandrainfall)waspresentinearly 2010,butnotinanyotheryearoftheperiodunderstudy (Figures5and6).DiscussionDengueisthemostimportantmosquito-borneviral diseaseglobally,andhasincreasedinincidenceanddistributiondespiteongoingvectorcontrolinterventions duringthelastthreedecades[1-3].Todate,wehavea limitedunderstandingofthespatiotemporaldynamicsof denguetransmission,particularlyatthelocalscale,dueto thecomplex,non-stationaryrelationshipsamongdengue infection,climate,vector,andvirusstraindynamics [41,52-54];andthegeographicandtemporalvariationin thesocial-ecologicalconditionsthatinfluencerisk[18-21]. Morerobustanalysistools,suchaswaveletanalysesand multimodelinference,andtheincreasingavailabilityof geospatialepidemiological,climate,andsocial-ecological datahaveincreasedourabilitytoexplorethesedynamics. StudiessuchasthisprovidecriticalinformationtoimprovediseasesurveillanceandtodevelopanEWSand otherevidence-basedinterventions. Inthisstudy,wefoundthatneighborhoodswithcertain social-ecologicalconditionsweremorelikelytohavecases ofdengueduringthelargestoutbreaktodateinElOro Province.Denguecaseswereclusteredinneighborhoodleveltransmissionhotspotsnearthecitycenterduringthe epidemic.Riskfactorsincludedpoorhousingcondition, greateraccesstopipedwaterinsidethehome,less distancetothecentralhospital,anddemographicsofthe headsofhouseholds( i.e., olderage,femalegender).In analysesof10yearsofweeklyepidemiologicalandclimate data,wefoundthatdengue,rainfallandminimum temperatureco-variedandhadcommonpowerat1-yearStewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page11of16 http://www.biomedcentral.com/1471-2334/14/610

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and2-yearcycles,withquasi-synchronizedhigherthan averagerainfallandminimumtemperatureslikelycontributingtothe2010dengueoutbreak.Thisstudycontributes toongoingeffortsbyINAMHIandtheMSPofEcuador todevelopadenguepredictionmodelandearlywarning system.Findingsfromthisstudywillinformthe developmentofdenguevulnerabilitymapsandclimatedrivendengueseasonalforecaststhatprovidetheMSP withinformationtotargethigh-riskregionsandseasons, allowingformoreefficientuseofscarceresources[9].Spatialdynamicsandsocial-ecologicalriskfactorsInMachala,arelativelysmallandheterogeneouscity, therewasevidenceofunequalexposureorunequal reportingofdengue.Duringtheepidemic,denguetransmissionwasfocusedinhotspotsinthewest-centralurban sector,amiddle-tolow-incomeresidentialareawithmoderateaccesstourbaninfrastructure.Althoughpeoplehad accesstobasicservices,ourpreviousstudiessuggestthat denguecontrolinthesecommunitiesmaybelimitedby thecostofhouseholdvectorcontrol,lackofsocial cohesion,andlimitedengagementwithlocalinstitutions [55].Previousstudiesthatusedspatialclusteringstatisticsalsofoundevidenceofsignificantclusteringofdenguetransmissionacrosstheurbanlandscape[18,56-58]. ApreviousstudyinGuayaquil,Ecuador,identified neighborhood-leveldenguehotandcoldspots,andfound thatthelocationofhotspotsshiftedoverthe5-year period,highlightingthespatiallydynamicnatureofdengueriskandtheimportanceofmultiyearstudies[39]. LongitudinalfieldstudiesinThailandfoundevidenceof fine-scalespatialandtemporalclusteringofdenguevirus serotypesandtransmissionattheschoolandhousehold levels[59,60].Focaltransmissionpatternsarelikely associatedwiththelimitedflightrangeofthe Ae.aegypti mosquito.RecentstudiesinPerudemonstratedtheimportanceofhumanmovementpatternsindetermining spatialdenguetransmissiondynamicswithinanurban area[61,62].Ataregionalscale,dengueoutbreaksare likelyinfluencedbyhumanmovementnorthandsouth alongtheEcuador-Peruborder.Futurestudiesshould continuetoinvestigatetheregionaleffectsofcross-border movementofpeopleandgoods,andthelocaleffectsof intra-urbanmovementbetweenwork,school,andhome tobetterunderstandthespatialdynamicsofdengue transmission. WefoundthatthecombinationofHCIandaccessto pipedwaterwasthemostimportantriskfactorfordengue transmission,asindicatedbythemagnitudeofthebest-fit modelparameterestimate(Table2ModelA);thisparameterwasalsoasignificantvariableinallothertopmodels (Additionalfile4:TableS2).Neighborhoodsweremore likelytoreportdengueiftheyhadpoorhousingconditions(likelyassociatedwithlowerincome)andgreater accesstopipedwaterinsidethehome(likelyassociated witholder,establishedcommunitieswithaccesstourban infrastructure).Thisapparentparadoxicalrelationship suggeststhathouseholdwaterstoragebehaviorsplayedan importantroleinthe2010dengueoutbreak.Inour experiencelow-incomehouseholdsinMachalawith accesstopipedwatertendtostorewaterincontainersin thepatioasasecondarywatersource,sincewatersupply interruptionsarecommon.Thesesecondarywatercontainersareoftenuncovered,andthecontainersbecome ideal Ae.aegypti larvalhabitatduringtherainyseason.In contrast,low-incomehouseholdswithoutaccesstopiped waterarelikelytostorewaterincontainersastheir primarywatersource(e.g.,55gallondrums),frequently fillingandemptyingthecontainersandthuspreventing Ae.aegypti fromdevelopingintoadultmosquitoes. Neighborhoodswerealsoatgreaterriskofdengueif theywereclosertothecentralhospital,reflectingeither spatiallybiasedreportingand/oratrueincreasein transmissionnearthecitycenter.Thisvariablewasalso significantinallofthetopmodels(Additionalfile4: TableS2).GiventhesmallsizeofthecityofMachala (~5kmacross)andeasyaccesstolow-costpublic transportation,traveltimetothehospitalwasnotlikelyto bealimitingfactor.However,peoplefromlowerincome communitiesmaybelesslikelytoseekmedicalcaredue tothecostofmedicineandthehighcostofmissingwork, leadingtounderreportingfromtheurbanperiphery.Itis alsopossiblethatpeopleresidingnearthecitycenterin Machalawereatgreaterriskbecausetheymayhavebeen lesswillingtocooperatewithvectorcontroltechnicians (E.Beltran, pers.comm. ),dueinparttothemisconception thatdengueisaproblemofpoorcommunitiesatthe urbanperiphery[55].Householdsintheseareasmayalso beatgreaterriskbecausetheystorewaterasasecondary watersource,asdescribedabove.Thesefindingshighlight thecomplexityoftheculturalandbehavioralfactors influencingdengueriskandtheimportanceoflocal-level studiesthatconsiderthesocialcontext. Ourfindingsareconsistentwithapreviouslongitudinal fieldstudyofhouseholdriskfactorsfor Ae.aegypti in Machala,whereitwasfoundthatpoorhousingcondition andaccesstopipedwaterinsidethehomewerepositively associatedwiththepresenceof Ae.aegypti pupae[22].This priorstudyfoundthat Ae.aegypti weremoreabundantin thecentralurbanareathathadbetteraccesstoinfrastructurethanintheurbanperiphery[22].Interestingly,the sameriskfactorsemergedinthestudypresentedhereand thepriorfieldstudydespitedi fferencesinrainfall(i.e.,the fieldstudywasconductedoneyearaftertheepidemic, duringadrierthanaverageyear)anddifferencesinspatial scale(i.e.,household-versu sneighborhoodlevel).These findingsindicatethathigh-riskhouseholdscouldbeidentifiedandtargetedusingacombinationofcensusdataandStewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page12of16 http://www.biomedcentral.com/1471-2334/14/610

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alocallyadaptedrapidsurveyofhousingconditions, similartothePremiseConditionIndex,anaggregate indexmeasuringhousecondition,patiocondition,and patioshade,whichhasbeenvalidatedinothercountries [63,64].TheHCIandthecombinedHCI-wateraccess variablesdevelopedinthisstudyshouldbeexploredand validatedasdenguepredictorsinfuturestudiesinthisregion. Ae.aegpyti juvenileindiceswereincludedintwoofthe topsevenbest-fitmodelstopredictthepresenceof dengueinneighborhoods(Additionalfile4:TableS2).A previousstudyinElOroProvincefoundthat Ae.aegypti indices(HouseIndex)werepositivelyassociatedwith dengueoutbreaksattheprovincelevel[26].Although pupaloradultindicesareconsideredbetterpredictorsof dengueriskthanlarvalindices[65],ourfindingssuggest thatlarvalindicesmayhavesomepredictivepowerinthis region.InEcuador,entomologicalsurveillanceislimited tolarvalindices,andneighborhoodsarerarelysampledin consecutiveperiodsinagivenyearduetolimitedresources.Thesefindingshighlighttheneedforadditional studiesofthevector-denguedynamicsinthisregionand localevaluationsoftherobustnessofvectorabundance measuresinordertostrengthencost-effectiveentomologicalsurveillancesystems.ClimateanddengueperiodicityThewaveletanalysisinthisstudyprovidedanuanced understandingoftherelationshipsamonglocaldengue transmissionandclimatevariablesatmultipletemporal scales.Theanalysisof10yearsofweeklyepidemiological andclimatedatafromMachalaprovidedevidenceof significant1-yearand2-yearcyclesindengue,rainfalland minimumtemperature.The1-yearcyclesofminimum temperatureandrainfalllikelycontributedtotheannual denguecyclesobservedinthepowerspectrum.Thisfindingwasexpected,aspreviousstudieshavedocumented significantannualdenguecyclesinthisregion[26].Interestingly,wealsofoundevidenceof2-yearcyclesinthe rainfallwaveletpowerspectrumthatwerelikelyassociated withbiannualcyclesofdenguetransmission,apattern thatwaspreviouslyundocumentedinEcuador. Indeed,ouranalysessuggestthatthe2010dengue epidemiccouldberelatedtoatimelycoincidenceofabove normalminimumtemperaturesandabovenormalrainfall episodesduringthemoderate2009to2010ElNiñoevent. Previousstudiesinthisregionhaveshownthat Ae.aegypti abundanceisassociatedwithrainfallandminimum temperature[22].In2010,rainfallfromFebruaryand March,thepeakofdengueseason,wasalmostdoublethe long-termaverage(89%and81%aboveaverage,respectively),likelyincreasingtheavailabilityofmosquitolarval habitat.Temperatureandtemperaturefluctuationsinfluenceratesofmosquitodevelopmentandvirusreplication [66-71].Theslowrateofclimatephasechangeobserved inthisanalysissuggeststhepotentialtomonitorthe climateinthisregiontoidentifyfuturetimeperiodswith synchronousclimateconditionssimilarto2010,thatmay increasetheriskofadengueoutbreak. Ourresultsindicatethatthe2-yearbandinprecipitation isanimportantcomponentintheco-variabilityofdengue incidencefortheperiodunderstudy,althoughitsrolein the2010dengueepidemicrequiresfurtherinvestigation. ThisperiodicbandisnotuniquetoMachala.Twoto three-yearcyclesofdenguetransmissionhavebeen reportedinotherpartsoftheworld[41,53],particularlyin yearsassociatedwithElNiñoevents.Thestatistically significant2-yearbandispresentinthedenguepower spectrumfortheentiretimeseries.Thisisnottruefor rainfallorminimumtemperature,whosevariabilityinthe regionisstronglyassociatedwithElNiño-Southern Oscillation(ENSO).ThissuggeststhatalthoughENSO hasastronginfluenceintheoccurrenceofdengueepidemicsincoastalEcuador,othervariables(e.g.,immunity) arealsoinvolvedintheprocessand/orthatthereisa persistentmechanismfortheclimate ’ sbiannualcontributioninthedenguespectrum.Itisinterestingtonotethat similar2-yearcycleshavebeenreportedfordengueand malariainmountainlocationsinPeru[72],butnotalong thePeruvianCoastorAmazon[73].Wehypothesizethat thebiannualsignalfoundinPeruandMachalaisrelated toanadditionalclimatemodepresentovertheAndesin thisregion[28]inadditiontoENSO.Machalamaybe uniquelysituatedtocaptureclimatesignalsfromENSO andtheso-called[28]Andeanmode,givenitsproximity totheAndeanfoothillsandthestrongcoupledclimateoceansystem(i.e. , teleconnections)presentintheregion.LimitationsAlthoughthisstudyrevealedpatternsofclimateandsocialecologicalconditionsasimportantdriversofdengue transmission,thisstudyhassomelimitations.Itshouldbe notedthatnon-climatefactorsthatwereundocumentedin thisstudy(e.g.,populationimmunity,vectorcontrol interventions)arealsokeydriversofinterannualvariability indengue[26,74,75]andmostlikelyinfluencedthe2010 outbreak.The1-yearofspatiallyexplicitepidemiological dataconstrainedourabilitytoassesswhetherthesocialecologicalfactorsassociatedw iththespatialdistributionof denguetransmissionwereconsistentintime.The10-year timeseriesofweeklydenguedatawasnotavailableatthe appropriatespatialscalefor thisanalysis.Withmultiple yearsofdata,wecouldevaluatewhetherdenguetransmissionatthebeginningofthedengueseasonoratthe beginningofanepidemicismorelikelytobegininneighborhoodswithsimilarcharacteristics,toassesswhether therearepersistenthigh-risk,hotspotneighborhoodsthat triggeroutbreaks.Theanalyseswerealsolimitedbyalack oflaboratoryconfirmationforcasesorinformationaboutStewart-Ibarra etal.BMCInfectiousDiseases 2014, 14 :610Page13of16 http://www.biomedcentral.com/1471-2334/14/610

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theimmune,nutritional,orhealthstatusofthepopulation. Wearecurrentlycollaborat ingwiththeMSPtoimprove denguediagnosticinfrastr uctureintheregionandto reducethetimelagbetweenepidemiologicalreportingand vectorcontrolinterventions.Importantly,theMSPis undergoingareorganizationanddecentralizationprocess tomergethehealthandvectorcontroldivisionsatthelocal level,withthegoalofimprovinginformationflowsand linkingresponsestoevidence-basedinterventions.ConclusionsTheresultsofthisstudyhighlighttheimportanceofincorporatingclimateandsocial-ecologicalinformationwith georeferencedandclinicallyvalidatedepidemiologicaldata inadenguesurveillancesystem.InvestigatorsinEcuador areexploringthedevelopmentofweb-basedGISfornationaldenguesurveillanceusingopen-accesssoftware.GIS isaneffectivetooltointegratediversedatastreams,suchas dynamic,real-timeepidemiologicalandclimatedatawith staticvulnerabilitymapsgen eratedfromcensusdata.Open accesstoolsareespeciallyimpo rtantinresource-limitedsettings,andanalysispackagesta rgetedtodenguearebecomingavailable[76].Web-basedGIStoolshavebeen developedforglobaldenguesurveillance,suchastheCDC ’ s DengueMap,andforlocaldenguesurveillanceresearch projects[77,78].National-leveldengueGISinitiativeshave beendevelopedincountriessuchasMexico[79],where MinistryofHealthpractition ersandsoftwaredevelopers jointlydesignedthesoftwareplatform.Thiscollaborativeapproachtointegratediversedata streamswillideallyprovide publichealthdecision-makerswithinformationtoassess interventionprograms,allocateresourcesmoreefficiently, andprovidethefoundationfor anoperationaldengueEWS.AdditionalfilesAdditionalfile1:TableS1. Spanishdictionaryofcensusvariables evaluatedinthemultivariatemodeltopredictthepresenceofdengue. Additionalfile2:FigureS1. Histogramshowingthedensitydistribution ofneighborhooddengueincidenceinMachala,2010(n=253). Additionalfile3:FigureS2. Scattermatrixofparametersincludedin thetoplogisticregressionmodeltopredictthepresenceofdenguein neighborhoodsinMachalain2010. Additionalfile4:TableS2. Topcompetinglogisticregressionmodels ( AICc<2orWeight>1.5%)frommulti-modelselectiontopredictthe presence(1)andabsence(0)ofdengueattheneighborhoodlevelin Machalain2010. Competinginterests Theauthorsdeclarethattheyhavenocompetinginterests Authors ’ contributions AMSI,AGM,MJBC,andRMconceivedoftheinvestigation.EBA,TO,GCRC andKRcompiledthedatausedinanalyses.AMSI,AGM,andSJRconducted analysesanddraftedthemanuscript.Allco-authors,AMSI,AGM,MJBC,JLF, RM,TO,GCRC,KR,assistedwithinterpretationofthedata,providedfeedback forthismanuscript,andreadandapprovedthefinalmanuscript. Acknowledgements ManythankstocolleaguesattheMSPandINAMHIforsupportingongoing climate – healthinitiativesinEcuador.ThisworkwasfundedbytheNational SecretaryofHigherEducation,Science,TechnologyandInnovationof Ecuador(SENESCYT),granttoINAMHIfortheproject “ Surveillanceand climatemodelingtopredictdengueinurbancenters(Guayaquil,Huaquillas, Portovelo,Machala), ” andtheGlobalEmergingInfectionsSurveillanceand ResponseSystem(GEIS),grant#P0001_14_UN.AGMusedcomputational resourcesfromtheLatinAmericanObservatoryofExtremeEvents(www. ole2.org)andCentrodeModeladoCientífico(CMC),UniversidaddelZulia. Thefollowinginstitutesthatparticipatedinthisstudyalsoformpartofthe LatinAmericanObservatorypartnership(http://ole2.org):International ResearchInstituteforClimateandSociety(IRI),EarthInstitute,Columbia University,NewYork,NY,USA;andCentrodeModeladoCientífico(CMC), UniversidaddelZulia,Maracaibo,Venezuela;EscuelaSuperiorPolitécnicadel Litoral,Guayaquil,Ecuador;NationalInstituteofMeteorologyandHydrology, Guayaquil,Ecuador. Authordetails1DepartmentofMicrobiologyandImmunology,CenterforGlobalHealthand TranslationalScience,StateUniversityofNewYorkUpstateMedical University,750EastAdamsSt,Syracuse,NY13210,USA.2International ResearchInstituteforClimateandSociety(IRI),EarthInstitute,Columbia University,NewYork,NY,USA.3CentrodeModeladoCientífico(CMC), UniversidaddelZulia,Maracaibo,Venezuela.4DepartmentofGeography, EmergingPathogensInstitute,UniversityofFlorida,Gainesville,FL,USA.5SchoolofLifeSciences,CollegeofAgriculture,Engineering,andScience, UniversityofKwaZulu-Natal,Durban,SouthAfrica.6TheNationalServicefor theControlofVector-BorneDiseases,MinistryofHealth,Machala,ElOro Province,Ecuador.7FacultaddeMedicina,UniversidadTécnicadeMachala, Machala,ElOroProvince,Ecuador.8EscuelaSuperiorPolitécnicadelLitoral, Guayaquil,Ecuador.9DivisionofNutritionalSciences,CornellUniversity, Ithaca,NY,USA.10CenterforGeographicAnalysis,HarvardUniversity, Cambridge,MA,USA.11NationalInstituteofMeteorologyandHydrology, Guayaquil,Ecuador. 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