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Spatial-Temporal Analysis for Identification ofVulnerability to
Dengue in Seremban District, Malaysia
Naim, M. R.,' Sahani, M..,^ * Hod, R.,' Hidayatulfathi, O.,^
idrus, S.,* Norzawati, Y.,' Hazrin, H.,' Tahir,A.,' Wen, T. H.,^
King, C. C., Zainudin, M. A.J'GIS Project Team, Institute of Public
Health, Ministry of Health, Malaysia^Faculty of Health Sciences,
Universiti Kebangsaan Malaysia, E-mail: [email protected]^ Faculty
of Medicine, Universiti Kebangsaan, Malaysia* LESTARI, Universiti
Kebangsaan, Malaysia'Department of Geography, National Taiwan
University, Taiwan^Graduate School of Public Health, National
Taiwan University, Taiwan^Negeri Sembilan Health Department,
Ministry of Health, MalaysiaCorresponding Author: Mazrura
Sahani
.a
AbstractDengue is a major public health threat in Malaysia,
which is known for the hyperendemicity with all the jourserotypes
of the dengue virus circulating concurrently. Annual dengue cases
reported were 43,000 cases for2013, and this imposed a heavy toll
on the resources for dengue prevention and control program.
Theobjective of mapping in our study is to determine the spatial
clustering of the dengue cases and to identify theareas that are
vulnerable to dengue outbreaks. A Geographical Information System
(GIS) was used to assessthe vulnerability of Seremban district.
Dengue data were obtained from the Ministry of Health. We
determinedthe spatial distribution, the average distance of dengue
cases and identified hotspots areas using the Moran-sI, Average
Nearest Neighbourhood (ANN), Kernel density estimation.
Vulnerability to dengue was assessedwith the spatial temporal
analyses and Local Indicator for Spatial Autocorrelation (LISA).
From 2003-2009Seremban recorded 6076 dengue cases. Moran-s I showed
the cases occurred in clusters with a Z-score of16.384 (p
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theoretical and practical values that will furtherincrease
significant global awareness of theinfectious disease (Yang et al.,
2007). The usage ofGIS to control and prevent dengue outbreak
shouldbe explored extensively and put under seriousconsideration
(Houghton et al , 1996, Yang et al.,2007, Aziz, 2011 and Er et al ,
2010). Map showingthe mosquito vector data (presence or
abundancepatterns, epidemiological data (location of dengueor
dengue cases pattern scene), or vector controlcoverage performed a
useful tool in vector anddengue control programs. Map can be used
to guideand assess the progress of operations and activitiesto
disseminate information to outside parties. Forexample, the map can
help to alert people to the areain a city with a particularly high
risk exposure todengue virus (Eisen and Lozano-Fuentes,
2009).Dissemination of spatial and temporalcharacteristics of the
spread of the disease can bemonitored and used by the party
responsible forcarrying out control measures. Identifying keynodes
in the spread of infection, defined by timespent at unique
locations, can help us understandpast infectious episodes, and
predict futuredevelopments (Meliker and Sloan, 2011). With
thespread of disease models available, this diffusionprocess is
dynamically simulated and visualized intwo or three dimensions
spatial scale (Yang et al.,2007). High-risk populations can be
identified andthe location of the spatial distribution pattern
isdescribed and transmission behaviors of the diseasemay be found.
Prevention through more effectivedecisions can be made by
government and publichealth institutions through the provision of
bettermedical resources using GIS network analysismodel (Yang et
al., 2007). The objective of thisstudy was to identify the
vuhierable areas to dengueusing spatial temporal analysis.
Vulnerable areas inthis study area will be explained using
spatial-temporal indexes (the frequency index, the durationindex
and intensity index) of dengue cases andLocal Indicator of Spatial
Autocorrelation (LISA).
2. Materials and Methods2.1 Study Area and DataThe Seremban
district is one of the 7 districts inNegeri Sembilan, which is
located on the west coastof Peninsular Malaysia. The administration
ofSeremban district is divided between the SerembanMunicipal
Council (inner Seremban) and the NilaiMunicipal Council (outer
Seremban). Seremban isthe capital of Negeri Sembilan and consists
ofmostly urbanized area.
Seremban was selected as our study area because ithas the
highest recorded of dengue cases for NegeriSembilan. We focused on
surveillance data, basedon reported dengue cases from the year 2003
to2009. The data was obtained from Seremban districthealth office.
Dengue cases were based on clinicallyconfirmed cases. The MOH
Clinical PracticeGuidelines (Ministry of Health Malaysia,
2010b)were used to diagnose and classify DF and DHF.The software
used for mapping was ArcGIS 10. Thedengue data was used to map the
location of eachdengue case using the Global PositioningSystem
(GPS). The data obtained from the fieldobservations were then
utilized to construct adatabase as incidence points. The
statistical analysisused in our research was the
spatial-temporalanalyses such as the Moran's Index, ANN,
Kerneldensity and the spatial-temporal indexes. We alsoused the
Local Indicator of Spatial Autocorrelation(LISA). LISA is a spatial
risk index to identifyspatial patterns including clustering and
outliers(Anselin, 1995, dland, 1998 and Wen et al , 2010).This
research focused on the clustering of thedengue cases which can be
identified using thefeatures of frequency-time-incident cases and
thenumber of cases occurring in a certain period oftime or in an
epidemic. These factors give a true andmore realistic picture of
the epidemic events.
2.2 Moran 's IndexSpatial autocorrelation of Moran's index was
usedto analyse whether the dengue cases withinthe study district is
spatially correlated. Itis determined by calculating a mean for
observationand then comparing the value of each incident withthe
value at all other locations (Wen et al., 2010).Moran's I values
range from -1 which is negativespatial autocorrelation (approaching
scatteringincident) to +1 of positive spatial
autocorrelation(approaching grouping incidents). Valueapproaching 0
refers to the random distributionpattern.
2.3 Average Nearest Neighbourhood (ANN)Average nearest
neighborhood (ANN) was used toassess the pattern of clustering of
the incidence ofdengue. ANN calculates the distance between
eachfeature centroid to its nearest centroid. It thencalculates the
average nearest neighbordistance. The average ratio was calculated
as theaverage of the observed distance divided by theexpected
average distance (Wen et al., 2010).
3 2 Spatial-Temporal Analysis for Identification of
Vulnerability to Dengue in Seremban Distriet, Malaysia
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2.4 Kernel Density EstimationHot spots analysis using Kernel
density estimationinterpolation techniques was carried out
Kerneldensity estimation is a technique used to identifyhigh-risk
areas based on the pattern of diseaseincidence or the hotspot. It
calculates the density ofpoint features around each output raster
cell (Bithell,1990). This hotspot identification is an
importanttool to focus on the control activities to preventirther
transmissions of the mosquitoes-bornedisease.
2.5. Spatial-Temporal Indexes and LISAVuberability of the
population towards the denguecases in this study was assessed by
conducting aspatial autocorrelation in which all three of
thespatial-temporal indexes (frequency index, durationindex and
intensity index) were calculated andLISA. The frequency of a
disease during anepidemic can be described by a probability that
oneor more laboratory confirmed cases occurred incertain week(s)
out of the total epidemic period(Wen et al., 2006).
Occurrence index (a) can be defined as:
a = number of weeks of the epidemic / number ofweeks in the
year
An epidemic period usually involves severalepidemic waves.
Duration index () of an epidemiccan be described as the average
number of weeks inwhich occurrence of cases persists during the
wholeepidemic period (Wen et al., 2006).
Duration index () ean be defined as:
= number of epidemic weeks / total epidemicwave
The duration index () is very important forpractitioners and
administrators of public health andenvironment agencies because it
refiects theeffectiveness of the prevention or control
strategiesused during the epidemic. Intensity refers to thelikely
magnitude within an epidemic wave whenmore than one case occurs
(Wen et al.,2006). Incidence rate is taken as an index tomeasure
the magnitude of new cases appearingduring a specified
period.Intensity index (y) is measured as:Y = incidence rate/ total
epidemic wave
The vulnerability assessment is presented with amap of the local
indicators of spatial autocorrelation(LISA). LISA considers the
possible combinationsof the three indices, namely O, D and I, and
todistinguish these risk characteristics. Then, the high-risk areas
were identified and mapped out andcompared (Wen et al, 2010). LISA
provides eightpossible types of indexes summarized as O
foroccurrence index, D for duration index and I forintensity
index.
3. ResultsA total of 6076 dengue cases were recorded inSeremban
distriet from 2003 to 2009. The highestincidence of dengue, during
that period was in 2003,followed by 2004 and 2008 with
respectiveincidence rates of 37.4, 23.5 and 20.5 per
10,000populations. The spatial distribution of dengue casesin the
study area was identified using spatialautocorrelation i.e Moran's
index. This indexmeasures the autocorrelation based on the
locationand distribution of the dengue cases. The Moran'sIndex of
dengue incidence was 0.16 at p
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district A
district B
Figure 1 : Hotspot areas consist of twosub districts
Figure 2: Vulnerability areas in 2003. Eight types ofvulnerable
area were defined fi-om all the possiblecombinations of high values
of the three temporalindices in year 2003. Red area is the most
vulnerable
Sub district A
Figure 3: Vulnerability areas in 2004. Eight typesof vulnerable
area were defined firom all thepossible combinations of high values
of the threetemporal indices in year 2004
Figure 4: Vulnerability areas in 2005. Eight typesof vulnerable
area were defined from all thepossible combinations of high values
ofthe threetemporal indices in year 2005. Most of red areasare in 3
subdistricts in Seremban
3 4 Spatial-Temporal Analysis for Identification of
Vulnerability to Dengue in Seremban District, Malaysia
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Figure 5: Vulnerability areas in 2006. Eight typesof vulnerable
area were defined from all thepossible combinations of high values
of the threetemporal indices in year 2006
Figure 6: Vulnerability areas in 2007. Eight typesof vulnerable
area were defined from all thepossible comhinations of high values
ofthe threetemporal indices in year 2007. Highest vulnerableareas
is only in a subdistrict in Seremban
.g
SD
Sub district A
Figure 7: Vulnerability areas in 2008. Eight typesof vulnerahle
area were defined from all thepossible combinations of high values
of the threetemporal indices in year 2008
Figure 8: Vulnerahility areas in 2009. Eight typesof vulnerahle
area were defined from all thepossible combinations of high values
of the threetemporal indices in year 2009
hucniational Journal of Geoinformatics. Vol. 10, No. 1, March,
2014 3 5
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In 2006, there were also three sub districts with highODI. There
were 12 units with high ODI (Figure 5).In 2007, LISA analysis
showed there was only onehigh ODI area consisting of two units
(Figure 6). In2008, LISA analysis showed clearly the 3 high
ODIareas were located at 3 different sub districts (Figure7). This
area consisted of 8 units of the HighODI. In 2009, there were 10
units with high ODIlocated in two of sub-districts (Figure 8).
4. DiscussionThis study evaluated the
spatial-temporaldistribution of dengue cases in Seremban, an
urbandistrict in Malaysia. Data manipulation and GISpresentation
and spatial statistics were used to mapthe distribution of the
dengue incidence in the studyarea. The study found a significant
clustering patternof the dengue eases in the study area with
Moran'sIndex of 0.16. The Average Nearest Neighborhood(ANN) found
was a distance of 55 meters betweendengue eases and other cases
(with z score of -109.73 and p value less than 0.05). This differs
withEr et al., (2010) that found a Moran's Index of 0.75with an
average nearest neighborhood (ANN)analysis of 380 m. The difference
could beattributed to the density of population and thepattern of
housing in both areas. The district in thisstudy is more developed
compared to the areas ofstudy in Er et al, (2010).The significance
of the 55meters ANN in this study means that the control
andprevention activities such as destruction of breedingsites,
fogging and health promotion activities couldbe focused on a
smaller area, hence the control oftransmission should be easier to
achieve. Moran'sIndex reported in by Nakhapakom andJirakajohnkool
(2006), and in Rio de Janeiro, Brazilby Almeida et al, (2009)
showed similar findings ofsignificant clustering patterns of dengue
cases. Thisstudy showed the potential of an area at risk even inthe
absence of data on the mosquito density or otherenvironmental
factors. However, Aziz (2011)reported a spatial relationship
between theincidences of dengue with the environmental factorsthat
were associated with the breeding ofmosquitoes. For dengue risk,
areas marked withdark color signified the locations with the
maximumnumber of dengue incidence. Dengue density mapusing LISA
analysis showed that most cases wereconcentrated in two
sub-districts as discussed below.In 2003, LISA analysis showed that
the high-ODImostly occurred in the two sub districts. The year2004
recorded the absence of high-ODI. There wasa decline in dengue
cases as compared with thoserecorded in 2003. However, in 2005, the
high ODI
areas of dengue cases were seen to beincreasing. This showed
that in some areas, thedengue control program was still not
adequate. For2005, the areas that have high ODI was mainly inthe
three sub-districts. In 2006, the High ODI is stilllocated at the
same sub-district but with fewchanges. In 2007, there was only one
area with highODI. However, 2008 showed that the same area hada
high ODI and with addition of two areas located intwo different sub
districts. In 2009, there were onlytwo sub-districts that have high
ODI. The sameareas located at same sub-districts were found ashigh
ODI throughout the study period. Similarfindings were found by Wen
et al., (2010) insouthern Taiwan where in 2002 during a
dengueoutbreak, there were 20 high ODI areas which wereconcentrated
in two areas in the city of Kaohsiungand Fengshan. In the high ODI
area, 13 out of 52epidemiological weeks have eonfirmed cases
ofdengue. During this dengue outbreak, the averageduration is
longer than a month with an averageintensity of 3.2 cases per 10
000 people perepidemic wave. Similar findings were reported in
aprevious study of Wen et al., (2006). Galli andNetto (2008) also
used LISA to identify the area atrisk in the city of Sao Jos do Rio
Preto,Brazil. Their research reported that in the year2001/2002,
there were 6 units of ODI area where themost vulnerable area was in
the city. Their findingsshowed that the most vulnerable area for
denguewas in the northern region of the eity. This studyshowed the
advantage of using spatial temporalanalysis which enables us to
identify the specificareas that are vulnerable to dengue outbreaks.
Thisfinding could guide the planning andimplementation of dengue
control and preventionprograms and prioritizing the resources in
riskmanagement. It will enable the health authorities aswell as
other stakeholders such as the municipalagencies, solid waste
management, urbandevelopment agencies to plan and implement
thedengue prevention and control programs morecomprehensively and
effeetively (Wen et al., 2010).The limitation of our study is due
to our dependenceon the surveillance data which is based on
thedengue ineidence. We could not include data on themosquito
density due to the unavailability of thisdata. In understanding the
dynamics of dengueoutbreaks, the inclusion of the mosquito density
datais important because the disease transmissiondepends on the
contacts between human and themosquito which carries the dengue
virus. Data onthe number of breeding sites and mosquito
densitywould be extremely useful in measuring the risk.
3 6 Spatial-Ternporal Analysis for Identification of
V'ulncrabilit) to Dengue in Seremban District, Malaysia
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The application of this analysis could be used in thefuture by
incorporating other variables that maycontribute to the dengue
outbreaks, such asinformation on the human population density,human
movement and habits, availability ofbreeding sites and the
entomological data. Thisanalysis could also be applied to other
localities tohelp identify the high risk area more effectively.
5. ConclusionDengue eases showed a clustering pattern in
thestudy area. The Average Nearest neighborhoodanalysis (ANN)
showed that the average distance ofthose dengue cases was within 55
m (p
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3 8 Spatial-Temporal Analysis for Identification of
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