Investigating Spatio-Temporal Distribution and Diffusion Patterns Of

Home , Gulkada, Swat District

Journal of Infection and Public Health 11 (2018) 550–557

Contents lists available at ScienceDirect

Journal of Infection and Public Health

j ournal homepage: http://www.elsevier.com/locate/jiph

Investigating spatio-temporal distribution and diffusion patterns of

the dengue outbreak in Swat, Pakistan

a b b c,d e

Suleman Atique , Ta-Chien Chan , Chien-Chou Chen , Chien-Yeh Hsu , Somia Iqtidar ,

f a g,∗

Valérie R. Louis , Syed A. Shabbir , Ting-Wu Chuang

Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan

Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taiwan

Master’s Program in Global Health and Development, Taipei Medical University, Taiwan

Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan

Department of Medicine, King Edward Medical University, Lahore, Pakistan

Institute of Public Health, Heidelberg University, Heidelberg, Germany

Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

a r t i c l e i n f o a b s t r a c t

Article history: Introduction: Dengue has been endemic to Pakistan in the last two decades. There was a massive outbreak

Received 5 April 2017

in the Swat valley in 2013. Here we demonstrate the spatio-temporal clustering and diffusion patterns

Received in revised form

of the dengue outbreak.

29 November 2017

Methods: Dengue case data were acquired from the hospital records in the Swat district of Pakistan.

Accepted 6 December 2017

Ring maps visualize the distribution and diffusion of the number of cases and incidence of dengue at

the level of the union council. We applied space-time scan statistics to identify spatio-temporal clusters.

Keywords:

Ordinary least squares and geographically weighted regression models were used to evaluate the impact

Dengue

Swat of elevation, population density, and distance to the river.

Pakistan Results: The results show that dengue distribution is not random, but clustered in space and time in the

Spatio-temporal Swat district. Males constituted 68% of the cases while females accounted for about 32%. A majority of

Clusters the cases (>55%) were younger than 40 years of age. The southern part was a major hotspot affected by

Diffusion the dengue outbreak in 2013. There are two space-time clusters in the spatio-temporal analysis. GWR

and OLS show that population density is a signiﬁcant explanatory variable for the dengue outbreak, while

GWR exhibits better performance in terms of ‘R = 0.49 and AICc = 700’.

Conclusion: Dengue fever is clustered in the southern part of the Swat district. This region is relatively

urban in character, with most of the population of the district residing here. There is a need to strengthen

the surveillance system for reporting dengue cases in order to respond to future outbreaks in a robust

way.

for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://

creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction dengue infection [3,4]. About one third of the world’s population

inhabit a country at risk of dengue fever, with the majority residing

Dengue fever is an arboviral disease caused by the dengue virus in developing countries [5]. Currently dengue is a major threat to

(DENV). DENV belongs to the Flavivirus genus within the Flaviviridae tropical and subtropical countries. However, dengue is expanding

family and has four distinct serotypes, DEN-1, DEN-2, DEN-3, and to other regions such as Europe, which are not located in tropical or

DEN-4, with each serotype found alone in most cases and, rarely, subtropical regions, because of increased international travel [6].

as co-infection [1,2]. Dengue is present in more than 128 coun- Dengue virus is emerging in new areas and regions, which could

tries around the globe, and more than 4 billion people are at risk of be attributable to a suitable habitat for the vector, rapid urbaniza-

tion [7], increased international travel, climate change, and local

and regional climatic phenomena [8]. Dengue virus infection is

usually asymptomatic but can develop into cases presenting symp-

∗

Corresponding author at: Wuxing Street 250, Xinyi District, Taipei 11031,

toms ranging from a mild fever to potentially fatal dengue shock

Taiwan.

syndrome [9]. The transmission of the dengue virus can occur

E-mail address: [email protected] (T.-W. Chuang).

https://doi.org/10.1016/j.jiph.2017.12.003

CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

S. Atique et al. / Journal of Infection and Public Health 11 (2018) 550–557 551

at the same time as other Flavivirus viruses, such as the zika or spatial regression model to reveal spatio-temporal clusters and the

chikungunya virus, leading to an ambiguous diagnosis or combined related risk factors at the union council level in the Swat district.

infections [10,11].

Aedes aegypti and Aedes albopictus are the two important vec- Materials and methods

tors involved in dengue transmission. These vectors are widely

present in tropical and subtropical countries located in Africa, Asia, Study area

Australia, the Americas, and the Middle East [12]. Ae. aegypti is

the primary vector of the dengue, yellow fever, and chikungunya The Swat district is located in Khyber Pakhtunkhwa near the

viruses, and is broadly found in the subtropics and tropics [13]. Pakistan–Afghanistan border (Fig. S1). The Swat valley is situated

◦

The Indian sub-continent is at risk of vector-borne diseases like in the north of Khyber Pakhtunkhwa, at 35 north latitude and

◦

malaria and dengue. Although incidences of malaria have dropped 72 east longitude, and is enclosed by mountains. The Swat dis-

since 1990, dengue has appeared and is continuously increasing trict varies in average elevation from 770 m in the Barikot union

in the region [14]. Ae. aegypti is an urban mosquito and likes to council to around 4000 m in the Kalam union council. The high alti-

dwell and breed in artiﬁcially created habitats rather than natural tude of the Swat valley means that it enjoys very pleasant weather,

environments [15,16]. which attracts large numbers of tourists from across the country.

There are many drivers which impact the spread of dengue Swat is situated in the temperate zone. June is the hottest month,

◦

by affecting the behavior and life-cycle of vectors. Temperature and has mean maximum and minimum temperatures of 33 C and

◦

changes and rainfall patterns [17] in the locality, along with 16 C respectively. January is the coldest month, with average tem-

◦ ◦

regional climate phenomena like the El Nino˜ Southern Oscillation peratures ranging from 11 C to −2 C. Around 1.3 million people

(ENSO) and the Indian Ocean Dipole (IOD), have been found to have live in the Swat valley area, which includes the valleys of Chitral

an inﬂuence in this regard [18–20]. It is postulated that the ENSO and Gilgit-Baltistan in the north, the Dir valley in the west, and the

phenomenon has an impact on the redistribution, intensiﬁcation, Mardan valley in the south [40]. The Indus River separates it from

and generation of several vector-borne diseases. the Hazara division in the east. The Swat district is divided into 65

Higher temperatures can reduce larval development time and administrative units, known as union councils, as shown in Fig. S1.

the extrinsic incubation period (EIP) of a virus [21,22]. Precipita-

tion plays an arbitrary role on vector ecology: rainfall is required Data source

to create mosquito habitats but extreme rainfall has the effect of

killing the larvae and adult mosquito [23]. Generally speaking, an The conﬁrmed dengue case data for the study area were col-

increase in rainfall and temperature, as well as humidity, could lead lected from the district health ofﬁce in the Swat district. Positive

to an increased risk of dengue transmission [24–26]. In addition to cases were conﬁrmed following detection of the nonstructural pro-

climate parameters, population density and lower socioeconomic tein 1 or anti-dengue IgM by an enzyme-linked immunosorbent

indicators are also positively linked to dengue incidence [27,28]. assay (ELISA). Age, gender, and location of the cases were recorded

Higher population density is associated with an increase in dengue in the data at the time of patient admission to a health facility.

incidence, as observed in several countries [29,30]. Population ﬁgures have been extracted from the district health

Pakistan has experienced several dengue outbreaks since it was proﬁle of Swat published by PAIMAN (Pakistan Initiative for Moth-

ﬁrst reported in 1994 [31]. Since then it has experienced sporadic ers and Newborns) and sponsored by the United States Agency for

cases of dengue in other parts of the country. The four serotypes of International Development (USAID).

DENV have been identiﬁed in outbreaks in Pakistan [32]. In an out-

break in Karachi, Pakistan’s largest metropolitan city and economic Spatio-temporal cluster analysis

hub, DEN-2 and DEN-3 were identiﬁed as the prevalent serotypes

[33]. However, in the 2011 outbreak in Lahore city, DEN-2 was the The number of dengue cases and incidence (both monthly and

dominant serotype, and DEN-3 and DEN-4 contributed to fewer weekly) were calculated, and ring maps were plotted to visualize

infections [34]. The ﬁrst outbreak in Lahore city occurred in 2006, the diffusion patterns of dengue cases in space and time following

and later spread to the other cities of the Punjab province over a the method developed by Chan et al. [39]. The space-time anal-

period of 5 years. This led to the largest outbreak of 2011, in which ysis was carried out by SaTScan version 9.4.2 to detect spatial

more than 20,000 cases were reported along with more than 360 and temporal clusters of dengue transmission [41]. A space-time

deaths across the province [35,36]. permutation model was used to detect clusters across space/time

Recently, geographical information system (GIS) and other by comparing the disease risk within and outside the scanning

related tools have been used in vector-borne disease surveil- windows. Clusters with the highest log likelihood ratio (LLR) are

lance and control. Several studies have reported the geographical considered the most likely clusters. The maximum window size was

distribution of dengue outbreaks in space and time along with set as 50% of the population at risk [41]. The p-value was obtained

prospective hotspot predictions [37,38]. Spatial ring maps are a through Monet Carlo simulation to perform hypothesis testing, and

novel way of displaying the spatio-temporal diffusion patterns of the cut-off value is set as 0.05.

disease outbreaks and crimes [39]. These are important tools in

spatial epidemiology. GIS and related tools help to identify the spa- Ordinary least squares (OLS) and geographically weighted

tial targets of intervention to effectively combat the spread of the regression (GWR)

disease.

The Swat valley in Pakistan experienced an unprecedented out- Both ordinary least squares (OLS) and geographically weighted

break in 2013 [40]. No dengue outbreak had been recorded in regression (GWR) models were applied to assess the impact of ele-

this region before 2013, probably due to surveillance data not vation, distance to water bodies, and population density on the

being available. Here we visualized the geographical distribution occurrence of dengue fever. The detailed information and spa-

of dengue in the Swat district at the union council level, which tial distribution of dependent and independent variables used for

is the smallest administrative unit in Pakistan. To the best of our OLS and GWR are shown in the supplementary material (Table S1,

knowledge no other research has investigated the spatio-temporal Fig. S3). OLS was applied to evaluate the global relations between

distribution of the 2013 dengue outbreak in the Swat district at the dependent and independent variables. GWR helps to identify the

level of the union council. We applied spatial scan statistics and a spatial inﬂuence on the neighborhoods which are not explained by

552 S. Atique et al. / Journal of Infection and Public Health 11 (2018) 550–557

Table 1 Table 2

Demographic characteristics of dengue cases in Swat, 2013. Spatio-temporal clusters of dengue fever in the Swat district.

Variables Number of case Percentage (%) Cluster type Radius Time frame Observed Expected RR p-value

Gender Primary cluster 11.26 km 8/22–10/30 5267 299.26 17.6 <0.001

Male 6157 68.2 Secondary cluster 29.88 km 9/7–9/22 337 135.73 2.48 <0.001

Female 2875 31.8

Age

<10 358 3.9 Table 3

11–20 1767 19.6 Summary of the ordinary least squares (OLS) regression model.

21–30 1969 21.8

Parameters Estimates Standard error p-value VIF

31–40 1241 13.7

41–50 678 7.5

Intercept 15.65

51–60 369 4.1 *

Population density 0.03 0.07 0.0001 1.32

61–70 153 1.7

Elevation −0.009 0.03 0.75 8.21

>70 70 0.8

Distance to river 0.12 1.12 0.92 8.79

Missing 2426 26.9 R-squared 0.37

AIC 718.85

Total 9032 –

VIF = variance inﬂation factor.

p-Value < 0.05.

OLS [42]. We used cross validation (CV) as the bandwidth method

and opted for the adaptive kernel type in our analysis settings for Table 4

GWR. The adaptive kernel was chosen because the distribution of Summary of the geographical weighted regression (GWR) model.

union councils was heterogeneous in the study area.

Parameter Minimum 25% quartile 50% quartile 75% quartile Maximum

The coefﬁcient of determination (R ) and corrected Akaike’s

−

Intercept 51.27 11.82 74.91 138.01 201.12

Information Criterion (AICc) were used to compare the model per-

Population 0.01 0.02 0.03 0.036 0.05

formance of OLS and GWR. Variance inﬂation factor (VIF) was density

used to evaluate the extent of multicollinearity among the inde- Elevation −0.15 −0.1 −0.06 −0.016 0.03

Distance to −2.09 −0.35 1.39 3.13 4.87

pendent variables. A VIF value greater than 10 indicates strong

river

multicollinearity among the variables. GWR models can be elab-

Condition 14.68 19.21 23.74 28.27 32.79

orated by the following equation: numbera

k R-squared 0.49

AIC 700.52

i=ˇ (ui, vi) + ˇi(ui, vi)xij + εi

0 a

condition number: if the value is larger than 30, it indicates multicollinearity.

j=1

where yi signiﬁes the cumulative incidence rate of dengue fever in transmission season (Table 2). The secondary cluster covered a

2013 at union council i; ui and vi are centroid coordinates for union larger area (radius = 29.88 km), which includes 32 union councils

council i; xij represent a set of k-independent variables (where j = 1, with shorter temporal clusters (9/7–9/22).

. . ˇ ˇ

, k); and 0 and i are coefﬁcient estimates for the correspond- Three risk factors were analyzed using OLS and GWR

ing location. The map layout was performed by ArcGIS 10.3 (ESRI, (Tables 3 and 4). The global regression (OLS) model demon-

Redland, CA). strates that population density and distance to river are positively

associated with dengue incidence in the Swat district; however,

Results only population density reached statistical signiﬁcance (Table 3).

Dengue incidence decreased with an increase in elevation in the

A total of 9032 conﬁrmed cases of dengue fever were reported in study area.

the Swat district in 2013 (Table 1). The outbreak started in August The GWR model showed the detailed spatial distribution of the

2013, reached its peak in September, and subsided in December associations between dengue incidence and risk factors in the Swat

(Fig. S2). The infection was dominant among males (68%) while district (Fig. 3). The correlations showed high variability in terms

females accounted for about 32%. The majority of the cases (>55%) of space. Overall, population density showed a strong correlation

were younger than 40 years of age. with dengue incidence in the south and southwestern areas, with

The spatial and temporal diffusion visualization indicates that distance to river only positively associated with dengue incidence

dengue cases started to emerge in mid-August and subsided by in the south (Fig. 3B and C). Elevation is negatively associated with

mid-November (Fig. 1). The ﬁrst dengue cases began to appear in dengue fever near the hotspot region but the effect was inverse

week 34 in the union councils located in the southern part of the in the north (Fig. 3D). The local R-squared indicates that the GRW

Swat district and spread to the surrounding regions between weeks model performed better in the southwestern region of the Swat

36 and 47. As indicated by the ring map, two union councils did district (Fig. 3A).

not report any dengue cases, even though they are located in the The GWR model exhibited better model performance than the

central part of the district, which could be because of missing data OLS model in the study (AICc: 700.52 V.S.718.85). The higher R

or a reporting error. value (0.49) also supports the fact that GWR can better explain

Fig. 2(A) demonstrates the spatial distribution of dengue inci- the associations between dengue incidence and risk factors after

dence in each union council of the Swat district. The dengue hotspot accounting for spatial autocorrelation. No spatial autocorrelation

is mainly clustered in the southern part of Swat, which is more can be detected among the standardized residuals of the GWR

−

urban and is home to a major chunk of the Swat population. There model (Moran’s I test: z-score = 0.54, P = 0.58).

are 16 union councils included in the primary cluster, Rahim Abad,

Shahdara Nawan Kalay, Banringaro Darai, Amankot Faiz Abad, Discussion

Malakanan Landakass, Malok Abad, Rang Muhallah, Gulkada, Saidu

Sharif, and Qamber (Fig. 2(B)). The temporal distribution of the ﬁrst This study attempts to investigate the spatio-temporal distri-

cluster, which is from 8/22 to 10/30, almost spanned the whole bution of the 2013 dengue outbreak in the Swat district. This is

S. Atique et al. / Journal of Infection and Public Health 11 (2018) 550–557 553

Fig. 1. Space-time diffusion patterns of dengue fever in Swat.

the ﬁrst study to analyze dengue transmission at the union council ing compared with other studies, which indicate no signiﬁcant

level, which is the smallest administrative unit in Pakistan. There gender difference among dengue cases [3,47,48]. However, a pre-

is a dearth of literature on the 2013 dengue outbreak in terms vious study in the Lahore district, the dengue hotspot of the Punjab

of spatio-temporal and diffusion patterns. The majority of other province of Pakistan, supports our observation [8]. This ﬁnding is

investigations focused on its demographical, clinical, serological, also in line with the study on dengue in Jeddah, Saudi Arabia [38].

and epidemiological features [43–46]. This could be attributable to cultural practices, as females mostly

It is worth noting in terms gender distribution that dengue wear full sleeves and long attire in Pakistan. The majority of females

incidence is much higher among males. This is a unique ﬁnd- have relatively little chance to be exposed to the open environment

554 S. Atique et al. / Journal of Infection and Public Health 11 (2018) 550–557

Fig. 2. Dengue incidence rate (A) and spatio-temporal clusters (B) in Swat.

The green circle indicates the union councils in the primary cluster while the red color indicates the secondary cluster.

S. Atique et al. / Journal of Infection and Public Health 11 (2018) 550–557 555

Fig. 3. GWR results of R-squared (A), coefﬁcient of distance to river (B), population density (C), and elevation (D).

because they contribute less to the workforce in urban areas. By Our study identiﬁed two signiﬁcant spatio-temporal clusters

contrast, males, being the breadwinners, have more chance to be of dengue incidence in Swat, and the hotspots could be associ-

exposed at dawn and dusk while going to and from work. Dawn ated with other environmental risk factors. Population density has

and dusk are the prime biting times of Aedes mosquitoes. been shown to be an important factor for dengue transmission

A previous study indicated that the introduction of dengue in because the vector prefers to inhabit surrounding urban regions

2013 and its subsequent spread across the Swat district could be [50]. It should be noted that the union councils in the northern

attributed to an outbreak in the southern part of the country, i.e. in part of the Swat district have an average elevation of 2000–4000 m,

the Punjab region [49]. The study looked at the impact of human which is not suitable for the mosquito vector to survive [51,52].

mobility on the transmission of dengue across Pakistan, and pro- Whether the presence of fewer dengue cases in areas of higher

posed that the dengue outbreak in Lahore could have triggered the elevation is attributable to the extended habitat suitability of the

dengue outbreak in Swat. On the other hand, the disease vectors, mosquito vector caused by climate change require further inves-

Ae. albopictus and Ae. aegypti, are also available in the Swat district tigation. The spatio-temporal spread of dengue infection in three

[43]. When the environmental conditions are suitable for mosquito different cities of Pakistan has been discussed by Fareed et al.

propagation, the introduced virus could maintain its transmission [53]. The study indicated that, along with environmental factors,

in the Swat district and cause the subsequent outbreak. It should be land cover and land use (LCLU) changes in Rawalpindi, Islam-

noted that the Swat area, by virtue of its high elevation and cooler, abad, and Swat also contributed to the spread of dengue. They

pleasant weather in the summer season, attracts many tourists are of the view that urbanization, housing density, LCLU, housing

from other regions, including the Punjab area, during the sum- type, indoor–outdoor, and breeding sites form a complex structure

mertime. Thus, travel activity could play an important role in the which promotes dengue fever occurrence and subsequent trans-

introduction of the dengue virus if the outbreak has occurred in mission. The next step is to explore the impact of landscape-level

other areas. factors, along with climate conditions, on dengue transmission and

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