Impact of Land-use Change on Dengue Hemorrhagic Fever in Kolaka
District, Southeast Sulawesi Province, Indonesia
RAMADHAN TOSEPU
Department of Environmental Health, Faculty of Public Health,
University of Halu Oleo, INDONESIA
FITRA SALEH
Department of Geography, Faculty of Earth Science and Technology,
University of Halu Oleo, INDONESIA
NASARUDDIN
Department of Biology, Faculty of Mathematics and Natural Sciences,
University of Halu Oleo, INDONESIA
MISRAN SAFAR
Department of Social Science Education, Faculty of Teacher and Education,
University of Halu Oleo, INDONESIA
DENIYATNO
Department of Mining Engineering, Faculty of Earth Science and Technology,
University of Halu Oleo, INDONESIA
Abstract: - Land use change is contributing to the emergence of zoonotic diseases in the community.
And can cause an increase in the spread of the virus through arthropods. This study aimed to
determine the association of land use factors and dengue hemorrhagic fever in Kolaka District,
Southeast Sulawesi Province, Indonesia. The secondary data obtained from various governments of
Indonesia were used for this study. Data of dengue hemorrhagic fever from Ministry of Health of
Republic Indonesia. Land use data is derived from the classification of Citra Landsat 8 on a scale of 1:
250,000 from 2010 to 2020. The Spearman rank correlation test was used to examine the relationship
between land-use change and the incidence rate of dengue hemorrhagic fever. The results of this study
In Period 2010-2015 is a correlation between Agriculture with dengue hemorrhagic fever ( = 0.812,
p <0.05), and water bodies with =0.812. The area of agricultural land is increasing every year; in
2010, only 3.32% increase to 51.08% in 2015. Furthermore, in period 2016-2020 is a correlation
between Forest with dengue hemorrhagic fever ( = 0.900, p <0.05), and Settlement ( = -0.900, p
<0.05). Our findings could be used to improve the understanding of land-use change and dengue
hemorrhagic fever in the Kolaka district and provide information on land use that does not damage the
environment.
Key-Words: - Land use; Dengue hemorrhagic fever; Forest; Agriculture; Water bodies; Indonesia
Received: May 26, 2021. Revised: November 14, 2021. Accepted: December 13, 2021. Published: January 9, 2022.
1 Introduction
Dengue hemorrhagic fever (DHF) is an infection
caused by the dengue virus [1]. Dengue fever is a
highly contagious disease [2]. The dengue virus is
transmitted through the Aedes aegypti and Aedes
albopictus mosquitoes [3, 4]. Dengue fever and
dengue hemorrhagic fever/dengue shock syndrome
are caused by four viral serotypes. In Indonesia,
dengue hemorrhagic fever is still an important
public health problem [5]. In Indonesia, dengue
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
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Ramadhan Tosepu, Fitra Saleh,
Nasaruddin, Misran Safar, Deniyatno
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infection has been endemic for the last two centuries
[6]. The World Health Organization estimates that
50-100 million dengue infections occur each year.
This situation is increasingly worrying because
almost half of the world's population lives in
dengue-endemic countries. Globally this virus is
spreading very fast, with a 30-fold increase over the
last 50 years [7]
In Indonesia, the number of dengue cases reported
in 2019 was 138,127 cases. This number increased
compared to 2018 of 65,602 cases. Deaths due to
DHF in 2019 also increased compared to 2018 from
467 to 919 deaths. Illness and death can be
described using the incidence rate (IR) indicator per
100,000 population and the case fatality rate (CFR)
as a percentage [8]. In Southeast Sulawesi, there
were 3,433 cases reported in 2016, with the
Incidence Rate reaching 132.5 per 100,000
population. In 2017 the number of reported cases
decreased significantly to 941 cases, with the Pain
Rate returning to an average condition ranging from
35.7/100,000 population [9]. In Kolaka, in 2018,
there were 213 cases of dengue fever with an
Incidence Rate of 82.7 per 100,000 population. Of
these cases, 2 cases died [9]. Furthermore, in 2019,
the number of dengue cases was 250 cases with an
Incidence Rate of 95.5 per 100,000 population. Of
these cases, 1 case died [8].
Many studies have been carried out by combining
the land change factor with the density of Aedes
mosquitoes [10]. Land use such as water bodies in
certain agricultural practices was identified as a risk
factor for dengue fever [11]. Excessive land use is
one of the problems for the emergence of infectious
diseases. Natural forest is a complex ecosystem, and
as a habit of various flora, if the ecosystem is
disturbed, it will impact human health [12]. Land-
use change affects mosquito habitat, distribution,
vector abundance [13]. The occurrence of land
change using the Normalized Difference Vegetation
Index (NDVI) indicator is a risk factor for
environmental change in urban areas [14]. The
objective of our study was to determine the
association of land use factors and dengue
hemorrhagic fever in Kolaka District, Southeast
Sulawesi Province, Indonesia.
2 Material and Methods
2.1 Locations of Study
This research was conducted in Kolaka district,
Southeast Sulawesi province, Indonesia.
Astronomically, Kolaka district is located along the
equator section of the equator, extending from North
to South between 3°36'- 4°35' South Latitude (SL)
and extending from West to East between 120 ° 45'-
121 ° 52 ' East Longitude (EL). Based on its
geographical position, the territorial boundaries of
Kolaka district in the north is Kolaka Utara district,
in the south is Bombana district, in the east is
Kolaka Timur district and in the west is Sulawesi
Selatan Province in Teluk Bone [15].
Fig.1: Map of Kolaka district.
Source: www.googleearth.com
2.2 Data Collection
The secondary data obtained from various
governments of Indonesia were used for this study.
Data of dengue hemorrhagic fever from Ministry of
Health of Republic Indonesia. Land use data is
derived from the classification of Citra Landsat 8 on
a scale of 1: 250,000 from 2010 to 2020. By taking
the map using satellite images downloaded
https://glovis.usgs.gov. Land use data acquired and
interpreted through Landsat imagery using GIS
software [16]. Land use is done by the Multispectral
interpretation method with a guided classification
that is a maximum likelihood algorithm.
Interpretation of remote sensing data using spectral
channels in the image which is then classified based
on the characteristics of the object or land cover.
The result is land use data in the form of raster data,
with map output. Thematic maps are created and
edited, overlay, and visualized in the QGIS [17].
There are four classifications of land use in the
district of Kolaka (National Standard of
Indonesian). The following table is land-use
classification [18]: Settlement/residential: Acreage
or land used as a residential area or residential
environment and the activity place that supports
people's lives; Agriculture: Agricultural area is
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flooded by water, either with the technology of
irrigation, rained, tides. The agricultural area is
characterized by bund pattern and is planted with
crops short-lived; Forest: Forest that grow on dry
land habitats that can be either lowland forests, hills,
and mountains, or the tropical highland forests that
have undergone human intervention or had appeared
logged-over (appearance groove and patches of
logged-over); Water bodies: The area is natural or
unnatural; consist of Swamps, rivers, lakes, and
dams.
2.3 Data Analysis
Land use data obtained from the Kolaka district is
categorized by sub-district. The proportional land
use data type was used to analyze the association
with the DHF incidence rate. The climate data was
overlaid by years, from 2010 to 2020. The criteria of
IR DHF used by an indicator of the Ministry of
Health of the Republic of Indonesia [8]. The was
visualization by sub-district and by month in 2010
and 2020. The proportion of data in each type was
calculated from the area (Ha). As data were not
normally distributed, the Spearman rank correlation
test was used to examine the relationship between
land-use change and the incidence rate of dengue
hemorrhagic fever.
3 Results
Table 1. Distribution of Population, DHF cases, and
Incidence Rate of DHF in Kolaka district, 2010-
2020
Years
Population
DHF
Cases
2010
208766
274
2011
213064
125
2012
232470
84
2013
215871
198
2014
203985
427
2015
233437
751
2016
238679
685
2017
243022
243
2018
248798
202
2019
252843
221
2020
258454
58
Table 1 shows that the high incidence rate of dengue
hemorrhagic fever is in 2015 (321.71 per 10000
population), and the low incidence rate is in 2020
(22.44 per 10000 population).
Table 2. Distribution of Land use change in Kolaka
district, 2010-2020
years
Land-use change (Ha)
Agriculture
Forest
Settlemen
t
Water
bodies
2010
9,561.37
266,680.51
7,658.90
3,741.05
2011
9,526.58
253,282.09
8,765.49
3,717.38
2012
9,561.37
266,680.51
7,658.90
3,741.05
2013
110,809.89
147,598.41
24,054.66
5,153.50
2014
141,610.94
116,408.84
24,367.36
5,229.33
2015
146,906.40
105,711.67
25,915.32
9,083.11
2016
110,975.6
166,226.1
4,568.02
5,931.51
2017
111,734.3
164,005.8
5,469.97
6,491.12
2018
112,396.5
163,429.4
5,472.618
6,402.6
2019
112,528.3
163,292.1
5,632.629
6,248.169
2020
112,471.1
163,223.9
5,952.709
6,053.541
Land use factors in Kolaka District are classified
into four variables: agriculture, forest, settlement,
and water bodies. The land use information was
analyzed using the multispectral interpretation
method with supervised classification of maximum
likelihood algorithm. The use of this algorithm is for
a homogenous object. On the contrary, in the
Kolaka district, agriculture areas were extended
every year while the settlement areas also increased
similar to the agriculture type (Table 2)
Table 3. Correlation between land-use change and
an incidence rate of DHF
Period
(years)
Agricu
lture
Forest
Settle
ment
Water
bodies
2010-
2015
0.812*
-
0.754
0.754
0.812*
2016-
2020
-0.700
0.900
**
-
0.900
**
-0.100
Correlation is significant at the 0.01 level (2-
tailed).**
Correlation is significant at the 0.05 level (2-
tailed).*
In period 2010-2015 is a correlation between
Agriculture with dengue hemorrhagic fever ( =
0.812, p <0.05), and water bodies with =0.812.
The area of agricultural land is increasing every
year; in 2010, only 3.32% increase to 51.08% in
2015. Furthermore, in period 2016-2020 is a
correlation between Forest with dengue
hemorrhagic fever ( = 0.900, p <0.05), and
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Settlement ( = -0.900, p <0.05). This is inversely
proportional to the forest that has decreased the size
of the area. Potential Land change that occurred in
the Kolaka district is Cocoa plantation development
(Table 3)
Fig.2: Map of Land use change in Kolaka district,
during 2010-2020
Figure 2 shows that land use in Kolaka district has
changed every year. Agricultural areas were
increased from 9,561.37 Ha in 2010 to 112,471.1 Ha
in 2020. In contrast, forest area is narrowed region,
in 2010 extensive forest area is decreased from
266,680.51 Ha to 163,223.9 Ha in 2020.
4 Discussion
Land-use changes include deforestation, road
construction, agribusiness agro-development, dam
construction, irrigation, coastal zone degradation,
wetland modification, mining, urban environmental
expansion, and other activities [19]. The last few
decades in the tropics have experienced dramatic
events in the field of land use. Food Agricultural
Organization (FAO), in the program of Forest
Resources Assessment (FRS), estimates that land-
use change causes the loss of tropical natural forests
in the world [20]. Land-use change may occur due
to demographic, socio-economic, and biophysical
factors. Demographic factors include population
size and population growth, population structure,
and the number of household heads. Socio-
economic factors include education levels, family
income levels, and types of livelihoods. While the
biophysical factors such as soil type and slope [21-
23]. This situation causes a change in the function of
the land area from a planned function to another
function that is not in accordance with its
designation. David et al. further mentioned that
land-use change is influenced by complex
interaction results between human factors and
environmental factors [24]. Therefore, the
occurrence of changes in the use of agricultural land
into non-agricultural cannot be avoided because it is
closely related to the dynamics of the population
and the dynamics of development [25].
Climate change and land-use change can cause
disease in humans, either directly or indirectly [12],
and have serious impacts such as direct health
problems such as death, injury, casualties, diseases
with ecological intermediaries, and plant disease
epidemics [26, 27]. The emergence of various
diseases is strongly influenced by environmental
factors such as climate change or land use. Most
diseases are caused by vector arthropods, such as
mosquitoes, flies, lice. Because of cold-blooded
insects, changes in marginal temperature potentially
have biological effects on disease transmission.
Thus, climate change can change the incidence,
transmission, and geographic range of diseases such
as malaria, dengue and yellow fever, leishmaniasis,
lyme disease, and onchocerciasis [26, 28].
Forests are areas that have associated primarily
trees and other vegetation types that are capable of
producing wood and other forest products [28, 29].
These include forests, private forests, protected
forests, which only have forests identified by
boundaries. Forests make the most significant
contribution to water supply in maintaining high
water quality through soil stabilization and erosion
prevention. By holding sediments and pollutants
from other land uses and slopes, forests can protect
water bodies and waterways. Research conducted on
Oahu Island found that areas with different
vegetation and variety enabled the breeding of Ae.
Albopictus [30]. Furthermore, wetlands and forest
areas opened in lowland areas are good breeding
grounds for mosquitoes [31].
Agricultural development can lead to
environmental and ecological changes, creating a
new vector of diseases and intermediate hosts
enabling microclimate conditions that support
longer vector life; simple habitat in the area due to
the loss of vital predator species that keep the vector
population under control; or it may cause an
increase in human vector contact frequency [13]
Residential density in settlements favored mosquito
breeding. Research conducted in Malaysia found
that land use on construction sites and industrial
estates played a major role in the incidence of
dengue fever [32]. A previous study reported that
dengue data had a significant association with slum
housing [33]. Research conducted by Scott found
that human settlements and non-agricultural areas
determined the occurrence of dengue fever cases.
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The increased population allows Aedes mosquitoes
to intimidate an infected person and transfer the
dengue virus after infection to others [34]. Water
bodies are areas where vectors can breed; in this
study, water bodies consist of man-made/lake
reservoirs, canals in addition to natural lakes, rivers,
and streams
5 Conclusions
Changes in land use have an effect on increasing the
incidence rate of dengue hemorrhagic fever. In
period 2010-2015 is a correlation between
Agriculture with dengue hemorrhagic fever ( =
0.812, p <0.05), and water bodies with =0.812.
The area of agricultural land is increasing every
year; in 2010, only 3.32% increase to 51.08% in
2015. Furthermore, in period 2016-2020 is a
correlation between Forest with dengue
hemorrhagic fever ( = 0.900, p <0.05), and
Settlement ( = -0.900, p <0.05). Influencing factors
include agriculture and water bodies in the 2010-
2015 period. Next in the 2016-2020 period is forest
and settlement. The land change occurred due to the
conversion of land use from forest to agricultural
land. This land conversion disrupts the habitat of the
Aedes aegypti mosquito, so it moves to community
settlements. Epidemiological data of dengue
hemorrhagic fever through surveillance is essential
in efforts to prevent the disease. As well as
longitudinal land use data provides a warning for the
government to realize environmentally sound
development. This is because the conversion over
this land does not submit the government, which is
influenced by various factors, among others:
economic pressure; The increase in population so
that they open the land extensively, applicable land
regulations have not been able to control the rate of
land conversion.
References:
[1] A. Banerjee, A. Tripathi, S. Duggal, A.
Banerjee, and S. Vrati, "Dengue virus infection
impedes megakaryopoiesis in MEG-01 cells
where the virus envelope protein interacts with
the transcription factor TAL-1," Scientific
Reports, vol. 10, no. 1, pp. 1-12, 2020.
[2] N. Khetarpal and I. Khanna, "Dengue fever:
causes, complications, and vaccine strategies,"
Journal of immunology research, vol. 2016,
2016.
[3] M. Mistawati, Y. Yasnani, and H. Lestari,
"Forecasting prevalence of dengue hemorrhagic
fever using ARIMA model in Sulawesi
Tenggara Province, Indonesia," Public Health
of Indonesia, vol. 7, no. 2, pp. 75-86, 2021.
[4] M. E. Parra-Amaya, M. E. Puerta-Yepes, D. P.
Lizarralde-Bejarano, and S. Arboleda-Sánchez,
"Early detection for dengue using local
indicator of spatial association (LISA)
analysis," Diseases, vol. 4, no. 2, p. 16, 2016.
[5] N. I. Ishak and K. Kasman, "The effect of
climate factors for dengue hemorrhagic fever in
Banjarmasin City, South Kalimantan Province,
Indonesia, 2012-2016," Public Health of
Indonesia, vol. 4, no. 3, pp. 121-128, 2018.
[6] S. Siswanto, R. Risva, and M. Nana,
"Epidemiology forecasting analysis of dengue
haemorraghic fever with seasonal
autoregressive integrated moving average in
tropical area," 2019.
[7] World Health Organization, "Global strategy
for dengue prevention and control 2012-2020,"
2012.
[8] Ministry of Health Republic of Indonesia,
"Profil Kesehatan Indonesia Tahun 2019,"
2019.
[9] Dinas Kesehatan Sulawesi Tenggara, "Profil
Kesehatan Provinsi Sulawesi Tenggara Tahun
2017 " 2017.
[10] A. Rohani, A. Abdullah, Y. Ong, I. Saadiyah, I.
Zamree, and H. Lee, "Survey of mosquito
larvae distribution in container habitats
collected from urban and rural areas in major,"
Tropical Biomedicine, vol. 18, no. 1, pp. 41-49,
2001.
[11] Y. L. Cheong, P. J. Leitão, and T. Lakes,
"Assessment of land use factors associated with
dengue cases in Malaysia using Boosted
Regression Trees," Spatial and spatio-temporal
epidemiology, vol. 10, pp. 75-84, 2014.
[12] J. A. Patz, S. H. Olson, C. K. Uejio, and H. K.
Gibbs, "Disease emergence from global climate
and land use change," Medical Clinics of North
America, vol. 92, no. 6, pp. 1473-1491, 2008.
[13] S. O. Vanwambeke et al., "Impact of land-use
change on dengue and malaria in northern
Thailand," EcoHealth, vol. 4, no. 1, pp. 37-51,
2007.
[14] S. Y. I. Sari, Y. Adelwin, and F. R. Rinawan,
"Land use changes and cluster identification of
dengue hemorrhagic fever cases in Bandung,
Indonesia," Tropical medicine and infectious
disease, vol. 5, no. 2, p. 70, 2020.
[15] Badan Pusat Statistik Kabupaten Kolaka,
"Kolaka Dalam Angkat 2020," 2021.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.12
Ramadhan Tosepu, Fitra Saleh,
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E-ISSN: 2224-3496
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Volume 18, 2022
[16] E. Budiyanto, Sistem Informasi Geografis
Menggunakan ArcView GIS. Penerbit Andi,
2002.
[17] S. Rasmussen, M. Talla, and R. Valverde,
"Case study on geocoding based scheduling
optimization in supply chain operations
management," WSEAS Transactions on
Computer Research, vol. 7, pp. 29-35, 2019.
[18] Badan Standarisasi Nasional Republik
Indonesia, "Standar of Cover Clasification,
National Standarization Agency of Indonesia.,"
2010.
[19] J. A. Patz et al., "Unhealthy landscapes: policy
recommendations on land use change and
infectious disease emergence," Environmental
health perspectives, vol. 112, no. 10, pp. 1092-
1098, 2004.
[20] R. Drigo, "Trends and patterns of tropical land
use change," Forests, water and people in the
humid tropics, edited by: Bonell, M. and
Bruijnzeel, LA, Cambridge University Press,
Cambridge, UK, pp. 9-39, 2005.
[21] L. V. Rasmussen and A. Reenberg, "Land use
rationales in desert fringe agriculture," Applied
Geography, vol. 34, pp. 595-605, 2012.
[22] S. J. Walsh, J. P. Messina, C. F. Mena, G. P.
Malanson, and P. H. Page, "Complexity theory,
spatial simulation models, and land use
dynamics in the Northern Ecuadorian
Amazon," Geoforum, vol. 39, no. 2, pp. 867-
878, 2008.
[23] C. F. Mena, S. J. Walsh, B. G. Frizzelle, Y.
Xiaozheng, and G. P. Malanson, "Land use
change on household farms in the Ecuadorian
Amazon: Design and implementation of an
agent-based model," Applied Geography, vol.
31, no. 1, pp. 210-222, 2011.
[24] D. M. Lapola et al., "Indirect land-use changes
can overcome carbon savings from biofuels in
Brazil," Proceedings of the national Academy
of Sciences, vol. 107, no. 8, pp. 3388-3393,
2010.
[25] D. Herdhiansyah, L. Sutiarso, D. Purwadi, and
T. TIP, "POTENTIAL AREAS ANALYSIS
FOR DEVELOPMENT OF PRIME
COMMODITIES PLANTATION IN THE
KOLAKA DISTRICT, SOUTHEAST
SULAWESI," Journal of Agroindustrial
Technology, vol. 22, no. 2, 2012.
[26] J. A. Foley et al., "Global consequences of land
use," science, vol. 309, no. 5734, pp. 570-574,
2005.
[27] R. Amelia, N. Anggriani, and A. Supriatna,
"Optimal control model of Verticillium lecanii
application in the spread of yellow red chili
virus," WSEAS Transactions on Mathematics,
vol. 18, pp. 351-358, 2019.
[28] A. J. McMichael, R. E. Woodruff, and S.
Hales, "Climate change and human health:
present and future risks," The Lancet, vol. 367,
no. 9513, pp. 859-869, 2006.
[29] C. J. P. Colfer, D. Sheil, and M. Kishi, Forests
and human health: assessing the evidence.
Cifor, 2006.
[30] C. J. P. Colfer, Human health and forests: A
global overview of issues, practice and policy.
Routledge, 2012.
[31] S. O. Vanwambeke, S. N. Bennett, and D. D.
Kapan, "Spatially disaggregated disease
transmission risk: land cover, land use and risk
of dengue transmission on the island of Oahu,"
Tropical Medicine & International Health, vol.
16, no. 2, pp. 174-185, 2011.
[32] S. Pathirana, M. Kawabata, and S. M. Baban,
"Impact of climate and land cover/use
variability on vector borne diseases: an analysis
of epidemic outbreaks of malaria and dengue
incidence," in 28th Asian conference on remote
sensing (ACRS 2007), 2007, pp. 12-16.
[33] W. J. McBride and H. Bielefeldt-Ohmann,
"Dengue viral infections; pathogenesisand
epidemiology," Microbes and infection, vol. 2,
no. 9, pp. 1041-1050, 2000.
[34] C. Nazri, A. A. Hassan, Z. Abd Latif, and R.
Ismail, "Impact of climate and landuse
variability based on dengue epidemic outbreak
in Subang Jaya," in 2011 IEEE Colloquium on
Humanities, Science and Engineering, 2011:
IEEE, pp. 907-912.
Conflict of Interest:
The authors declare no conflict of interest.
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.12
Ramadhan Tosepu, Fitra Saleh,
Nasaruddin, Misran Safar, Deniyatno
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