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Disentangling the Drivers of Dengue Transmission: A Mixed-Methods Approach Combining Path Analysis and Spatial Clustering in Banjarbaru, South Kalimantan

JURNAL KESEHATAN LINGKUNGAN (Journal of Environmental Health) · Vol. 18 No. 1si (2026) · hlm. 84–93

DOI
10.20473/jkl.v18i1si.2026.84-93
ISSN
2540-881X, 1829-7285
Terbit
2026-06-21
Penerbit
Universitas Airlangga
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc-sa/4.0

Abstrak

Introduction: Dengue Fever remains a major public health problem. Nonetheless, the association between climatic factors and spatial distribution of cases are poorly understood. This study aimed to investigate the direct and indirect effects of climate variables on dengue incidence and map the spatiotemporal pattern of dengue cases in Banjarbaru, South Kalimantan, during 2016–2024. Methods: Monthly data on temperature (X1), wind speed (X2), humidity (X3), sunshine duration (X4), total rainfall (X5), and dengue cases (Y1) were analyzed using Partial Least Squares Path Modeling (PLS-PM) to assess the path relationship causalities. Meanwhile, the Global Moran’s I and Local Indicators of Spatial Association (LISA) were employed to detect the pattern of clustering. Results and Discussion: From this study reveal that PLS-PM results depict that humidity (β = 0.359, p = 0.045) and sunshine duration (β = 0.325, p = 0.007) have significant direct positive effect on dengue cases. Rainfall does not have a direct effect neither is it a mediator in the relationship. The analysis of the spatial patterns of dengue transmission revealed significant clustering for the years 2017, 2018, 2021, and 2022. Furthermore, the LISA maps showed the presence of clear high-high and low-low clusters during the years of interest. Conclusion: The recent findings suggest that rainfall may not be the primary culprit when it comes to Dengue, rather sunlight hours and humidity stand out to be direct climatic factors for Dengue. The identified spatial clusters suggest the need for location-specific interventions rather than blanket city-wide interventions.

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  • Lambung Mangkurat University
  • Universitas Negeri Surabaya
  • Universitas Airlangga
  • State University of Semarang
  • Universitas Halu Oleo
  • Institut Teknologi Sepuluh Nopember
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Rekan penulisOrganisasiArtikel bersama
Ririh YudhastutiAfiliasi tidak diketahui7
Babucarr JasseyAfiliasi tidak diketahui5
Juhairiyah JuhairiyahAfiliasi tidak diketahui3
Triwibowo Ambar GarjitoAfiliasi tidak diketahui3
Liestiana IndriyatiAfiliasi tidak diketahui3
Na’imah HijriatiLambung Mangkurat University2
Dicky AndiarsaAfiliasi tidak diketahui2
Khuliyah Candraning DiyanahUniversitas Airlangga2
Aditya Sukma PawitraUniversitas Airlangga2
Hari Basuki NotobrotoAfiliasi tidak diketahui2
Rahmat DapariAfiliasi tidak diketahui2
Lilis SulistyoriniAfiliasi tidak diketahui2
Diva Alishya ShafwahAfiliasi tidak diketahui2
Nafi'atul IrbahAfiliasi tidak diketahui2
Endang SusantiniUniversitas Negeri Surabaya1
Lisa LisdianaState University of Semarang1
M. Thamrin H.Universitas Negeri Surabaya1
Guntur TrimulyonoAfiliasi tidak diketahui1
Mahanani Tri AsriAfiliasi tidak diketahui1
Firas KhaleylaAfiliasi tidak diketahui1
Arika FebrianiLambung Mangkurat University1
Irma Sari YuliantiLambung Mangkurat University1
Dewi AnggrainiLambung Mangkurat University1
Isnawati IsnawatiAfiliasi tidak diketahui1
Wanti WantiUniversitas Halu Oleo1
Ferry KriswandanaAfiliasi tidak diketahui1
Muhammad Ir'faiAfiliasi tidak diketahui1
Muhammad PahruddinAfiliasi tidak diketahui1
Tirta FebrianiAfiliasi tidak diketahui1
I Putu Eka Widya PratamaInstitut Teknologi Sepuluh Nopember1
Siti Nur Azella ZaineAfiliasi tidak diketahui1
Anis Mahmuda ChafsahAfiliasi tidak diketahui1
Akhmad Ibnu HijaAfiliasi tidak diketahui1
Triwibowo Ambar GarjitoAfiliasi tidak diketahui1
Norjanah NorjanahAfiliasi tidak diketahui1
Untung NorleyandiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JKL/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Disentangling the Drivers of Dengue Transmission: A Mixed-Methods Approach Combining Path Analysis and Spatial Clustering in Banjarbaru, South Kalimantan</dc:title>
	<dc:creator>Isnawati</dc:creator>
	<dc:creator>Muhammad Rasyid Ridha</dc:creator>
	<dc:creator>Dewi Sri Susanti</dc:creator>
	<dc:creator>Muhammad Irfa'i</dc:creator>
	<dc:subject xml:lang="en-US">Climate Variability</dc:subject>
	<dc:subject xml:lang="en-US">Dengue Fever</dc:subject>
	<dc:subject xml:lang="en-US">LISA</dc:subject>
	<dc:subject xml:lang="en-US">PLS-PM (Path Modeling)</dc:subject>
	<dc:subject xml:lang="en-US">Spatial Analysis</dc:subject>
	<dc:description xml:lang="en-US">Introduction: Dengue Fever remains a major public health problem. Nonetheless, the association between climatic factors and spatial distribution of cases are poorly understood. This study aimed to investigate the direct and indirect effects of climate variables on dengue incidence and map the spatiotemporal pattern of dengue cases in Banjarbaru, South Kalimantan, during 2016–2024. Methods: Monthly data on temperature (X1), wind speed (X2), humidity (X3), sunshine duration (X4), total rainfall (X5), and dengue cases (Y1) were analyzed using Partial Least Squares Path Modeling (PLS-PM) to assess the path relationship causalities. Meanwhile, the Global Moran’s I and Local Indicators of Spatial Association (LISA) were employed to detect the pattern of clustering. Results and Discussion: From this study reveal that PLS-PM results depict that humidity (β = 0.359, p = 0.045) and sunshine duration (β = 0.325, p = 0.007) have significant direct positive effect on dengue cases. Rainfall does not have a direct effect neither is it a mediator in the relationship. The analysis of the spatial patterns of dengue transmission revealed significant clustering for the years 2017, 2018, 2021, and 2022. Furthermore, the LISA maps showed the presence of clear high-high and low-low clusters during the years of interest. Conclusion: The recent findings suggest that rainfall may not be the primary culprit when it comes to Dengue, rather sunlight hours and humidity stand out to be direct climatic factors for Dengue. The identified spatial clusters suggest the need for location-specific interventions rather than blanket city-wide interventions.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2026-06-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JKL/article/view/89062</dc:identifier>
	<dc:identifier>10.20473/jkl.v18i1si.2026.84-93</dc:identifier>
	<dc:source xml:lang="en-US">JURNAL KESEHATAN LINGKUNGAN ; Vol. 18 No. 1si (2026): JURNAL KESEHATAN LINGKUNGAN; 84-93</dc:source>
	<dc:source>2540-881X</dc:source>
	<dc:source>1829-7285</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JKL/article/view/89062/38065</dc:relation>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
</oai_dc:dc>