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Unveiling Eco-Epidemiological Risk Assessment through Bayesian Spatio-Temporal SPDE-INLA Approach

Jurnal Matematika, Statistika dan Komputasi · Vol. 22 No. 3 (2026) · pp. 655–670

DOI
10.20956/j.v22i3.49930
ISSN
2614-8811, 1858-1382
Published
2026-05-14
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Dengue hemorrhagic fever (DHF) remains a major public health burden in tropical areas. The DHF driven by nonlinear interactions, vector dynamics, and population density. Characterizing spatio-temporal risk heterogeneity is critical to targeted intervention. We analyzed dengue risk in Kendari using a Bayesian Stochastic Partial Differential Equation (SPDE) via Integrated Nested Laplace Approximation (INLA). DHF monthly data from 2022–2024 were integrated with geospatial information to estimate relative risk and spatial risk contours. Model performance was compared with Generalized Linear Models (GLM), Intrinsic Conditional Autoregressive (ICAR), and second order Random Walk (RW2). Kendari Barat and Kendari districts emerged as primary hotspots, while Kadia, Mandonga, Baruga, Kambu, and Wua-Wua were high-risk districts. Abeli, Poasia, and Puuwatu districts exhibited moderate risk. Lalodati, Soropia, and Sampara districts showed lower risk. Risk contours revealed clusters concentrated in the urban core and along major corridors, highlighting the influence of settlement density and spatial connectivity. The SPDE–INLA model outperformed GLM, ICAR, and RW2 in capturing spatial structure and improving predictive accuracy. High resolution risk estimates supported specific district including intensified source reduction before and during peak rainfall, selective fogging, larval control, community education, microclimate monitoring, and early case screening in high-risk areas.

Keywords

Collaboration network of the authors

40 authors · 94 co-authorship links · 5 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Baharuddin BaharuddinHasanuddin University5
Mustamin AnggoUniversitas Halu Oleo3
Hafiludin SamparadjaAffiliation unknown2
Wahyuni WahyuniHasanuddin University2
Yasnani YasnaniUniversitas Halu Oleo1
Fadilah SahdaUniversitas Halu Oleo1
Asnia ZainuddinUniversitas Halu Oleo1
Vina AuraningtyasUniversitas Halu Oleo1
Ramadhan TosepuUniversitas Halu Oleo1
Rini RiniUniversitas Halu Oleo1
Nur Arfa YantiAffiliation unknown1
Sri AmbardiniUniversitas Halu Oleo1
Dirvamena BoerUniversitas Halu Oleo1
Darwis SulemanUniversitas Halu Oleo1
Sawaludin SawaludinUniversitas Halu Oleo1
Sani FitrianiUniversitas Halu Oleo1
Noor Husna KhairisaUniversitas Halu Oleo1
Nur AlfianiUniversitas Halu Oleo1
Loade Muh. Iradat SalihinUniversitas Halu Oleo1
Nurgiantoro NurgiantoroAffiliation unknown1
Muhamad AzharuddinUniversitas Halu Oleo1
Muhammad SudiaAffiliation unknown1
Sri WahyuningsihAffiliation unknown1
Kadir KadirUniversitas Halu Oleo1
Misryati MisryatiAffiliation unknown1
Safrin SafrinAffiliation unknown1
Novayanti NovayantiUniversitas Halu Oleo1
Rahmat AlimuddinAffiliation unknown1
M. TahirAffiliation unknown1
Rini HamsidiAffiliation unknown1
Nur Arfa YantiUniversitas Halu Oleo1
Haidin HaidinUniversitas Halu Oleo1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Unveiling Eco-Epidemiological Risk Assessment through Bayesian Spatio-Temporal SPDE-INLA Approach</dc:title>
	<dc:creator xml:lang="en">Mukhsar, Mukhsar</dc:creator>
	<dc:creator xml:lang="en">Usman, Ida</dc:creator>
	<dc:creator xml:lang="en">Sani, Asrul</dc:creator>
	<dc:creator xml:lang="en">Gubu, La</dc:creator>
	<dc:creator xml:lang="en">Muzuni, Muzuni</dc:creator>
	<dc:creator xml:lang="en">Majid, Ruslan</dc:creator>
	<dc:creator xml:lang="en">Sudayasa, I Putu</dc:creator>
	<dc:creator xml:lang="en">Fahmiati, Fahmiati</dc:creator>
	<dc:subject xml:lang="en">Bayesian</dc:subject>
	<dc:subject xml:lang="en">DHF</dc:subject>
	<dc:subject xml:lang="en">INLA</dc:subject>
	<dc:subject xml:lang="en">,Relative Risk</dc:subject>
	<dc:subject xml:lang="en"> SPDE</dc:subject>
	<dc:description xml:lang="en">Dengue hemorrhagic fever (DHF) remains a major public health burden in tropical areas. The DHF driven by nonlinear interactions, vector dynamics, and population density. Characterizing spatio-temporal risk heterogeneity is critical to targeted intervention. We analyzed dengue risk in Kendari using a Bayesian Stochastic Partial Differential Equation (SPDE) via Integrated Nested Laplace Approximation (INLA). DHF monthly data from 2022–2024 were integrated with geospatial information to estimate relative risk and spatial risk contours. Model performance was compared with Generalized Linear Models (GLM), Intrinsic Conditional Autoregressive (ICAR), and second order Random Walk (RW2). Kendari Barat and Kendari districts emerged as primary hotspots, while Kadia, Mandonga, Baruga, Kambu, and Wua-Wua were high-risk districts. Abeli, Poasia, and Puuwatu districts exhibited moderate risk. Lalodati, Soropia, and Sampara districts showed lower risk. Risk contours revealed clusters concentrated in the urban core and along major corridors, highlighting the influence of settlement density and spatial connectivity. The SPDE–INLA model outperformed GLM, ICAR, and RW2 in capturing spatial structure and improving predictive accuracy. High resolution risk estimates supported specific district including intensified source reduction before and during peak rainfall, selective fogging, larval control, community education, microclimate monitoring, and early case screening in high-risk areas.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2026-05-14</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://journal.unhas.ac.id/index.php/jmsk/article/view/49930</dc:identifier>
	<dc:identifier>10.20956/j.v22i3.49930</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 22 No. 3 (2026): May 2026; 655-670</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/49930/14417</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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