Melola Discovery

← Melola Discovery

Akses terbuka Jurnal

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

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

DOI
10.20956/j.v22i3.49930
ISSN
2614-8811, 1858-1382
Terbit
2026-05-14
Penerbit
Department of Mathematics, Hasanuddin University
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

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.

Kata kunci

Jaringan kolaborasi para penulis

40 penulis · 114 tautan kepenulisan bersama · 8 lintas organisasi · menampilkan 40 yang paling banyak terhubung

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Universitas Halu Oleo
  • Hasanuddin University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Baharuddin BaharuddinHasanuddin University8
Bahriddin AbapihiUniversitas Halu Oleo6
Mustamin AnggoUniversitas Halu Oleo2
Andi TenriawaruUniversitas Halu Oleo2
Ruslan RuslanUniversitas Halu Oleo2
Rahmalia SahupalaUniversitas Halu Oleo2
Yasnani YasnaniUniversitas Halu Oleo1
Fadilah SahdaUniversitas Halu Oleo1
Asnia ZainuddinUniversitas Halu Oleo1
Vina AuraningtyasUniversitas Halu Oleo1
Ramadhan TosepuUniversitas Halu Oleo1
Rini RiniUniversitas 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 NurgiantoroAfiliasi tidak diketahui1
Muhamad AzharuddinUniversitas Halu Oleo1
Novayanti NovayantiUniversitas Halu Oleo1
Nur Arfa YantiUniversitas Halu Oleo1
Haidin HaidinUniversitas Halu Oleo1
Gusti Ngurah Adhi WibawaUniversitas Halu Oleo1
Makkulau MakkulauUniversitas Halu Oleo1
Lilis LaomeUniversitas Halu Oleo1
Agusrawati AgusrawatiUniversitas Halu Oleo1
Herdi BudimanUniversitas Halu Oleo1
Wayan SomayasaUniversitas Halu Oleo1
Jufra JufraAfiliasi tidak diketahui1
Muh. Kabil DjafarUniversitas Halu Oleo1
Norma MukhtarAfiliasi tidak diketahui1
Catur Budi NugrohoUniversitas Halu Oleo1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 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">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>
</oai_dc:dc>