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Bayesian Conditional Autoregressive (CAR) dengan Model Localised dalam Menaksir Risiko Relatif DBD di Kota Makassar

ESTIMASI: Journal of Statistics and Its Application · Vol. 1 No. 1 (2022) · pp. 21–31

DOI
10.20956/ejsa.v1i1.9298
ISSN
2721-379X, 2721-3803
Published
2022-02-02
Publisher
Hasanuddin University
Language
en

Abstract

Bayesian Conditional Autoregressive (CAR) is used in disease mapping because it is able to model relative risks by taking into account the smoothing of the estimated relative risk and entering spatial information to reduce the errors of the estimated relative risk parameters so that a more reliable relative risk model is obtained. In this study, the relative risk value of the spread of dengue fever will be calculated using Bayesian CAR with the localised model. These results were obtained using the OpenBUGS program and are illustrated in the 2016 dengue fever case data. Based on the model, mapping of dengue fever in Makassar can be identified in each district and shows that Makassar City is still very vulnerable to dengue fever.

Keywords

Collaboration network of the authors

25 authors · 56 co-authorship links

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  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nirwan IlyasAffiliation unknown2
La Podje TalangkoAffiliation unknown2
Sitti SahrimanAffiliation unknown2
Grace Oktavia YusufAffiliation unknown1
Fitriatusakiah FitriatusakiahAffiliation unknown1
Raupong RaupongAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Georgina Maria TinungkiAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Anisa AnisaSultan Ageng Tirtayasa University1
Najiha AlimatunAffiliation unknown1
Nasrah SirajangAffiliation unknown1
Reski AmalahAffiliation unknown1
Nur Fuadil Maqnun WahabAffiliation unknown1
Nurtit SunusiAffiliation unknown1
Ainul FajriAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Ika Pratiwi HayaAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
Wa Ode Sitti Amni AmniAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1

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/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Bayesian Conditional Autoregressive (CAR) dengan Model Localised dalam Menaksir Risiko Relatif DBD di Kota Makassar</dc:title>
	<dc:creator xml:lang="en">Khaerati, Rusydah</dc:creator>
	<dc:creator xml:lang="en">Jaya, Andi Kresna</dc:creator>
	<dc:subject xml:lang="en">Bayesian Conditional Autoregressive</dc:subject>
	<dc:subject xml:lang="en">Dengue Fever</dc:subject>
	<dc:subject xml:lang="en">Localised Model</dc:subject>
	<dc:subject xml:lang="en">Relative Risk</dc:subject>
	<dc:description xml:lang="en">Bayesian Conditional Autoregressive (CAR) is used in disease mapping because it is able to model relative risks by taking into account the smoothing of the estimated relative risk and entering spatial information to reduce the errors of the estimated relative risk parameters so that a more reliable relative risk model is obtained. In this study, the relative risk value of the spread of dengue fever will be calculated using Bayesian CAR with the localised model. These results were obtained using the OpenBUGS program and are illustrated in the 2016 dengue fever case data. Based on the model, mapping of dengue fever in Makassar can be identified in each district and shows that Makassar City is still very vulnerable to dengue fever.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2022-02-02</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/ESTIMASI/article/view/9298</dc:identifier>
	<dc:identifier>10.20956/ejsa.v1i1.9298</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 1, No. 1, Januari, 2020 : Estimasi; 21-31</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/9298/6803</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 ESTIMASI: Journal of Statistics and Its Application</dc:rights>
</oai_dc:dc>