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Stochastic and Deterministic Dynamic Model of Dengue Transmission Based on Dengue Incidence Data and Climate Factors in Bandung City

Communication in Biomathematical Sciences · Vol. 5 No. 1 (2022) · pp. 78–89

DOI
10.5614/cbms.2022.5.1.5
ISSN
2549-2896
Published
2022-08-02
Publisher
The Indonesian Bio-Mathematical Society
Language
eng
License
creativecommons.org/licenses/by-nd/4.0

Abstract

Indonesia, a country in the tropics, is an area of distribution and an endemic area of dengue. The death rate caused by dengue is relatively high In Indonesia. Therefore, the health authority must prioritize preventing and controlling dengue disease for a long-term policy. This study proposes a method based on dynamic climate variables in estimating the proportion of infected human and infected mosquito. We focus on the dengue case in Bandung city, one of the big cities in Indonesia, which is classified as endemic dengue. We applied the Poisson regression method involving dynamic climate variables to estimate the average number of infected human population. We then use these estimation results as the basis for approximating the proportion of infected human and mosquito populations using a deterministic and stochastic model approach. Effective reproduction number is also obtained here. The simulation results show that the stochastic model looks better in capturing dengue incidence data than the deterministic model. Therefore, dengue transmission can be reduced by controlling the abundance of mosquito populations, considering climate conditions and the historical number of infected human.

Keywords

Collaboration network of the authors

40 authors · 103 co-authorship links · 8 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
  • Tadulako University
  • Indonesia Open University
  • Hasanuddin University
  • State University of Malang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Natalis RansiUniversitas Halu Oleo5
Arman ArmanUniversitas Halu Oleo5
Kadir KadirUniversitas Halu Oleo3
Edy SoewonoAffiliation unknown3
Busnawir BusnawirUniversitas Halu Oleo2
Jafar JafarIndonesia Open University2
Lilis LaomeUniversitas Halu Oleo2
Norma MuhtarUniversitas Halu Oleo2
Herdi BudimanUniversitas Halu Oleo2
La SurimiUniversitas Halu Oleo1
Jumadil NangiUniversitas Halu Oleo1
Adha Mashur SajiahUniversitas Halu Oleo1
Muhammad Andi FathoniUniversitas Halu Oleo1
Muhammad SudiaAffiliation unknown1
Umrana UmranaAffiliation unknown1
Amanda La HadiAffiliation unknown1
Safri SafriUniversitas Halu Oleo1
Untung UntungAffiliation unknown1
Nana SumarnaAffiliation unknown1
Sufriadin SufriadinHasanuddin University1
SUDIRMAN SUDIRMANState University of Malang1
Safari SafariAffiliation unknown1
Hermanto HermantoUniversitas Halu Oleo1
Wayan SomayasaUniversitas Halu Oleo1
Juliana JulianaAffiliation unknown1
Rosliana EsoUniversitas Halu Oleo1
Jufra JufraAffiliation unknown1
Ambo Wonua NusantaraUniversitas Halu Oleo1
Sulastrianah SulastrianahUniversitas Halu Oleo1
Andi TenriawaruUniversitas Halu Oleo1
Ruslan RuslanUniversitas Halu Oleo1
Putu SudayasaUniversitas Halu Oleo1
Samirudin RujumiAffiliation unknown1
BusnawirAffiliation unknown1
Mustamin AnggoUniversitas Halu Oleo1
Tatiriah .Affiliation 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) http://journals.itb.ac.id/index.php/cbms/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Stochastic and Deterministic Dynamic Model of Dengue Transmission Based on Dengue Incidence Data and Climate Factors in Bandung City</dc:title>
	<dc:creator>La Pimpi</dc:creator>
	<dc:creator>Indratno, Sapto Wahyu</dc:creator>
	<dc:creator>Puspita, Juni Wijayanti</dc:creator>
	<dc:creator>Cahyono, Edi</dc:creator>
	<dc:subject xml:lang="en-US">Climate variables</dc:subject>
	<dc:subject xml:lang="en-US">dengue disease transmission</dc:subject>
	<dc:subject xml:lang="en-US">effective reproduction number</dc:subject>
	<dc:subject xml:lang="en-US">Poisson regression model</dc:subject>
	<dc:subject xml:lang="en-US">infected human</dc:subject>
	<dc:subject xml:lang="en-US">infected mosquito</dc:subject>
	<dc:subject xml:lang="en-US">62M10</dc:subject>
	<dc:subject xml:lang="en-US">62J05</dc:subject>
	<dc:subject xml:lang="en-US">62P10</dc:subject>
	<dc:description xml:lang="en-US">Indonesia, a country in the tropics, is an area of distribution and an endemic area of dengue. The death rate caused by dengue is relatively high In Indonesia. Therefore, the health authority must prioritize preventing and controlling dengue disease for a long-term policy. This study proposes a method based on dynamic climate variables in estimating the proportion of infected human and infected mosquito. We focus on the dengue case in Bandung city, one of the big cities in Indonesia, which is classified as endemic dengue. We applied the Poisson regression method involving dynamic climate variables to estimate the average number of infected human population. We then use these estimation results as the basis for approximating the proportion of infected human and mosquito populations using a deterministic and stochastic model approach. Effective reproduction number is also obtained here. The simulation results show that the stochastic model looks better in capturing dengue incidence data than the deterministic model. Therefore, dengue transmission can be reduced by controlling the abundance of mosquito populations, considering climate conditions and the historical number of infected human.</dc:description>
	<dc:publisher xml:lang="en-US">The Indonesian Bio-Mathematical Society</dc:publisher>
	<dc:date>2022-08-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/cbms/article/view/18603</dc:identifier>
	<dc:identifier>10.5614/cbms.2022.5.1.5</dc:identifier>
	<dc:source xml:lang="en-US">Communication in Biomathematical Sciences; Vol. 5 No. 1 (2022); 78-89</dc:source>
	<dc:source>2549-2896</dc:source>
	<dc:source>10.5614/cbms.2022.5.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/cbms/article/view/18603/5890</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Communication in Biomathematical Sciences</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nd/4.0</dc:rights>
</oai_dc:dc>