Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia
Dengue fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. A data set of confirmed dengue incidences collected in the province of West Java has allowed us to explore dengue's temporal trends and s...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022016383 |
| _version_ | 1818472543033491456 |
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| author | Ilham Saiful Fauzi Nuning Nuraini Regina Wahyudyah Sonata Ayu Bony Wiem Lestari |
| author_facet | Ilham Saiful Fauzi Nuning Nuraini Regina Wahyudyah Sonata Ayu Bony Wiem Lestari |
| author_sort | Ilham Saiful Fauzi |
| collection | DOAJ |
| description | Dengue fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. A data set of confirmed dengue incidences collected in the province of West Java has allowed us to explore dengue's temporal trends and spatial distributions to obtain more obvious insights into its spatial-temporal evolution. We utilized the Richards model to estimate the growth rate and detect the peak (or turning point) of the dengue infection wave by identifying the temporal progression at each location. Using spatial analysis of geo-referenced data from a local perspective, we investigated the changes in the spatial clusters of dengue cases and detected hot spots and cold spots in each weekly cycle. We found that the trend of confirmed dengue incidences significantly increases from January to March. More than two-third (70.4%) of the regions in West Java had their dengue infection turning point ranging from the first week of January to the second week of March. This trend clearly coincides with the peak of precipitation level during the rainy season. Further, the spatial analysis identified the hot spots distributed across central, northern, northeastern, and southeastern regions in West Java. The densely populated areas were likewise seen to be associated with the high-risk areas of dengue exposure. Recognizing the peak of epidemic and geographical distribution of dengue cases might provide important insights that may help local authorities optimize their dengue prevention and intervention programs. |
| first_indexed | 2024-04-14T04:09:51Z |
| format | Article |
| id | doaj.art-a9ca7375430e4b46afec9a0d3c442aae |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-14T04:09:51Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-a9ca7375430e4b46afec9a0d3c442aae2022-12-22T02:13:16ZengElsevierHeliyon2405-84402022-08-0188e10350Temporal trend and spatial clustering of the dengue fever prevalence in West Java, IndonesiaIlham Saiful Fauzi0Nuning Nuraini1Regina Wahyudyah Sonata Ayu2Bony Wiem Lestari3Department of Accounting, Politeknik Negeri Malang, Malang, Indonesia; Corresponding author.Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, Indonesia; Center for Mathematical Modeling and Simulation, Institut Teknologi Bandung, Bandung, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Palangka Raya, Palangkaraya, IndonesiaDepartment of Public Health, Faculty of Medicine, Universitas Padjadjaran, Bandung, Indonesia; Department of Internal Medicine, Radboud Institute for Health Sciences, Radboud University Medical Centre, Nijmegen, the NetherlandsDengue fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. A data set of confirmed dengue incidences collected in the province of West Java has allowed us to explore dengue's temporal trends and spatial distributions to obtain more obvious insights into its spatial-temporal evolution. We utilized the Richards model to estimate the growth rate and detect the peak (or turning point) of the dengue infection wave by identifying the temporal progression at each location. Using spatial analysis of geo-referenced data from a local perspective, we investigated the changes in the spatial clusters of dengue cases and detected hot spots and cold spots in each weekly cycle. We found that the trend of confirmed dengue incidences significantly increases from January to March. More than two-third (70.4%) of the regions in West Java had their dengue infection turning point ranging from the first week of January to the second week of March. This trend clearly coincides with the peak of precipitation level during the rainy season. Further, the spatial analysis identified the hot spots distributed across central, northern, northeastern, and southeastern regions in West Java. The densely populated areas were likewise seen to be associated with the high-risk areas of dengue exposure. Recognizing the peak of epidemic and geographical distribution of dengue cases might provide important insights that may help local authorities optimize their dengue prevention and intervention programs.http://www.sciencedirect.com/science/article/pii/S2405844022016383Dengue feverInfectious disease modelingTemporal trendRichards modelSpatial clusteringHot spot analysis |
| spellingShingle | Ilham Saiful Fauzi Nuning Nuraini Regina Wahyudyah Sonata Ayu Bony Wiem Lestari Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia Heliyon Dengue fever Infectious disease modeling Temporal trend Richards model Spatial clustering Hot spot analysis |
| title | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
| title_full | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
| title_fullStr | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
| title_full_unstemmed | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
| title_short | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
| title_sort | temporal trend and spatial clustering of the dengue fever prevalence in west java indonesia |
| topic | Dengue fever Infectious disease modeling Temporal trend Richards model Spatial clustering Hot spot analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022016383 |
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