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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Bibliographic Details
Published in:Heliyon 2022-08, Vol.8 (8), p.e10350-e10350, Article e10350
Main Authors: Fauzi, Ilham Saiful, Nuraini, Nuning, Ayu, Regina Wahyudyah Sonata, Lestari, Bony Wiem
Format: Article
Language:English
Subjects:
Dengue fever
Hot spot analysis
Infectious disease modeling
Richards model
Spatial clustering
Temporal trend
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Summary: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. Dengue fever; Infectious disease modeling; Temporal trend; Richards model; Spatial clustering; Hot spot analysis
ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2022.e10350