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. 2024 May 17;15(5):366.
doi: 10.3390/insects15050366.

Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia

Mamenun  1   2 , Yonny Koesmaryono  2 , Ardhasena Sopaheluwakan  3 , Rini Hidayati  2   4 , Bambang Dwi Dasanto  2 , Rita Aryati  5
Affiliations

Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia

Mamenun et al. Insects. .

Abstract

Dengue has become a public health concern in Indonesia since it was first found in 1968. This study aims to determine dengue hotspot areas and analyze the spatiotemporal distribution of dengue and its association with dominant climate parameters nationally. Monthly data for dengue and climate observations (i.e., rainfall, relative humidity, average, maximum, and minimum temperature) at the regency/city level were utilized. Dengue hotspot areas were determined through K-means clustering, while Singular Value Decomposition (SVD) determined dominant climate parameters and their spatiotemporal distribution. Results revealed four clusters: Cluster 1 comprised cities with medium to high Incidence Rates (IR) and high Case Densities (CD) in a narrow area. Cluster 2 has a high IR and low CD, and clusters 3 and 4 featured medium and low IR and CD, respectively. SVD analysis indicated that relative humidity and rainfall were the most influential parameters on IR across all clusters. Temporal fluctuations in the first mode of IR and climate parameters were clearly delineated. The spatial distribution of heterogeneous correlation between the first mode of rainfall and relative humidity to IR exhibited higher values, which were predominantly observed in Java, Bali, Nusa Tenggara, the eastern part of Sumatra, the southern part of Kalimantan, and several locations in Sulawesi.

Keywords: Indonesia; climate; dengue; hotspot area; incidence rate.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Spatial distribution of data completeness in Indonesia: (a) for dengue data, (b) for 156 climate stations. Stations marked with blue dot represent those stations used in analyses, while marked X represent those stations excluded from analyses because of a lack of data completeness.
Figure 2
Figure 2
Geographical distribution of annual incidence rate (cases per 100,000 inhabitants) from 2010 to 2020 in Indonesia.
Figure 3
Figure 3
(a) Four clusters of dengue endemic regency/city level, and (b) the percentage of cluster members based on incidence rate and case density for the period 2010–2020.
Figure 4
Figure 4
Map of dengue hotspot areas based on clustering: (a) national level, (b) Java Island, Bali, and Lombok.
Figure 5
Figure 5
Temporal patterns of coefficient expansion of the first mode of climate factors and dengue incidence rate for each cluster; (a) cluster 1, (b) cluster 2, (c) cluster 3, and (d) cluster 4, and for climate parameters for each cluster; (A) RR, (B) RH, (C) Tavg, (D) Tmax, and (E) Tmin.
Figure 6
Figure 6
The heterogeneous correlation map between the expansion coefficients of the first mode of climate factors and IR in all clusters; (a) RR, (b) RH, (c) Tavg, (d) Tmax, and (e) Tmin.
See this image and copyright information in PMC

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