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. 2018 Dec 8;15(12):2784.
doi: 10.3390/ijerph15122784.

Spatiotemporal Variation and Hot Spot Detection of Visceral Leishmaniasis Disease in Kashi Prefecture, China

Canjun Zheng  1 , Jingying Fu  2   3 , Zeng Li  4 , Gang Lin  5 , Dong Jiang  6   7 , Xiao-Nong Zhou  8
Affiliations

Spatiotemporal Variation and Hot Spot Detection of Visceral Leishmaniasis Disease in Kashi Prefecture, China

Canjun Zheng et al. Int J Environ Res Public Health. .

Abstract

Visceral leishmaniasis (VL) remains a serious public health problem in China. To explore the temporal, spatial, and spatiotemporal characteristics of visceral leishmaniasis (VL), the spatial and spatiotemporal clustering distribution and their relationships with the surrounding geographic environmental factors were analyzed. In this study, the average nearest-neighbor distance (ANN), Ripley's K-function and Moran's I statistics were used to evaluate spatial autocorrelation in the VL distribution of the existing case patterns. Getis⁻Ord Gi* was used to identify the hot-spot and cold-spot areas based on Geographic Information System (GIS), and spatiotemporal retrospective permutation scan statistics was used to detect the spatiotemporal clusters. The results indicated that VL continues to be a serious public health problem in Kashi Prefecture, China, particularly in the north-central region of Jiashi County, which is a relatively high-risk area in which hot spots are distributed. Autumn and winter months were the outbreak season for VL cases. The detection of spatial and spatiotemporal patterns can provide epidemiologists and local governments with significant information for prevention measures and control strategies.

Keywords: GIS; Kashi Prefecture; hot spots; spatiotemporal clusters.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study region, Kashi Prefecture and its counties.
Figure 2
Figure 2
Research Technology Roadmap.
Figure 3
Figure 3
(a) Yearly distribution of visceral leishmaniasis (VL) cases from 2005 to 2017 and (b) monthly aggregated VL cases incidence.
Figure 4
Figure 4
Geographical distribution of all VL cases between 2005 and 2017.
Figure 5
Figure 5
Hot spots and spatiotemporal scan statistical analysis.
Figure 6
Figure 6
Mapping showing the relationships between VL cases’ distribution and environmental factors. Note: (a) represents the annual average temperature, (b) represents the annual average precipitation, (c) represents the annual average normalized difference vegetation index (NDVI), and (d) represents the annual average relative humidity from 2005 to 2017.
See this image and copyright information in PMC

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