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

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.

Multiple Nodular Lesions in Spleen and Liver in Visceral Leishmaniasis

“…Although VL is seen in both genders, most of our cases were men, similar to the previous studies (15,16,(24)(25)(26). Since men live outdoor more than women, they are exposed to phlebotomine sand flies which is a vector that causes VL transmission (25).…”

“…Since men live outdoor more than women, they are exposed to phlebotomine sand flies which is a vector that causes VL transmission (25). Additionally, sex-linked biological factors such as sex hormone may affect immune responses to the parasites (25,26).…”

An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan

“…where d i is an instance of disease incidence at a location, defined by a pair of coordinates (x, y), and t is the time of the occurrence incident. Clustering works as a high performance algorithm for hotspot detection in spatio-temporal data [62,63]. We employ a spatial algorithm for finding dense disease incidence areas called hotspots.…”

Abordagens Geográfica da Vigilância, Prevenção e Promoção da Saúde

“…Os sistemas de informação geográfica têm sido amplamente empregados na avaliação da dispersão espacial e temporal da LV em vários estados brasileiros (ANTONIALLI et al, 2007;FURTADO et al, 2015;SILVA et al, 2017) e no exterior (ZHENG et al, 2018). Por meio deles, é possível identificar espacialmente padrões de distribuição, áreas de risco e prioritárias para que autoridades de saúde pública implementem e/ou otimizem as ações de controle e vigilância (CARDIM et al, 2016;KIRBY;DELMELLE;EBERTH, 2017).…”