Fine scale Spatial-temporal cluster analysis for the infection risk of Schistosomiasis japonica using space-time scan statistics

Abstract

BackgroundMarching towards the elimination of schistosomiasis in China, both the incidence and prevalence have witnessed profound decline over the past decades, with the strategy shifting from morbidity control to transmission control. The current challenge is to find out hotspots of transmission risk for precise targeted control in low-prevalence areas. This study assessed the risk at the village level, using the spatial and temporal characteristics of Schistosomiasis japonica in Anhui province from 2006 to 2012.MethodThe comprehensive database was generated from annual surveillance data at village level in Anhui province between 2006 and 2012, comprising schistosomiasis prevalence among humans and cattle, occurrence rate of infected environments and incidence rate of acute schistosomiasis. The database parameters were matched with geographic data of the study area and fine scale spatial-temporal cluster analysis based on retrospective space-time scan statistics was used to assess the clustering pattern of schistosomiasis. The analysis was conducted by using SaTScan 9.1.1 and ArcGIS 10.0. A spatial statistical modelling was carried out to determine the spatial dependency of prevalence of human infection by using Geoda 1.4.3.ResultA pronounced decline was found in the prevalence of human infection, cattle infection, occurrence rate of environment with infected vector snails and incidence rate of acute schistosomiasis from 2006 to 2012 by 48.6%, 71.5%, 91.9% and 96.4%, respectively. Meanwhile, all 4 indicators showed a statistically significant clustering pattern both in time and space, with a total of 16, 6, 8 and 4 corresponding clustering foci found respectively. However, the number of clustering foci declined with time, and none was found after year 2010. All clustering foci were mainly distributed along the Yangtze River and its connecting branches. The result shows that there is a direct spatial relationship between prevalence of human infection and the other indicators.ConclusionA decreasing trend in space-time clustering of schistosomiasis endemic status was observed between 2006 and 2012 in Anhui province. Nevertheless, giving the complexity in schistosomiasis control, areas within the upper-stream of Yangtze River in Anhui section and its connecting branches should be targeted for effective implementation of control strategies in the future.

Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China

“…These areas were a mixture of lakes, rivers and marshlands along the lake, which appeared alternately in different seasons more frequently than in other regions, providing an ideal environment for snail growth and reproduction [31]. Meanwhile, most of the permanent residents in these endemic counties are farmers and fishermen ensuring increased contact with contaminated water [32].…”

Schistosomiasis: from established diagnostic assays to emerging micro/nanotechnology-based rapid field testing for clinical management and epidemiology

“…Schistosomiasis is one of the most prevalent and widespread [1] parasitic neglected tropical diseases in the world. It is caused by fluke worms of the genus Schistosoma and affects humans and various other animals [2,3]. The 6 species involved in human infections are S. mansoni, S. haematobium, S. japonicum, S. mekongi, S. guinensii and S. intercalatum.…”

Statistical evaluation of spatial uncertainty in schistosomiasis mapping

“…moisture, temperature, rainfall and water characteristics), the topography (i.e. elevation, slope) of the area (Soares Magalhães et al, Stensgaard et al, 2013;Walz et al, 2015b) and snail infection status (Gao et al, 2014;. In the endemic provinces of the Philippines, exposure to snails is mostly driven by the local topography, land use and the physical and chemical components of the water and soil (Pesigan et al, 1958).…”

“…A Bayesian network (BN) is a probabilistic graphical model that captures the various conditional dependencies of a set of random variables (discrete or continuous) (Bishop, 2006;Bottcher and Dethlefsen, 2003), into a joint probability distribution by means of a directed acyclic graph (Fenton and Neil, 2012;Nielsen and Jensen, 2009). (Gao et al, 2014; Abbreviation: na, not applicable A BN for a set of random variables is defined by the pair , . Here, is the set of probability distributions for all variables in the network.…”

“…Secondly, model validation procedures could be improved by including positive and negative human cases. Collecting data on livestock infection (Gao et al, 2014; could also serve for validation as livestock infection, particularly carabao, has been suggested to play an important role in the transmission of S. japonicum in the Philippines (Gordon et al, 2012).…”