Sandwich mapping of schistosomiasis risk in Anhui Province, China
Author(s) -
Yi Hu,
Robert Bergquist,
Henry Lynn,
Fenghua Gao,
Qizhi Wang,
Shiqing Zhang,
Rui Li,
Liqian Sun,
Congcong Xia,
Chenglong Xiong,
Zhijie Zhang,
Qingwu Jiang
Publication year - 2015
Publication title -
geospatial health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.545
H-Index - 36
eISSN - 1970-7096
pISSN - 1827-1987
DOI - 10.4081/gh.2015.324
Subject(s) - kriging , schistosomiasis japonica , statistics , sampling (signal processing) , china , multivariate statistics , geostatistics , geography , stratified sampling , sample (material) , schistosomiasis , homogeneous , physical geography , spatial variability , mathematics , computer science , schistosoma japonicum , biology , archaeology , filter (signal processing) , chromatography , helminths , computer vision , zoology , chemistry , combinatorics
Schistosomiasis mapping using data obtained from parasitological surveys is frequently used in planning and evaluation of disease control strategies. The available geostatistical approaches are, however, subject to the assumption of stationarity, a stochastic process whose joint probability distribution does not change when shifted in time. As this is impractical for large areas, we introduce here the sandwich method, the basic idea of which is to divide the study area (with its attributes) into homogeneous subareas and estimate the values for the reporting units using spatial stratified sampling. The sandwich method was applied to map the county-level prevalence of schistosomiasis japonica in Anhui Province, China based on parasitological data collected from sample villages and land use data. We first mapped the county-level prevalence using the sandwich method, then compared our findings with block Kriging. The sandwich estimates ranged from 0.17 to 0.21% with a lower level of uncertainty, while the Kriging estimates varied from 0 to 0.97% with a higher level of uncertainty, indicating that the former is more smoothed and stable compared to latter. Aside from various forms of reporting units, the sandwich method has the particular merit of simple model assumption coupled with full utilization of sample data. It performs well when a disease presents stratified heterogeneity over space
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