Spatio-temporal Transmission and Environmental Determinants of Schistosomiasis Japonica in Anhui Province, China

Overview

Journal PLoS Negl Trop Dis

Specialties Infectious Diseases
Tropical Medicine

Date 2015 Feb 7

PMID 25659112

Citations 16

Authors

Yi Hu

Rui Li

Robert Bergquist

Henry Lynn

Fenghua Gao

Qizhi Wang

Shiqing Zhang

Liqian Sun

Zhijie Zhang

Qingwu Jiang

Affiliations

Soon will be listed here.

Abstract

Background: Schistosomiasis japonica still remains of public health and economic significance in China, especially in the lake and marshland areas along the Yangtze River Basin, where the control of transmission has proven difficult. In the study, we investigated spatio-temporal variations of S. japonicum infection risk in Anhui Province and assessed the associations of the disease with key environmental factors with the aim of understanding the mechanism of the disease and seeking clues to effective and sustainable schistosomiasis control.

Methodology/principal Findings: Infection data of schistosomiasis from annual conventional surveys were obtained at the village level in Anhui Province, China, from 2000 to 2010 and used in combination with environmental data. The spatio-temporal kriging model was used to assess how these environmental factors affected the spatio-temporal pattern of schistosomiasis risk. Our results suggested that seasonal variation of the normalized difference vegetation index (NDVI), seasonal variation of land surface temperature at daytime (LSTD), and distance to the Yangtze River were negatively significantly associated with risk of schistosomiasis. Predictive maps showed that schistosomiasis prevalence remained at a low level and schistosomiasis risk mainly evolved along the Yangtze River. Schistosomiasis risk also followed a focal spatial pattern, fluctuating temporally with a peak (the largest spatial extent) in 2005 and then contracting gradually but with a scattered distribution until 2010.

Conclusion: The fitted spatio-temporal kriging model can capture variations of schistosomiasis risk over space and time. Combined with techniques of geographic information system (GIS) and remote sensing (RS), this approach facilitates and enriches risk modeling of schistosomiasis, which in turn helps to identify prior areas for effective and sustainable control of schistosomiasis in Anhui Province and perhaps elsewhere in China.

Citing Articles

Unraveling trends in schistosomiasis: deep learning insights into national control programs in China.

Su Q, Bauer C, Bergquist R, Cao Z, Gao F, Zhang Z Epidemiol Health. 2024; 46:e2024039.

PMID: 38514196 PMC: 11369565. DOI: 10.4178/epih.e2024039.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study.

Liu X, Sun Y, Yin Y, Dai X, Bergquist R, Gao F Infect Dis Poverty. 2023; 12(1):108.

PMID: 38017569 PMC: 10685489. DOI: 10.1186/s40249-023-01163-3.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.

Grover E, Allshouse W, Lund A, Liu Y, Paull S, James K Int J Health Geogr. 2023; 22(1):12.

PMID: 37268933 PMC: 10236814. DOI: 10.1186/s12942-023-00331-w.

Three Gorges Dam: the changing trend of snail density in the Yangtze River basin between 1990 and 2019.

Gong Y, Tong Y, Jiang H, Xu N, Yin J, Wang J Infect Dis Poverty. 2023; 12(1):45.

PMID: 37118831 PMC: 10142781. DOI: 10.1186/s40249-023-01095-y.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination.

Grover E, Allshouse W, Lund A, Liu Y, Paull S, James K Res Sq. 2023; .

PMID: 36747768 PMC: 9901017. DOI: 10.21203/rs.3.rs-2511279/v1.

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