Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control · 2015
[Spatial regression analysis of relationship between schistosome infection rate of Oncomelania hupensis snails and climate factors].
Аннотация на английскомСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Chen Yan-yan, Iu Jian-bing, Xiao Ying, Zhou Xiao-rong, Jiang Yong, Dai Ling-feng, Cai Shun-xiang
Аннотация
OBJECTIVE: To explore the relationship between the schistosome infection rate of O. hupensis snails and the climate factors in endemic areas of schistosomiasis, so as to provide the evidence for improving the snail control.
METHODS: The snail and climate data of 18 counties in Hubei Province in 2009 were collected to obtain the infection rate of O. hupensis snails and to fit the spatial regression models.
RESULTS: The multiple linear regression model showed that the residuals were autocorrelated (Moran's I = 0.182 8, P < 0.01) and the spatial regression was necessary. The spatial lag model (SLM) was selected according to the results obtained by Lagrange multiplier statistics. The spatial parameter ρ of SLM was significant (p= - 0.151 5, P < 0.05) and the infection rate of O. hupensis snails was positively correlated with the annual average temperature (P < 0.05). The correlations between the infection rate of O. hupensis and the annual average relative humidity, precipitation and sunshine duration were not significant respectively (all P > 0.05).
CONCLUSIONS: The spatial regression models could be well applied in the analysis of the relationship between the O. hupensis snails and climate factors. The annual average temperature is the primary climate factor influencing the infection of O. hupensis snails.
METHODS: The snail and climate data of 18 counties in Hubei Province in 2009 were collected to obtain the infection rate of O. hupensis snails and to fit the spatial regression models.
RESULTS: The multiple linear regression model showed that the residuals were autocorrelated (Moran's I = 0.182 8, P < 0.01) and the spatial regression was necessary. The spatial lag model (SLM) was selected according to the results obtained by Lagrange multiplier statistics. The spatial parameter ρ of SLM was significant (p= - 0.151 5, P < 0.05) and the infection rate of O. hupensis snails was positively correlated with the annual average temperature (P < 0.05). The correlations between the infection rate of O. hupensis and the annual average relative humidity, precipitation and sunshine duration were not significant respectively (all P > 0.05).
CONCLUSIONS: The spatial regression models could be well applied in the analysis of the relationship between the O. hupensis snails and climate factors. The annual average temperature is the primary climate factor influencing the infection of O. hupensis snails.