Chin J Schisto Control ›› 2007, Vol. 19 ›› Issue (4): 289-292.

Sensitivity analysis of the yearly extreme low temperature to predict the distribution of On.comelania hupensis

Cui Dao-yong,Zhang zhi-jie, Ni Ying', Pen.g Wen.-xiang, Zhuang Jian-lin, Zhou Yi-biao

• Online:2013-01-06 Published:2013-01-14

年极端低气温在湖北钉螺分布中的敏感性分析

1. 1安徽省池州市贵池区血吸虫病防治站（池州247000）；2复旦大学公共卫生学院流行病学教研室、救育部公共卫生安全重点实验室
• 作者简介:崔道永(19 52 -)|男|主管医师。研究方向：血吸虫病防治

Abstract:

Objective   To explore the sensitive temperature index in order to predict the distribu-
tion of Oncomelania hupensis. Methods   Sixty-one weather stations, distributed in Shanghai, Jiang-
su. Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Hebei, Henan: Shandong, Shanxi, Shaanxi,
Sichuan provinces (municipality) , were selected as research stations.  North latitude 34 was set as
the boundary of Oncomelan,ia hupensis's distribution. The differences of yearly extreme low temper-
ature and mean temperature in a year between snail areas and no snail areas were tested respectively
by using t test to show their respective significance on the prediction of Oncomelania hupensis's dis-
tribution. Finally, unconditional logistic regression analysis was used to determine which tempera-
ture index had the better effect to predict the distribution of Oncomelan,ia hupensis. Results  Both
the yearly extreme low temperature and mean temperature in a year had significant effect on the dis-
tribution of Oncomelania hupensis under the t test (t= - 6. 49,  P<0.01; t= - 3. 93,  P<0. 01).
The differences of the yearly extreme low temperature and mean temperature in a year between snail
areas and no snail areas were 6. 72 'C and 3. 02 'C respectivcly, and the degree of their differences
was about 2. 23 times.  The impact of yearly extreme low temperature was more important than that
of mean temperature in a year because mean temperature in a year became no longer statistically sig-
nificant if they were tested simultaneously by using unconditional logistic regression analysis. Con-
clusion  Yearly extreme low temperature may be more sensitive on predicting the distribution of  Oncomelania hupensis than mean temperature in a year.

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