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首页> 外文期刊>The Southeast Asian journal of tropical medicine and public health >PREDICTING CLINICALLY DIAGNOSED DYSENTERY INCIDENCE OBTAINED FROM MONTHLY CASE REPORTING BASED ON METEOROLOGICAL VARIABLES IN DALIAN, LIAONING PROVINCE, CHINA, 2005-2011 USING A DEVELOPED MODEL
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PREDICTING CLINICALLY DIAGNOSED DYSENTERY INCIDENCE OBTAINED FROM MONTHLY CASE REPORTING BASED ON METEOROLOGICAL VARIABLES IN DALIAN, LIAONING PROVINCE, CHINA, 2005-2011 USING A DEVELOPED MODEL

机译：2005年第10期基于气象变量的辽宁省大连市2005〜2011年每月病例报告的临床诊断发病率预测。

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This study describes our development of a model to predict the incidence of clinically diagnosed dysentery in Dalian, Liaoning Province, China, using time series analysis. The model was developed using the seasonal autoregressive integrated moving average (SARIMA). Spearman correlation analysis was conducted to explore the relationship between meteorological variables and the incidence of clinically diagnosed dysentery. The meteorological variables which significantly correlated with the incidence of clinically diagnosed dysentery were then used as covariables in the model, which incorporated the monthly incidence of clinically diagnosed dysentery from 2005 to 2010 in Dalian. After model development, a simulation was conducted for the year 2011 and the results of this prediction were compared with the real observed values. The model performed best when the temperature data for the preceding month was used to predict clinically diagnosed dysentery during the following month. The developed model was effective and reliable in predicting the incidence of clinically diagnosed dysentery for most but not all months, and may be a useful tool for dysentery disease control and prevention, but further studies are needed to fine tune the model.
机译：这项研究使用时间序列分析描述了我们开发的模型，该模型可预测中国辽宁省大连市临床诊断的痢疾的发病率。该模型是使用季节性自回归综合移动平均值（SARIMA）开发的。进行了Spearman相关分析，以探讨气象变量与临床诊断的痢疾发生率之间的关系。然后，将与临床诊断出的痢疾发生率显着相关的气象变量用作协变量，该模型纳入了2005年至2010年大连市临床诊断出的痢疾的月发生率。在模型开发之后，对2011年进行了模拟，并将此预测的结果与实际观测值进行了比较。当将前一个月的温度数据用于预测下个月的临床诊断的痢疾时，该模型的效果最佳。所开发的模型可在大多数（但不是全部）月份中预测临床诊断的痢疾的发病率而有效且可靠，并且可能是控制和预防痢疾的有用工具，但需要进一步研究以调整模型。

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