Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors.
Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors.
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郑州市手足口病住院时间序列分析：建立以气候变量为预测变量的预测模型
DOI：
10.1371/journal.pone.0087916
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Zhang W
中科院分区：
文献类型：
--
作者：
Feng H;Duan G;Zhang R;Zhang W
背景 自2008年以来，中国大陆手足口病（HFMD）大规模暴发频繁，并造成神经系统后遗症。提前几周预测手足口病疫情活动有助于采取预防措施，有效控制手足口病。方法 采用逆转录酶聚合酶链反应（RT-PCR）技术对河南省郑州市2008年至2012年手足口病住院儿童样本进行病原学检测，采用季节性自回归移动平均（SARIMA）模型对手足口病周数、人肠道病毒71（HEV71）和柯萨奇病毒A16（CoxA16）相关手足口病进行分析。开发并验证。计算手足口病住院人数与气候变量之间的互相关性，以确定作为外部因素纳入的重要变量。当存在显着的预测气象变量时，使用多元 SARIMA 模型进行时间序列建模。结果 2008年1月至2012年6月，2932份手足口病住院患者样本中，748份检出HEV71，527份检出CoxA16，787份检出其他肠道病毒（其他EV）。滞后2周或3周的平均大气温度（T{avg}）被认为是手足口病和病原体数量的重要预测因子。开发并验证了与滞后 2 (T{avg}-Lag 2) 周的 T{avg} 相关的 SARIMA(0,1,0)(1,0,0)52、与 T{avg}-滞后 2 周的 SARIMA(0,1,2)(1,0,0)52 和与 T{avg}-滞后 3 周的 SARIMA(0,1,1)(1,1,0)52 相关的 SARIMA(0,1,1)(1,1,0)52，用于描述和预测每周手足口病、HEV71 相关手足口病和 Cox A16 相关手足口病住院人数。结论 某些手足口病病原体的季节变化可能与气象因素有关。包含气候变量的 SARIMA 模型可用作预测年度手足口病流行的早期可靠监测系统。
Background Large-scale outbreaks of hand-foot-mouth disease (HFMD) have occurred frequently and caused neurological sequelae in mainland China since 2008. Prediction of the activity of HFMD epidemics a few weeks ahead is useful in taking preventive measures for efficient HFMD control. Methods Samples obtained from children hospitalized with HFMD in Zhengzhou, Henan, China, were examined for the existence of pathogens with reverse-transcriptase polymerase chain reaction (RT-PCR) from 2008 to 2012. Seasonal Autoregressive Integrated Moving Average (SARIMA) models for the weekly number of HFMD, Human enterovirs 71(HEV71) and CoxsackievirusA16 (CoxA16) associated HFMD were developed and validated. Cross correlation between the number of HFMD hospitalizations and climatic variables was computed to identify significant variables to be included as external factors. Time series modeling was carried out using multivariate SARIMA models when there was significant predictor meteorological variable. Results 2932 samples from the patients hospitalized with HFMD, 748 were detected with HEV71, 527 with CoxA16 and 787 with other enterovirus (other EV) from January 2008 to June 2012. Average atmospheric temperature (T{avg}) lagged at 2 or 3 weeks were identified as significant predictors for the number of HFMD and the pathogens. SARIMA(0,1,0)(1,0,0)52 associated with T{avg} at lag 2 (T{avg}-Lag 2) weeks, SARIMA(0,1,2)(1,0,0)52 with T{avg}-Lag 2 weeks and SARIMA(0,1,1)(1,1,0)52 with T{avg}-Lag 3 weeks were developed and validated for description and predication the weekly number of HFMD, HEV71-associated HFMD, and Cox A16-associated HFMD hospitalizations. Conclusion Seasonal pattern of certain HFMD pathogens can be associated by meteorological factors. The SARIMA model including climatic variables could be used as an early and reliable monitoring system to predict annual HFMD epidemics.
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