Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.
Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.
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利用搜索引擎查询数据和气候变量预测手足口病发病率：中国广东省的一项生态研究
DOI：
10.1136/bmjopen-2017-016263
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发表时间：
2017-10-06
期刊：
影响因子：
2.9
通讯作者：
Hao Y
中科院分区：
文献类型：
--
作者：
Du Z;Xu L;Zhang W;Zhang D;Yu S;Hao Y
目的手足口病（HFMD）在我国，特别是广东省已造成严重的疾病负担。本研究以加强监测系统为基础，探讨增加温带及搜寻引擎查询资料是否能改善手足口病的风险预测。设计生态研究。收集关于手足口病确诊病例、气候参数和搜索引擎查询日志的信息。2011-2014年期间，监测系统共发现136万例手足口病病例。分析是在总体水平上进行的，不涉及机密信息。结果测量使用带外部变量的季节性自回归积分移动平均（ARIMA）模型（ARIMAX）预测2011 - 2014年手足口病发病率，考虑温度和搜索引擎查询数据（百度指数，BDI）。拟合优度和预测精度的统计用于比较模型（1）仅基于监测数据，以及添加（2）温度，（3）BDI和（4）温度和BDI。结果手足口病发病率与BDI（r=0.794，p<0.001）和温度（r=0.657，p<0.001）呈高度相关。在分布滞后非线性模型中，发现BDI对HFMD发病率有线性影响（无滞后），温度对HFMD发病率有非线性影响（滞后1周）。与仅基于监测数据的模型相比，包含BDI的ARIMAX模型达到了最佳拟合优度，赤池信息准则（AIC）值为-345.332，而包含BDI和温度的模型具有最准确的预测，平均绝对百分比误差（MAPE）为101.745%。结论结合搜索引擎查询数据的ARIMAX模型显著提高了手足口病的预测能力。进一步的研究是必要的，以检查是否包括搜索引擎查询数据也提高了其他环境中的其他传染病的预测。
Objectives Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Design Ecological study. Setting and participants Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved. Outcome measures A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. Results A high correlation between HFMD incidence and BDI (r=0.794, p<0.001) or temperature (r=0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%. Conclusions An ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings.
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