我国手足口病重症患者数自回归移动平均模型预测研究北大核心CSCD

采用自回归移动平均(Autoregressive integrated moving average,ARIMA)模型对我国(不含中国港澳台)手足口病月报告的重症患者数进行预测研究,为该模型在手足口病及其它传染病预防控制中的应用提供参考依据。根据2010-2015年全国手足口病月报告重症患者数时间序列,以2016年1-9月的月报告重症患者数作为验证数据,建立我国手足口病月报告重症患者数的ARIMA模型,并与2010-2014年数据建立的模型进行比较。2010-2014、2010-2015年两个不同时间序列建立的我国手足口病月报告重症患者数模型分别为ARIMA(1,1,0)(2,1,0)12、ARIMA(0,1,1)(2,1,0)12。以上两个不同时间序列预测结果比较发现,数据积累较多,预测的平均相对误差变小,但预测时间越短尚未发现平均相对误差较小。同一研究内容,时间序列年代不同,所建立的预测模型可能不同;认为ARIMA模型数据积累越多、预测时间越短、预测误差越小的情况还需得到进一步验证。     

我国手足口病重症患者数自回归移动平均模型预测研究

采用自回归移动平均(Autoregressive integrated moving average,ARIMA)模型对我国(不含中国港澳台)手足口病月报告的重症患者数进行预测研究,为该模型在手足口病及其它传染病预防控制中的应用提供参考依据。根据2010-2015年全国手足口病月报告重症患者数时间序列,以2016年1-9月的月报告重症患者数作为验证数据,建立我国手足口病月报告重症患者数的ARIMA模型,并与2010-2014年数据建立的模型进行比较。2010-2014、2010-2015年两个不同时间序列建立的我国手足口病月报告重症患者数模型分别为ARIMA(1,1,0)(2,1,0)12、ARIMA(0,1,1)(2,1,0)12。以上两个不同时间序列预测结果比较发现,数据积累较多,预测的平均相对误差变小,但预测时间越短尚未发现平均相对误差较小。同一研究内容,时间序列年代不同,所建立的预测模型可能不同;认为ARIMA模型数据积累越多、预测时间越短、预测误差越小的情况还需得到进一步验证。     

基于太白山南坡巴山冷杉NPP动态变化的时间序列模型预测效果对比

基于收集整理的太白山地区1959-2016年58年间的气象数据及太白山巴山冷杉林(Abies fargesii Franch.forest)的生理参数数据,运用Biome-BGC模型模拟计算并对输出数据进行提取分析,得到太白山南坡巴山冷杉林的多年净初级生产力(NPP)。然后分别利用自回归求和移动平均模型(ARIMA)、R语言、NAR动态神经网络模型对太白山南坡巴山冷杉林NPP的动态变化进行趋势拟合和短期预测,建立适用于太白山南坡巴山冷杉林NPP的时间序列模型,并应用白噪声检验等相关检验方法对3种模型的预测效果进行评价。结果显示:太白山南坡巴山冷杉林NPP在短期内(2017-2026年)仍保持着波动上升的趋势,可能出现1959年以来的最高值;在对巴山冷杉林未来变化的预测过程中,3种预测模型各有特点,ARIMA模型对太白山南坡巴山冷杉林NPP的预测结果通过了白噪声检验,并给出了在不同置信区间下的可能结果; NAR动态神经网络模型的拟合效果较好,也通过了误差自相关性检验,预测结果较好地模拟了太白山南坡巴山冷杉林NPP在未来一段时期内的变化趋势; R语言在剔除异常数据点后能够运用基础数据较好地对太白山南坡巴山冷杉林NPP动态变化进行模拟,表明预测结果与验证结果相关性达到0.944,误差项的P值远低于0.01。本研究表明3种方法构建的模型在数据拟合中均呈现出较好的效果,预测结果均在可信范围内,在实际预测工作中可根据数据特点选用不同方法。     

石灰岩山地淡竹林演替序列的群落物种多度分布格局

利用断棍模型(BSM)、生态位优先占领模型(NPM)、优势优先模型(DPM)、随机分配模型(RAM)和生态位重叠模型(ONM),对石灰岩山地淡竹林演替序列3类群落15个样地的种 多度关系进行拟合,并利用卡方(x²)和赤池信息量准则(AIC)检验.结果表明: 淡竹纯林、竹阔混交林和阔叶林最优物种多度分布格局模型分别为：DPM(x²=35.86,AIC=-69.77)、NPM(x²=1.60,AIC=-94.68)和NPM(x²=0.35,AIC=-364.61);BSM对混交林和阔叶林的拟合效果较好,对淡竹纯林的拟合欠佳;RAM和ONM对3类群落的拟合均不能接受;在淡竹纯林向阔叶林演替过程中,物种数逐渐增加,多度分布均匀,物种多度分布格局由DPM向NPM转变.由生境过滤作用主导转换成种间竞争作用主导是淡竹林演替序列物种多度格局变化的主要原因.采用多种模型和检验方法综合分析群落演替内、外因素变化,将有助于深入理解群落演替的生态过程.     

增广Kalman滤波器在维生素C二步发酵中的应用

本文在维生素C二步发酵动力学模型研究的基础上,引进增广Kalman滤波器理论,将数学模型、发酵系统和实际操作等因素引起的偏差归为白噪声序列,用于发酵状态及模型参数的滤波处理。结果表明：滤波估计比模型计算的拟合精度大为提高。通过对模型参数的分析,加深了对该系统动力学特性的认识,为维生素c二步发酵过程的状态估计、状态预测及在线辨识奠定了理论基础。     

吉林蛟河42 hm2针阔混交林样地植物种-面积关系

 种-面积关系是生态学中的基本问题, 其构建方式对种-面积关系的影响以及最优种-面积模型的选择仍然存在争议。该文利用吉林蛟河42 hm²针阔混交林样地数据, 分别采用巢式样方法和随机样方法建立对数模型、幂函数模型和逻辑斯蒂克模型, 并通过赤池信息量准则(AIC)检验种-面积模型优度。结果表明, 种-面积关系受到取样方法的影响, 随机样方法的拟合效果优于巢式样方法。采用随机样方法构建的幂指数模型(AIC = 89.11)和逻辑斯蒂克模型(AIC = 71.21)优于对数模型(AIC = 113.81)。根据AIC值可知, 随机样方法构建的逻辑斯蒂克模型是拟合42 hm²针阔混交林样地种-面积关系的最优模型。该研究表明: 在分析种-面积关系时不仅应考虑尺度效应, 还需注意生境变化及群落演替的影响。     

基于时间序列的云南省乙类传染病分析预测

采用自回归移动平均模型(ARIMA)对云南省乙类传染病的月发病率进行预测研究,为传染病的防控提供参考依据。收集2005～2016年云南省年度人口数据和乙类传染病月发病率数据,针对2005～2016年云南省年度人口数据建立GM(1,1)预测模型;根据2005～2016年云南省乙类传染病月发病率数据建立ARIMA预测模型。云南省乙类传染病的发病率有很强的周期性和季节性,可通过决定系数R~2、赤池信息准则(AIC)和施瓦茨准则(SC)选择出最优的乘积季节模型ARIMA(0,1,1)×(2,1,0)_(12)来预测云南省乙类传染病的月发病率。通过对比2017年1月到10月传染病发病率的真实值和预测值,得到误差的平均值为0.8,相对误差的平均值为3.56%,说明预测效果比较满意。通过F检验和t检验显示预测值和真实值无显著性差异,说明ARIMA乘积季节模型可以较好的预测云南省乙类传染病。     

麦田天敌消长演替规律及超长期预测的研究

通过聚类分析,在明确麦田天敌消长演变规律的基础上,采用时间序列分析法,建立麦田总体天敌和优势天敌七星瓢虫的超长期预测模型,经过2001年和2002年实际应用,季节水平模型、ARIMA模型的预测效果较好,并用这两种模型对未来三年麦田天敌的消长进行了超长期预测。     

自回归移动平均模型在医疗服务需求预测中的应用

目的 拟合医疗服务需求时间序列资料的预测模型。方法 采用自回归移动平均模型对出院人次进行模型拟合。结果 模型拟合得到的最优模型为一阶自回归移动平均模型,模型预测2020年某市三甲医院的出院总人次将为93.88万人次。结论 自回归移动平均模型适用于出院总人次时间序列模型拟合,预测结果显示,在没有外来干预因素影响的情况下,三甲医院出院总人次将会延续2009年以前的上升趋势继续上涨。     

水晶梨病虫害防治预测模型

[目的]为提高水晶梨病虫害防治工作效率,进一步提升病虫害的预测效果和精度。[方法]深入研究了灰色模型(GM),利用GM对水晶梨环境因子数据进行建模得到病虫害预测公式,通过差分方程推导出时间响应式和参数估计,建立了优化初始值的灰色模型(OIVGM),将OIVGM与BP神经网络预测模型(BP)进行组合,建立了优化初始值的灰色BP神经网络预测组合模型(OIVGM-BP)。[结果]通过单位根检验法测量模型的稳定性,OIVGM-BP一阶差分处理后,T统计量(-5.487654)小于5%临界值(-2.878073),数据序列表明平稳,OIVGM-BP可以稳定进行预测。通过白噪声检验方法测量OIVGM-BP的适应性,OIVGM-BP的残差P值从第二阶开始,均大于0.05,说明OIVGM-BP的适应性较好,各阶均通过了白噪声检验。LRM、GM、TSM、BP、OIVGM-BP对梨锈病、白粉病、腐烂病、梨黄粉蚜、梨二叉蚜、梨木虱6种病虫害的预测准确率的平均值分别为70.81%、70.09%、69.74%、65.64%、83.01%,OIVGM-BP的预测准确率优于其他4种预测模型。[结论]OIVGM-BP能够对水晶梨病虫害进行有效预测,能够更好地指导农业生产。     

An evaluation of prior influence on the predictive ability of Bayesian model averaging

Model averaging is gaining popularity among ecologists for making inference and predictions. Methods for combining models include Bayesian model averaging (BMA) and Akaike’s Information Criterion (AIC) model averaging. BMA can be implemented with different prior model weights, including the Kullback–Leibler prior associated with AIC model averaging, but it is unclear how the prior model weight affects model results in a predictive context. Here, we implemented BMA using the Bayesian Information Criterion (BIC) approximation to Bayes factors for building predictive models of bird abundance and occurrence in the Chihuahuan Desert of New Mexico. We examined how model predictive ability differed across four prior model weights, and how averaged coefficient estimates, standard errors and coefficients’ posterior probabilities varied for 16 bird species. We also compared the predictive ability of BMA models to a best single-model approach. Overall, Occam’s prior of parsimony provided the best predictive models. In general, the Kullback–Leibler prior, however, favored complex models of lower predictive ability. BMA performed better than a best single-model approach independently of the prior model weight for 6 out of 16 species. For 6 other species, the choice of the prior model weight affected whether BMA was better than the best single-model approach. Our results demonstrate that parsimonious priors may be favorable over priors that favor complexity for making predictions. The approach we present has direct applications in ecology for better predicting patterns of species’ abundance and occurrence.     

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks

Background

Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power.

Results

We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt.

Conclusions

Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.     

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei,China

Background

A prediction model for tuberculosis incidence is needed in China which may be used as a decision-supportive tool for planning health interventions and allocating health resources.

Methods

The autoregressive integrated moving average (ARIMA) model was first constructed with the data of tuberculosis report rate in Hubei Province from Jan 2004 to Dec 2011.The data from Jan 2012 to Jun 2012 were used to validate the model. Then the generalized regression neural network (GRNN)-ARIMA combination model was established based on the constructed ARIMA model. Finally, the fitting and prediction accuracy of the two models was evaluated.

Results

A total of 465,960 cases were reported between Jan 2004 and Dec 2011 in Hubei Province. The report rate of tuberculosis was highest in 2005 (119.932 per 100,000 population) and lowest in 2010 (84.724 per 100,000 population). The time series of tuberculosis report rate show a gradual secular decline and a striking seasonal variation. The ARIMA (2, 1, 0) × (0, 1, 1)₁₂ model was selected from several plausible ARIMA models. The residual mean square error of the GRNN-ARIMA model and ARIMA model were 0.4467 and 0.6521 in training part, and 0.0958 and 0.1133 in validation part, respectively. The mean absolute error and mean absolute percentage error of the hybrid model were also less than the ARIMA model.

Discussion and Conclusions

The gradual decline in tuberculosis report rate may be attributed to the effect of intensive measures on tuberculosis. The striking seasonal variation may have resulted from several factors. We suppose that a delay in the surveillance system may also have contributed to the variation. According to the fitting and prediction accuracy, the hybrid model outperforms the traditional ARIMA model, which may facilitate the allocation of health resources in China.     

Modeling to Predict Cases of Hantavirus Pulmonary Syndrome in Chile

Background

Hantavirus pulmonary syndrome (HPS) is a life threatening disease transmitted by the rodent Oligoryzomys longicaudatus in Chile. Hantavirus outbreaks are typically small and geographically confined. Several studies have estimated risk based on spatial and temporal distribution of cases in relation to climate and environmental variables, but few have considered climatological modeling of HPS incidence for monitoring and forecasting purposes.

Methodology

Monthly counts of confirmed HPS cases were obtained from the Chilean Ministry of Health for 2001–2012. There were an estimated 667 confirmed HPS cases. The data suggested a seasonal trend, which appeared to correlate with changes in climatological variables such as temperature, precipitation, and humidity. We considered several Auto Regressive Integrated Moving Average (ARIMA) time-series models and regression models with ARIMA errors with one or a combination of these climate variables as covariates. We adopted an information-theoretic approach to model ranking and selection. Data from 2001–2009 were used in fitting and data from January 2010 to December 2012 were used for one-step-ahead predictions.

Results

We focused on six models. In a baseline model, future HPS cases were forecasted from previous incidence; the other models included climate variables as covariates. The baseline model had a Corrected Akaike Information Criterion (AICc) of 444.98, and the top ranked model, which included precipitation, had an AICc of 437.62. Although the AICc of the top ranked model only provided a 1.65% improvement to the baseline AICc, the empirical support was 39 times stronger relative to the baseline model.

Conclusions

Instead of choosing a single model, we present a set of candidate models that can be used in modeling and forecasting confirmed HPS cases in Chile. The models can be improved by using data at the regional level and easily extended to other countries with seasonal incidence of HPS.     

ARIMA与SVM组合模型在害虫预测中的应用

害虫发生是一种复杂、 动态时间序列数据, 单一预测模型都是基于线性或非线性数据, 不能同时捕捉害虫发生的线性和非线性规律, 很难达到理想的预测精度。本研究首先采用差分自回归移动平均模型对昆虫发生时间序列进行线性建模, 然后采用支持向量机对非线性部分进行建模, 最后得到两种模型的组合预测结果。将组合模型应用到松毛虫Dendrolimus punctatus发生面积的预测, 实验结果表明组合模型的预测精度明显优于单一模型, 发挥了两种模型各自的优势。组合模型是一种切实可行的害虫预测预报方法。     

Ecological footprint simulation and prediction by ARIMA model—A case study in Henan Province of China

Ecological footprint (EF), as one of the sustainable development indicators, has received considerable attention. However, it has mostly been used as a static indicator. The accurate quantitative analysis on its development trend is still rare. Thus, the autoregressive integrated moving average (ARIMA) model was introduced to enhance the forecasting capacity of EF indicator. Taking Henan Province of China as a study area, we firstly computed the EF and the ecological carrying capacity (EC) in 1949–2006. Based on the computed results, the simulating process of the ARIMA model and the fitting and forecasting results were explained in detail. The final results demonstrated that ARIMA model could be used effectively in the simulation and prediction of EF and the predicted EF could help the decision-makers make a package of better planning for regional ecological balance or sustainable future.     

应用ARIMA模型预测宝安区某街道其它感染性腹泻发病率的探讨

目的：探讨应用ARIMA模型预测宝安区某街道其它感染性腹泻发病率的可行性。方法：应用SPSS13．0软件对2005年～2009年宝安区某街道其它感染性腹泻逐月发病率进行ARIMA模型建模拟合,用所得到的模型对2010年各月发病率进行预测,并评价其预测效果。结果：宝安区某街道其它感染性腹泻发病率每年11月为发病高峰,ARIMA（0,1,1）（0,1,0）12模型是其拟合的最佳模型,其预测结果和实际值绝对误差的绝对值最大为930．47,最小为1．96,平均值214．83,平均相对误差百分比39．04％。结论：模型虽然起到一定的预测效果,但预测精度仍存在误差,可通过积累新的周期数据对ARIMA模型进行修正和重新拟合,也可尝试新的预测方法或其他模型,才能加强和保证预测的精度。     

Time series analysis of the epidemiological transition in Minorca, 1634-1997

Autoregressive integrated moving average (ARIMA) models provide a powerful tool for detecting seasonal patterns in mortality statistics. The strength of ARIMA models lies in their ability to reveal complex structures of temporal interdependence in time series. Moreover, changes in model parameters provide an empirical basis for detecting secular trends and death seasonality patterns. This approach is illustrated by our analysis of changes in the mortality patterns of the population of the town of Es Mercadal on the island of Minorca between 1634 and 1997. These data reveal a transition from an early mortality pattern requiring a complex ARIMA model that accounts for a strong seasonal death pattern and periodic epidemic-related mortality crises to a much simpler 20th-century pattern that can be described by a simple single-parameter ARIMA model. These same data were also analyzed using standard seasonality tests. The results show that the reduction in the number of parameters required to fit the Es Mercadal mortality data coincides with the epidemiological transition in which the predominant causes of morbidly and mortality shift from infectious to degenerative causes.     

Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang,China

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)₁₂ model and the combined ARIMA (1, 1, 2) (1, 1, 1)₁₂-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)₁₂-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.     

Traditional medicines as a mechanism for driving research innovation in Africa

Background

Malaria remains a serious problem in French Guiana, which is at potential risk for drought linked with the El Niño Event and where there could be a risk of malaria epidemic after the onset of an El Niño event.

Methods

A time series analysis using ARIMA was developed to investigate temporal correlations between the monthly Plasmodium falciparum case numbers and El Niño Southern Oscillation (ENSO) as measured by the Southern Oscillation Index (SOI) at the Cayenne General Hospital between 1996 and 2009.

Results

The data showed a positive influence of El Niño at a lag of three months on P. falciparum cases (p < 0.001). The incorporation of SOI data in the ARIMA model reduced the AIC by 4%.

Conclusions

Although there is a statistical link, the predictive value of ENSO to modulate prevention intervention seems marginal in French Guiana. However, additional work should refine the regional dependence of malaria on the ENSO state.