Medication sales and syndromic surveillance, France

Although syndromic surveillance systems using nonclinical data have been implemented in the United States, the approach has yet to be tested in France. We present the results of the first model based on drug sales that detects the onset of influenza season and forecasts its trend. Using weekly lagged sales of a selected set of medications, we forecast influenzalike illness (ILI) incidence at the national and regional level for 3 epidemic seasons (2000-01, 2001-02, and 2002-03) and validate the model with real-time updating on the fourth (2003-04). For national forecasts 1-3 weeks ahead, the correlation between observed ILI incidence and forecast was 0.85-0.96, an improvement over the current surveillance method in France. Our findings indicate that drug sales are a useful additional tool to syndromic surveillance, a complementary and independent source of information, and a potential improvement for early warning systems for both epidemic and pandemic planning.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

时间序列模型在流感症状监测信息分析中的应用

通过对石家庄市流感流行规律分析[1,2],以实际效果和统计学原理论证了以药房药物销售量为代表的流感症状监测信息能够有效的服务于流感监测,症状监测与传统监测相结合是流感监测体系建设发展的目标[3,4].为了进一步了解流感症状监测信息,对2004年11月至2008年11月的门诊就诊量、药物销售量和学校缺勤率分别构建时间序列模型.     

趋势季节模型在腹泻症候群监测信息分析中的应用

目的 应用趋势季节模型对徐州市2006-2009年哨点医院腹泻症候群监测的数据进行拟合,探索适合社区全科医生对疾病或事件预警的方法.方法 利用时间序列的趋势季节模型对徐州市2006-2009年哨点医院腹泻症候群监测的数据进行建模.计算趋势方程,各季平均季节指数、季节指数的校正系数、各季调整季节指数,拟合2006-2009年各季节哨点医院腹泻症候群监测的数,并将拟合值与实际值比较,检验模型的拟合能力.计算季节预测误差指标,预测2010年全年和各季度检测数.结果 对所分析的徐州市2006-2009年哨点医院腹泻症候群监测的数据资料建立了直线回归模型t＝12 887.79﹢181.79t.各季度平均季节指数分别为98.77%、100.58%、100.43%、100.18%.季节指数的校正系数为1.000 1.各季度调整季节指数分别为98.78%、100.59%、100.44%、100.19%.对2006-2009年腹泻病症候群监测数拟合结果显示,精确度最高的2008年第3季度达99.54%,最差是2006年第1季度,为90.27%,拟合精度在90%以上.用趋势季节模型预测哨点医院2010年腹泻病症候群检测数,预测全年检测65 007例,1～4季度分别检测15 783、16 255、16 414、16 555例.结论 时间序列的趋势季节模型能较好的分析疾病或事件产生同时间的关系,并有较强的预测能力,从而为社区全科医生对疾病或事件产生的预警提供了有效的工具.     

A spatial epidemic model for disease spread over a heterogeneous spatial support

Data from the Iowa mumps epidemic of 2006 were collected on a spatial lattice over a regular temporal interval. Without access to a person‐to‐person contact graph, it is sensible to analyze these data as homogenous within each areal unit and to use the spatial graph to derive a contact structure. The spatio‐temporal partition is fine, and the counts of new infections at each location at each time are sparse. Therefore, we propose a spatial compartmental epidemic model with general latent time distributions (spatial PS SEIR) that is capable of smoothing the contact structure, while accounting for spatial heterogeneity in the mixing process between locations. Because the model is an extension of the PS SEIR model, it simultaneously handles non‐exponentially distributed latent and infectious time distributions. The analysis within focuses on the progression of the disease over both space and time while assessing the impact of a large proportion of the infected people dispersing at the same time because of spring break and the impact of public awareness on the spread of the mumps epidemic. We found that the effect of spring break increased the mixing rate in the population and that the spatial transmission of the disease spreads across multiple conduits. Copyright © 2015 John Wiley & Sons, Ltd.     

Timeliness of syndromic influenza surveillance through work and school absenteeism

In this paper, we investigate the usefulness of work and school absenteeism surveillance as an early warning system for influenza. In particular, time trends in daily absenteeism rates collected during the A(H1N1)2009 pandemic are compared with weekly incidence rates of influenza-like illness (ILI) obtained from the Belgian Sentinel General Practitioner (SGP) network. The results indicate a rise in absenteeism rates prior to the onset of the influenza epidemic, suggesting that absenteeism surveillance is a promising tool for early warning of influenza epidemics. To convincingly conclude on the usefulness of absenteeism data for early warning, additional data covering several influenza seasons is needed.     

Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration

The degeneration of the human brain is a complex process, which often affects certain brain regions due to healthy aging or disease. This degeneration can be evaluated on regions of interest (ROI) in the brain through probabilistic networks and morphological estimates. Current approaches for finding such networks are limited to analyses at discrete neuropsychological stages, which cannot appropriately account for connectivity dynamics over the onset of cognitive deterioration, and morphological changes are seldom unified with connectivity networks, despite known dependencies. To overcome these limitations, a probabilistic wombling model is proposed to simultaneously estimate ROI cortical thickness and covariance networks contingent on rates of change in cognitive decline. This proposed model was applied to analyze longitudinal data from healthy control (HC) and Alzheimer's disease (AD) groups and found connection differences pertaining to regions, which play a crucial role in lasting cognitive impairment, such as the entorhinal area and temporal regions. Moreover, HC cortical thickness estimates were significantly higher than those in the AD group across all ROIs. The analyses presented in this work will help practitioners jointly analyze brain tissue atrophy at the ROI-level conditional on neuropsychological networks, which could potentially allow for more targeted therapeutic interventions.     

Bayesian Markov switching models for the early detection of influenza epidemics

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white-noise process depending on whether the system is in an epidemic or in a non-epidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, the proposal provides the probability of being in an epidemic state at any given moment. In order to validate the methodology, a comparison of its performance with other alternatives has been made using influenza illness data obtained from the Sanitary Sentinel Network of the Comunitat Valenciana, one of the 17 autonomous regions in Spain. Copyright (c) 2008 John Wiley & Sons, Ltd.     

A general goodness-of-fit test for Markov and hidden Markov models

Markov models are a convenient and useful method of estimating transition rates between levels of a categorical response variable, such as a disease stage, which changes over time. In medical applications the response variable is typically observed at irregular intervals. A Pearson-type goodness-of-fit test for such models was proposed by Aguirre-Hernandez and Farewell (Statist. Med. 2002; 21:1899-1911), but this test is not applicable in the common situation where the process includes an absorbing state, such as death, for which the time of entry is known precisely nor when the data include censored state observations. This paper presents a modification to the Pearson-type test to allow for these cases. An extension of the method, to allow for the class of hidden Markov models where the response variable is subject to misclassification error, is given. The method is applied to data on cardiac allograft vasculopathy in post-heart-transplant patients.     

A Bayesian dynamic model for influenza surveillance

The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.     

A modelling approach for correcting reporting delays in disease surveillance data

One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.