Spreco Armin, Eriksson Olle, Dahlström Örjan, Cowling Benjamin John, Timpka Toomas
Faculty of Health Sciences, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Department of Computer and Information Science, Linköping University, Linköping, Sweden.
J Med Internet Res. 2017 Jun 15;19(6):e211. doi: 10.2196/jmir.7101.
Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic "big data" from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics.
The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county.
An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source.
The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000).
Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified.
流感是一种病毒性呼吸道疾病，能够引发疫情，对全球社区构成威胁。在医疗保健和公共卫生环境中，来自诊断和预诊断来源的电子“大数据”迅速增加，这使得新一代用于本地检测和预测冬季流感季节及流感大流行的方法得以发展。
本研究的目的是提出一种利用电子可用监测数据在本地环境中综合检测和预测流感病毒活动的方法，并通过对瑞典一个县的真实数据进行回顾性应用来评估其性能。
基于适用于本地监测的流感检测和预测方法的设计原理，正式定义了一种综合检测和预测方法。该新方法被回顾性应用于瑞典一个县（人口445,000）2008 - 2009年冬季流感季节的数据。结果数据代表了来自电子健康数据存储库中符合流感临床病例定义（基于国际疾病分类第10版[ICD - 10]编码）的个体。来自该县远程护理服务热线的信息被用作症状数据来源。
这种新的综合检测和预测方法基于非机械统计模型，旨在集成到本地健康信息系统中。该方法分为用于检测和预测本地流感病毒活动的单独模块。检测模块的功能是提醒当地医疗保健机构即将面临流感病例增加的时期（使用流感诊断数据），而预测模块的功能是预测活动高峰的时间（使用症状数据）及其强度（使用流感诊断数据）。对于检测建模，基于冬季流感季节开始时感染个体呈指数增长的假设，使用指数回归。对于预测建模，在当时的7天周期内应用线性回归以找到高峰时间，而使用正态分布密度函数的导数来找到高峰强度。我们发现综合检测和预测方法在2008 - 2009年冬季流感季节开始当天就检测到了（最佳及时性为0天），而预测的高峰估计比实际高峰提前7天出现，并且预测的高峰强度估计比实际强度低26%（每100,000人中有6.3例流感诊断病例，而实际为8.5例）。
我们的检测和预测方法是专门为本地应用于可电子获取用于监测的流感数据而设计的首批综合方法之一。该方法在回顾性研究中的性能表明对其进行进一步的前瞻性评估是合理的。
