Ali M A, Ahsan Z, Amin M, Latif S, Ayyaz A, Ayyaz M N
Centre of System Simulation and Visual Analytics Research, University of Engineering and Technology, GT Road, Lahore, Pakistan.
University Medical and Dental College, Sargodha Road, Faisalabad, Pakistan.
Public Health. 2016 May;134:72-85. doi: 10.1016/j.puhe.2016.01.006. Epub 2016 Feb 13.
Globally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs). However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service-oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future.
This research focuses on development of ID-Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real-time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management.
We have developed a robust service-oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief-complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy-logic to categorize syndromes. Standard routines by Center for Disease Control (CDC), i.e. c1-c3 (c1-mild, c2-medium and c3-ultra), and spatial scan statistics are employed for detection of temporal and spatio-temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio-temporal risk zones.
We analysed performance of surveillance framework using ID data for year 2011-2015. Dynamic syndromic classifier is able to classify chief-complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo-spatiotemporal displays, i.e. heat-maps, and choropleth are shown in respective sections.
The proposed framework will set a standard and provide necessary details for future implementation of such a system for resource-constrained regions. It will improve early outbreak detection attributable to natural and man-made biological threats, monitor spatio-temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation.
在全球范围内，疾病监测系统在传染病（ID）的爆发检测和应对管理中发挥着重要作用。然而，在巴基斯坦这样的发展中国家，由于公共卫生数据匮乏以及缺乏自动化监测系统，疫情爆发难以被检测到。我们的研究旨在为ID监测制定一个面向服务的集成可视化分析架构，确定关键组成部分，并为未来此类系统的轻松重现建立一个基线。
本研究聚焦于ID-Viewer的开发，它是一个用于ID监测的可视化分析决策支持系统。它融合了智能方法，利用来自急诊科（ED）的实时流数据进行早期爆发检测、医疗资源分配和疫情应对管理。
我们为ID监测开发了一个强大的面向服务的可视化分析架构，该架构为ID数据采集、爆发检测和疫情应对管理提供自动化机制。主要症状的分类使用动态分类模块完成，该模块采用神经网络和模糊逻辑对综合征进行分类。疾病控制中心（CDC）的标准程序，即c1 - c3（c1 - 轻度，c2 - 中度和c3 - 重度），以及空间扫描统计分别用于检测时间和时空疾病爆发。即将到来的疾病威胁的预测使用支持向量回归来进行早期预警和应对规划。开发了地理可视化分析显示，允许对症状群进行交互式可视化、监测疾病传播模式以及识别时空风险区域。
我们使用2011 - 2015年的ID数据分析了监测框架的性能。动态症状分类器能够以高分类准确率将主要症状分类到适当的综合征中。爆发检测方法能够在疫情时区开始时检测到ID爆发。预测模型能够提前2０周预测登革热趋势，名义归一化均方根误差为0.29。在相应部分展示了交互式地理时空显示，即热图和分级统计图。
所提出的框架将为资源受限地区未来实施此类系统设定标准并提供必要细节。它将改善对自然和人为生物威胁导致的早期爆发检测，监测时空疫情趋势，并确保疫情已经发生或未发生。先进的分析功能将有助于及时组织/制定健康管理政策、疾病控制活动以及高效的医疗资源分配。
