Spatial and temporal aberration detection methods for disease outbreaks in syndromic surveillance systems

Early surveillance of notifiable infectious diseases is a key element for their control by public health agencies. The goal of syndromic disease surveillance is to identify emerging infectious risks to public health in real or near real time as a method of early detection, trend monitoring, and false-alarm avoidance. This article reviews temporal, spatial, and spatial–temporal aberration detection techniques that can be used to facilitate the early detection of infectious disease outbreaks that can occur in nonrandom yet clustered distributions in geographic information systems (GIS)-based syndromic surveillance systems. The focus is on the approaches appropriate for prospective surveillance data. In addition, this article discusses the impact of data privacy, security, and data quality on detection algorithms and explores what the future GIS-based syndromic surveillance systems may hold.

对法定传染病进行早期监测是公共卫生机构控制传染病的关键要素。症状监测的目的是实时或近乎实时地识别对公共卫生构成的新出现的传染风险，作为一种早期发现、趋势监测和避免误报的方法。本文综述了时间、空间以及时空异常检测技术，这些技术可用于促进在基于地理信息系统（GIS）的症状监测系统中对可能以非随机但成簇分布形式出现的传染病暴发进行早期检测。重点是适用于前瞻性监测数据的方法。此外，本文还讨论了数据隐私、安全和数据质量对检测算法的影响，并探讨了未来基于GIS的症状监测系统可能的发展方向。