Dórea F C, Widgren S, Lindberg A
Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), SE-751 89 Uppsala, Sweden.
Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), SE-751 89 Uppsala, Sweden.
Prev Vet Med. 2015 Nov 1;122(1-2):21-32. doi: 10.1016/j.prevetmed.2015.10.002. Epub 2015 Oct 9.
A recent inventory showed that several countries are exploring varied types of animal health sources for the development of veterinary syndromic surveillance (VSS), however, few systems have documented success after the phase of development or exploratory analysis of data. We suggest there are three main challenges in the current development of animal syndromic surveillance: (i) the lack of standards in disease recording and classification; (ii) the development of statistical methods appropriate to deal with animal data; and (iii) the creation of ready-to-use tools that employ these statistical methods. We address the latter two challenges and present an R package - vetsyn - which covers the steps of VSS implementation from classified data to interface. Detailed tutorials are included with the package. The goal is to provide ready-to-use codes to automatize the process of converting pre-classified animal health data into epidemiological information. The package functions are illustrated using real data and simulated outbreaks. Functions to monitor data daily and weekly are available. The main innovation offered by the package is ability to manage data streams, analyses, alarms and user interface in a continuous flow. We expect that this will facilitate the implementation of syndromic surveillance systems by veterinary epidemiologists and surveillance practitioners.
最近的一项清查显示，有几个国家正在探索多种动物健康数据源，以用于发展兽医症候群监测（VSS），然而，在数据的开发或探索性分析阶段之后，很少有系统记录下成功案例。我们认为，当前动物症候群监测的发展存在三个主要挑战：（i）疾病记录和分类缺乏标准；（ii）开发适用于处理动物数据的统计方法；（iii）创建使用这些统计方法的即用型工具。我们应对后两个挑战，并展示一个R包——vetsyn，它涵盖了从分类数据到界面的VSS实施步骤。该包包含详细的教程。目标是提供即用型代码，以自动化将预先分类的动物健康数据转换为流行病学信息的过程。使用实际数据和模拟疫情说明了该包的功能。提供了每日和每周监测数据的功能。该包的主要创新之处在于能够以连续流的方式管理数据流、分析、警报和用户界面。我们预计，这将促进兽医流行病学家和监测从业人员实施症候群监测系统。
