Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems.
Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems.
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DOI：
10.1016/j.sste.2015.04.003
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发表时间：
2015-04
影响因子：
3.4
通讯作者：
Pfeiffer DU
中科院分区：
文献类型：
--
作者：
Stevens KB;Pfeiffer DU
审查一系列创新的空间健康数据来源。包括数据仓库、移动健康、谷歌地球。包括自愿提供地理信息和挖掘大数据。讨论每个数据源的优点、局限性和应用。突出它们被有效利用的研究。在过去的30多年里，流行病学研究收集疾病数据的地点属性已经变得司空见惯。尽管这种进步在很大程度上是由手持全球定位系统(GPS)以及最近内置GPS的智能手机和平板电脑的引入推动的，但地理参考疾病数据的收集已经超出了使用手持GPS设备的范围，现在存在许多众包地理参考疾病数据的来源，例如从谷歌搜索查询或Twitter消息的地理参考中获得的数据。此外，地图学已经超越了专业人员的领域，进入了众包绘图项目，这些项目在疾病控制和监测疫情(如2014年西非埃博拉疫情)方面发挥着关键作用。本文全面回顾了空间动物和人类健康数据的一系列创新来源，包括数据仓库、mHealth、Google Earth、志愿地理信息以及基于互联网的大数据源(如Google和Twitter)的挖掘。我们讨论了每种方法的优点、局限性和应用，并强调了它们被有效利用的研究。
Review a range of innovative sources of spatial health data. Includes data warehouses, mHealth, Google Earth. Includes volunteered geographic information and mining big data. Discuss advantages, limitations and applications of each data source. Highlight studies where they have been used effectively. During the last 30 years it has become commonplace for epidemiological studies to collect locational attributes of disease data. Although this advancement was driven largely by the introduction of handheld global positioning systems (GPS), and more recently, smartphones and tablets with built-in GPS, the collection of georeferenced disease data has moved beyond the use of handheld GPS devices and there now exist numerous sources of crowdsourced georeferenced disease data such as that available from georeferencing of Google search queries or Twitter messages. In addition, cartography has moved beyond the realm of professionals to crowdsourced mapping projects that play a crucial role in disease control and surveillance of outbreaks such as the 2014 West Africa Ebola epidemic. This paper provides a comprehensive review of a range of innovative sources of spatial animal and human health data including data warehouses, mHealth, Google Earth, volunteered geographic information and mining of internet-based big data sources such as Google and Twitter. We discuss the advantages, limitations and applications of each, and highlight studies where they have been used effectively.