BACKGROUND Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks. METHODS A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation. RESULTS For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques. CONCLUSIONS The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

中文翻译：

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对公共卫生信息学空间决策支持系统的系统评价，支持识别人畜共患疾病暴发的高风险区域。

背景技术人畜共患疾病占传染病暴发的很大一部分，并且对维持监测和预防措施的公共卫生规划造成负担。在相互关联的全球社区中，利用新的建模方法和数据源已变得必要。为了促进数据收集、分析和决策，过去十年报告的空间决策支持系统的数量有所增加。本系统综述旨在描述为协助公共卫生官员管理人畜共患疾病暴发而开发的空间决策支持系统的特征。方法 对 Google Scholar 数据库进行系统检索，查找 2008 年至 2018 年间发表的文章，没有语言限制。使用预定义的包含和排除标准，使用布尔逻辑和关键字搜索词对标题和摘要进行手动搜索。数据提取包括空间数据库管理、可视化和报告生成等项目。结果 对于本次审查，我们筛选了 34 篇全文文章。对设计和报告质量进行了评估，最终形成了 12 篇文章，对拟议的干预措施进行了评估，并描述了识别特征。尽管表明了建模技术的多样化利用，但多源数据集成和以用户为中心的设计的应用并不一致。结论 描述了针对人畜共患疾病暴发的最新 SDSS 设计中所采用的特征、数据源、开发和建模技术。在设计过程中，要有效利用新兴数据源和建模方法的价值，仍然存在许多挑战需要解决。未来，开发应遵循功能和系统开发的可比较标准，例如系统需求的用户输入，以及用于可视化不同规模存在的数据的灵活界面。PROSPERO 注册号：CRD42018110466。