Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, P.O. Box 3021, Morogoro, Tanzania.
SACIDS Foundation for One Health, Sokoine University of Agriculture, P.O. Box 3297, Morogoro, Tanzania.
BMC Vet Res. 2021 Mar 6;17(1):109. doi: 10.1186/s12917-021-02789-x.
Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. In Tanzania, the animal health surveillance system has been relying on a few data sources, which suffer from delays in reporting, underreporting, and high cost of data collection and transmission. The integration of data from multiple sources can enhance early detection and response to animal diseases and facilitate the early control of outbreaks. This study aimed to identify and assess existing and potential data sources for the animal health surveillance system in Tanzania and how they can be better used for early warning surveillance. The study used a mixed-method design to identify and assess data sources. Data were collected through document reviews, internet search, cross-sectional survey, key informant interviews, site visits, and non-participant observation. The assessment was done using pre-defined criteria.
A total of 13 data sources were identified and assessed. Most surveillance data came from livestock farmers, slaughter facilities, and livestock markets; while animal dip sites were the least used sources. Commercial farms and veterinary shops, electronic surveillance tools like AfyaData and Event Mobile Application (EMA-i) and information systems such as the Tanzania National Livestock Identification and Traceability System (TANLITS) and Agricultural Routine Data System (ARDS) show potential to generate relevant data for the national animal health surveillance system. The common variables found across most sources were: the name of the place (12/13), animal type/species (12/13), syndromes (10/13) and number of affected animals (8/13). The majority of the sources had good surveillance data contents and were accessible with medium to maximum spatial coverage. However, there was significant variation in terms of data frequency, accuracy and cost. There were limited integration and coordination of data flow from the identified sources with minimum to non-existing automated data entry and transmission.
The study demonstrated how the available data sources have great potential for early warning surveillance in Tanzania. Both existing and potential data sources had complementary strengths and weaknesses; a multi-source surveillance system would be best placed to harness these different strengths.
有效的动物健康监测系统需要可靠、高质量和及时的数据来进行决策。在坦桑尼亚，动物健康监测系统一直依赖于少数几个数据源，这些数据源存在报告延迟、漏报和数据收集与传输成本高的问题。整合来自多个来源的数据可以提高对动物疾病的早期检测和反应能力，并有助于早期控制疫情爆发。本研究旨在确定和评估坦桑尼亚动物健康监测系统现有的和潜在的数据来源，并探讨如何更好地利用这些数据来源进行早期预警监测。本研究采用混合方法设计来确定和评估数据来源。通过文件审查、互联网搜索、横断面调查、关键知情人访谈、现场访问和非参与式观察收集数据。使用预先定义的标准进行评估。
共确定并评估了 13 个数据来源。大多数监测数据来自于牲畜养殖户、屠宰场和牲畜市场；而动物浸泡点是使用最少的数据源。商业农场和兽医店、电子监测工具（如 AfyaData 和事件移动应用程序（EMA-i））以及信息系统（如坦桑尼亚国家牲畜识别和可追溯系统（TANLITS）和农业常规数据系统（ARDS））显示出有潜力为国家动物健康监测系统生成相关数据。在大多数来源中发现的常见变量包括：地点名称（13/13）、动物类型/物种（13/13）、综合征（10/13）和受影响动物数量（8/13）。大多数来源的监测数据内容都很好，并且具有中等至最大的空间覆盖范围。然而，在数据频率、准确性和成本方面存在显著差异。从已确定的来源中，数据的整合和协调非常有限，数据录入和传输几乎没有自动化。
本研究表明，现有的数据来源在坦桑尼亚具有很大的早期预警监测潜力。现有和潜在的数据来源都有各自的优势和劣势；多源监测系统最适合利用这些不同的优势。
