论文标题
综合监测准确性的荟萃分析
Meta-Analysis of the Accuracy of Syndromic Surveillance
论文作者
论文摘要
我们介绍了过去十年来，用于数字疾病监视研究的共同进化学习网络的首次荟萃分析。在此过程中，我们显示了与数字疾病监视有关的学术研究中发生的共同进化和动态变化，以提高准确性，方法和结果。使用动态网络分析，我们能够显示基于社交媒体的分析和算法的结合，这些分析和算法随后被其他研究人员作为共同进化学习网络改进。这从本质上说明了我们如何通过反馈循环来改善我们的研究并提高准确性，以纠正开放系统的行为，并使用数字疾病监测中的10年科学研究推断学习模式，可靠性和有效性。
We present the first meta-analysis of co-evolutionary learning networks for digital disease surveillance research over last 10 years. In doing so, we show the co-evolution and dynamical changes that occurred in academic research related to digital disease surveillance for improving accuracy, approach and results. Using dynamic network analysis, we are able to show the incorporation of social media-based analytics and algorithms which have been proposed and later improved by other researchers as co-evolutionary learning networks. This essentially demonstrates how we improve our research and increase accuracy through feedback loop for correcting the behaviour of an open system and perhaps infer learning patterns, reliability and validity using 10 years scientific research in digital disease surveillance.
