论文标题
连续人工预测作为综合症监测技术
Continuous Artificial Prediction Markets as a Syndromic Surveillance Technique
论文作者
论文摘要
综合症监测系统的主要目标是使用可用的数据源对社会爆发的早期爆发。在本文中，我们讨论了综合症监测系统的挑战以及如何有效地应用于综合症监测问题的连续人工预测市场[Jahedpari等，2017]。 我们使用两个众所周知的Google流感趋势模型，以及（ii）Google流感趋势模型的最新改进，称为GP [Lampos等，2015]，作为我们的案例研究，我们展示了C-APM如何改善其性能。我们的结果表明，C-APM通常比Google流感趋势的MAE较低。尽管这种差异在2004年和2007年（如2004年和2007年）相对较小，但在大多数年份，在2011年至2013年之间的差异相对较大。
The main goal of syndromic surveillance systems is early detection of an outbreak in a society using available data sources. In this paper, we discuss what are the challenges of syndromic surveillance systems and how continuous Artificial Prediction Market [Jahedpari et al., 2017] can effectively be applied to the problem of syndromic surveillance. We use two well-known models of (i) Google Flu Trends, and (ii) the latest improvement of Google Flu Trends model, named as GP [Lampos et al., 2015], as our case study and we show how c-APM can improve upon their performance. Our results demonstrate that c-APM typically has a lower MAE to that of Google Flu Trends in each year. Though this difference is relatively small in some years like 2004 and 2007, it is relatively large in most years and very large between 2011 and 2013.
