Safarishahrbijari Anahita, Osgood Nathaniel D
University of Saskatchewan, Saskatoon, SK, Canada.
JMIR Public Health Surveill. 2019 May 26;5(2):e11615. doi: 10.2196/11615.
Although dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources.
This study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts.
We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation.
Our results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods.
The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets.
尽管动态模型越来越多地被决策者用作洞察疾病流行情况的来源，以指导干预措施来控制传染病爆发，但由于未能及时跟上新出现的流行病学证据，这类模型长期以来一直面临迅速过时的风险。将统计滤波算法应用于高速数据流，最近已证明能有效地让此类模型根据每组新传入的观测数据自动重新校准。新一代地理空间特定的高速数据源的出现，包括相关搜索和社交媒体帖子的每日计数，增强了这类技术的吸引力。此类电子数据源中的信息补充了传统流行病学数据源的信息。
本研究旨在评估通过扩展利用每日搜索计数的方法，将基于机器学习重新校准的大流行预测模型应用于日常临床数据时，预测准确性能提高到何种程度。
我们将先前发表的甲型H1N1流感大流行预测模型与粒子滤波的序贯蒙特卡罗技术相结合，通过使用确诊病例数和搜索量来重新校准模型。通过预测性和数据集特定的交叉验证，使用范数差异度量来评估粒子滤波的有效性。
我们的结果表明，尽管每日搜索量数据存在质量限制，但将此类数据纳入滤波方法可显著提高动态模型的预测准确性。
利用易于获取、公开可用的高速数据源，可显著提高动态模型的预测准确性。这项工作突出了一条低成本、低负担的途径，可利用低成本的公共电子数据集加强基于模型的疫情干预应对规划。
