验证使用谷歌趋势来加强加利福尼亚州百日咳监测。
Validating the Use of Google Trends to Enhance Pertussis Surveillance in California.
作者信息
Pollett Simon, Wood Nicholas, Boscardin W John, Bengtsson Henrik, Schwarcz Sandra, Harriman Kathleen, Winter Kathleen, Rutherford George
机构信息
Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, NSW, Australia; Department of Epidemiology & Biostatistics, University of California at San Francisco, California, USA.
National Centre for Immunisation Research and Surveillance of Vaccine Preventable Diseases, The Children's Hospital at Westmead, Sydney, NSW, Australia; Discipline of Paediatrics and Child Health, University of Sydney, NSW, Australia.
出版信息
PLoS Curr. 2015 Oct 19;7:ecurrents.outbreaks.7119696b3e7523faa4543faac87c56c2. doi: 10.1371/currents.outbreaks.7119696b3e7523faa4543faac87c56c2.
INTRODUCTION AND METHODS
Pertussis has recently re-emerged in the United States. Timely surveillance is vital to estimate the burden of this disease accurately and to guide public health response. However, the surveillance of pertussis is limited by delays in reporting, consolidation and dissemination of data to relevant stakeholders. We fit and assessed a real-time predictive Google model for pertussis in California using weekly incidence data from 2009-2014.
RESULTS AND DISCUSSION
The linear model was moderately accurate (r = 0.88). Our findings cautiously offer a complementary, real-time signal to enhance pertussis surveillance in California and help to further define the limitations and potential of Google-based epidemic prediction in the rapidly evolving field of digital disease detection.
引言与方法
百日咳最近在美国再度出现。及时监测对于准确估计该疾病的负担以及指导公共卫生应对至关重要。然而，百日咳监测受到数据报告、整合及向相关利益攸关方传播方面的延迟限制。我们利用2009 - 2014年的每周发病率数据，对加利福尼亚州的百日咳构建并评估了一个实时预测谷歌模型。
结果与讨论
线性模型具有一定准确性（r = 0.88）。我们的研究结果谨慎地提供了一个补充性的实时信号，以加强加利福尼亚州的百日咳监测，并有助于进一步明确在快速发展的数字疾病检测领域基于谷歌的疫情预测的局限性和潜力。
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