Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis

作者：Fred Lu, Suqin Hou, Kristin Baltrusaitis, Manan Shah, Jure Leskovec, Rok Sosič, Jared B. Hawkins, John S. Brownstein, Giuseppe Conidi, Julia E. Gunn, Josh Gray, Anna Zink, Mauricio Santillana · 发表于：JMIR Public Health and Surveillance · 年份：2018 · DOI：10.2196/publichealth.8950 · 被引用次数：107 · 研究领域：Data-Driven Disease Surveillance、COVID-19 epidemiological studies、Influenza Virus Research Studies

BACKGROUND: Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. OBJECTIVE: Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care-based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. METHODS: We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenz...