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Abstract:
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In today’s digital age, people leave traces about nearly every aspect of their lives on the Internet. Such “big data” from Internet search engines offer great potential for real-time tracking of public health and social events, including influenza (flu) epidemics. Accurate, high-resolution tracking of flu activities at the regional level helps public health agencies make informed and proactive decisions. However, due to the complex data structure and reduced quality of localized Internet data, few established methods provide satisfactory performance. Our newly proposed method, ARGO2 (2-step Augmented Regression with GOogle data), efficiently combines publicly available Google search data with traditional flu surveillance data from the US Centers for Disease Control and Prevention (CDC); it effectively incorporates cross-regional, inter-resolution (national and regional) dependencies in flu activities. ARGO2 gives leading performance in flu tracking across all US regions compared with currently available, Internet-data-based methods. It also has the potential and flexibility to further include information from additional Internet sources.
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