Ongoing Influenza Activity Inference with Real-time Digital Surveillance Data
نویسنده
چکیده
Background. In order to track influenza activity, flu-like patient records collected from health care providers are combined to generate an estimate every week during the flu season. However, this estimate is problematic in terms of timeliness and granularity. A promising alternative is to infer flu activity with fine-grained, timely digital surveillance data. Fitting these extra measurements of flu activities will ideally generate real-time estimate of disease spread, at least one week ahead of official report.
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