*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (9/26) at 12 noon*
*Lunch provided --- All are welcome *
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Dear workshop community,
We will convene for the Applied Statistics Workshop (Gov 3009) tomorrow on
Wednesday (9/26).
The speaker is* Shihao Yang* (Harvard Statistics), who will be presenting
his paper "Big data, Google, and infectious disease prediction: A
statistical perspective".
*Where:* CGIS Knafel Building, Room K354 (see this link
<https://map.harvard.edu/?bld=04471&level=9> for directions).
*When: *Wednesday, September 26th at 12 noon - 1:30 pm.
*Abstract:* Big data generated from the internet have great potential in
tracking and predicting massive social activities, in particular infectious
diseases, whose accurate real-time prediction could help public health
officials make timely decisions to save lives. We introduce a model ARGO
(AutoRegression with GOogle search data / AutoRegression with General
Online data) that has successfully utilized publicly available Google
search data, with/without cloud-based electronic health records, to
estimate current and near-future influenza-like illness activity level
and/or dengue fever activity level for United States and five other
countries around the globe. Our regularized multivariate regression model
dynamically selects the most appropriate variables for prediction every
week, and significantly outperforms all previous internet-based tracking
models, including Google Flu Trends and Google Dengue Trends. We further
extend the model to multiple geographical resolution, tracking infectious
disease not only at national level but also at regional level, with
spatial-temporal information pooling, making it flexible, self-correcting,
robust and scalable.
*All are welcome! Lunch is provided! *
Best,
Connor Jerzak
Applied Statistics Workshop -- Graduate Student Coordinator
An anonymous feedback form for the workshop can be found here at this link
<https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-CU8HWrQPpe9pjTw/viewform?usp=sf_link>.
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