EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions
Zhi Wen1, Jingfu Zhang1, Guido Powell2
1School of Computer Science, McGill University, Montreal, QC H3A 0G4, Canada.
Abstract:
Non-pharmacological interventions (NPIs) are important for controlling infectious diseases such as COVID-19, but their implementation is currently monitored in an ad hoc manner. To address this issue, we present a three-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI. In this protocol, we outline the use of transfer-learning to address the limited number of NPI-labeled documents and topic modeling to support interpretation of the results. For complete details on the use and execution of this protocol, please refer to Wen et al. (2022).
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