Abstract
Inferring Global-Scale Temporal Latent Topics From News Reports to Predict Public Health Interventions for COVID-19
Highlights
It has long been understood that organized community efforts are required to control the spread of infectious diseases [1]
EpiTopics (Fig. 1), a machine learning framework for surveillance of non-pharmacological interventions (NPI) used to control the COVID-19 pandemic
We sought to learn a set of unbiased latent topic distributions by mining a large corpus of news articles about COVID-19, but without NPI labels
Summary
It has long been understood that organized community efforts are required to control the spread of infectious diseases [1] These efforts, called public health interventions, include social or non-pharmaceutical measures to limit the mobility and contacts of citizens and pharmaceutical interventions to prevent and limit the severity of infections. At the outset of the COVID-19 pandemic, recognizing the absence of systems for recording NPI, multiple groups initiated projects to track the use of interventions for COVID-19 around the world. These projects have relied on the manual efforts of volunteers to review digital documents accessible online with minimal coordination across projects [3]. This approach to monitoring interventions is difficult to sustain and scale to other infectious diseases, and it has produced information about NPI that is of variable reliability and quality [4]
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