Dataset Information

Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

ABSTRACT: The COVID-19 pandemic has highlighted the importance of non-pharmacological interventions (NPIs) for controlling epidemics of emerging infectious diseases. Despite their importance, NPIs have been monitored mainly through the manual efforts of volunteers. This approach hinders measurement of the NPI effectiveness and development of evidence to guide their use to control the global pandemic. We present EpiTopics, a machine learning approach to support automation of NPI prediction and monitoring at both the document level and country level by mining the vast amount of unlabeled news reports on COVID-19. EpiTopics uses a 3-stage, transfer-learning algorithm to classify documents according to NPI categories, relying on topic modeling to support result interpretation. We identified 25 interpretable topics under 4 distinct and coherent COVID-related themes. Importantly, the use of these topics resulted in significant improvements over alternative automated methods in predicting the NPIs in labeled documents and in predicting country-level NPIs for 42 countries. The bigger picture Accurate, scalable detection of the timing of changes to public health interventions for COVID-19 is an important step toward automating evaluation of the effectiveness of interventions. We show that it is possible to train an interpretable deep-learning model called EpiTopics on media news data to predict (1) the interventions mentioned in individual news articles and (2) the temporal change of intervention status at the country level. We addressed a main challenge of label scarcity among the news reports. Using EpiTopics, we modeled the latent semantics from 1.2 million unlabeled news reports on COVID-19 over 42 countries recorded from November 1, 2019 to July 31, 2020, identifying 25 interpretable topics under 4 COVID-related themes. Using the learned topic model, we inferred topic mixture membership for each labeled article, which allowed us to learn an accurate connection between the topics and the public health interventions at both the document level and country level. We developed a machine learning model called EpiTopics toward automatic detection of the changes in the status of non-pharmacological interventions (NPI) for COVID-19 from news reports. EpiTopics learns country-dependent topics from large numbers of COVID-19 news reports that do not have NPI labels. Subsequently, EpiTopics learns accurate connections between these topics and changes in NPI status from a set of labeled news reports, which enables accurate detection of temporal NPI status for each country referred to in the news reports.

SUBMITTER: Wen Z

PROVIDER: S-EPMC8805211 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Dataset Information

Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets