Ad-Hoc Monitoring of COVID-19 Global Research Trends for Well-Informed Policy Making

• Published in: ACM transactions on intelligent systems and technology 2023-02, Vol.14 (2), p.1-28, Article 26
• Main Authors: Sarkar, Souvika, Bijoy, Biddut Sarker, Saba, Syeda Jannatus, Feng, Dongji, Mahajan, Yash, Amin, Mohammad Ruhul, Islam, Sheikh Rabiul, Karmaker (“Santu”), Shubhra Kanti
• Format: Article
• Language: English
• Subjects: Applied computing; Computing methodologies; Document searching; Information extraction; Information systems; Users and interactive retrieval
• ISSN: 2157-6904; 2157-6912
• DOI: 10.1145/3576901

The COVID-19 pandemic has affected millions of people worldwide with severe health, economic, social, and political implications. Healthcare Policy Makers (HPMs) and medical experts are at the core of responding to this continuously evolving pandemic situation and are working hard to contain the spread and severity of this relatively unknown virus. Biomedical researchers are continually discovering new information about this virus and communicating the findings through scientific articles. As such, it is crucial for HPMs and funding agencies to monitor the COVID-19 research trend globally on a regular basis. However, given the influx of biomedical research articles, monitoring COVID-19 research trends has become more challenging than ever, especially when HPMs want on-demand guided search techniques with a set of topics of interest in mind. Unfortunately, existing topic trend modeling techniques are unable to serve this purpose as (1) traditional topic models are unsupervised, and (2) HPMs in different regions may have different topics of interest that they want to track.    To address this problem, we introduce a novel computational task in this article called Ad-Hoc Topic Tracking, which is essentially a combination of zero-shot topic categorization and the spatio-temporal analysis task. We then propose multiple zero-shot classification methods to solve this task by building on state-of-the-art language understanding techniques. Next, we picked the best-performing method based on its accuracy on a separate validation dataset and then applied it to a corpus of recent biomedical research articles to track COVID-19 research endeavors across the globe using a spatio-temporal analysis. A demo website has also been developed for HPMs to create custom spatio-temporal visualizations of COVID-19 research trends. The research outcomes demonstrate that the proposed zero-shot classification methods can potentially facilitate further research on this important subject matter. At the same time, the spatio-temporal visualization tool will greatly assist HPMs and funding agencies in making well-informed policy decisions for advancing scientific research efforts.