Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing

by   Ashkan Ebadi, et al.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January- May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.


page 8

page 9

page 10

page 12


Identifying Radiological Findings Related to COVID-19 from Medical Literature

Coronavirus disease 2019 (COVID-19) has infected more than one million i...

On the evolution of research in hypersonics: application of natural language processing and machine learning

Research and development in hypersonics have progressed significantly in...

Extracting Major Topics of COVID-19 Related Tweets

With the outbreak of the Covid-19 virus, the activity of users on Twitte...

COVID-19 Pandemic Outbreak in the Subcontinent: A data-driven analysis

Human civilization is experiencing a critical situation that presents it...

Is AI Model Interpretable to Combat with COVID? An Empirical Study on Severity Prediction Task

Black-box nature hinders the deployment of many high-accuracy models in ...

Characterizing the Landscape of COVID-19 Themed Cyberattacks and Defenses

COVID-19 (Coronavirus) hit the global society and economy with a big sur...

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

During 2020, the infection rate of COVID-19 has been investigated by man...

Please sign up or login with your details

Forgot password? Click here to reset