Covid 19
COVID-19 research continues to explore the pandemic's multifaceted impact, focusing on accurate prediction of disease severity and mortality, effective diagnosis and treatment strategies, and understanding the spread of misinformation. Current research leverages machine learning, particularly deep learning models like convolutional neural networks and large language models, to analyze diverse data sources including chest X-rays, blood test parameters, and social media posts. These efforts aim to improve clinical decision-making, enhance public health interventions, and ultimately mitigate the long-term consequences of the pandemic.
Papers
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting
Soumyanil Banerjee, Ming Dong, Weisong Shi
The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic
Christian Morgenstern, Daniel J. Laydon, Charles Whittaker, Swapnil Mishra, David Haw, Samir Bhatt, Neil M. Ferguson
Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19
Marcelo Matheus Gauy, Marcelo Finger
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly Detection
Ali Garjani, Atoosa Malemir Chegini, Mohammadreza Salehi, Alireza Tabibzadeh, Parastoo Yousefi, Mohammad Hossein Razizadeh, Moein Esghaei, Maryam Esghaei, Mohammad Hossein Rohban
A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging
Gargi Desai, Nelly Elsayed, Zag Elsayed, Murat Ozer
Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study
Chad A Melton, Brianna M White, Robert L Davis, Robert A Bednarczyk, Arash Shaban-Nejad