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
Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News
Mohammad Majid Akhtar, Bibhas Sharma, Ishan Karunanayake, Rahat Masood, Muhammad Ikram, Salil S. Kanhere
Taking a Language Detour: How International Migrants Speaking a Minority Language Seek COVID-Related Information in Their Host Countries
Ge Gao, Jian Zheng, Eun Kyoung Choe, Naomi Yamashita
A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19
Haoran Hu, Connor M Kennedy, Panayotis G. Kevrekidis, Hongkun Zhang
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
Rohitash Chandra, Chaarvi Bansal, Mingyue Kang, Tom Blau, Vinti Agarwal, Pranjal Singh, Laurence O. W. Wilson, Seshadri Vasan