Integrating Artificial Intelligence, Data Science, Mathematics, Medicine and Public Health, Epidemiology, Neuroscience, and Biomedical Science in Pandemic Management
About this Research Topic
The outbreak of Coronavirus Disease 2019 (COVID-19) has seriously impacted mental and physical health worldwide. New and emerging solutions to global health threats posted by COVID-19 are urgently needed. We now understand that there are considerable variations in disease severity, symptoms, outcomes, and mortality rates with respect to variables such as age, geographical location, underlying medical conditions, sex, and other factors. Accordingly, a large number of scientists and researchers with broad backgrounds, including artificial intelligence, have been recently mobilized to help predict disease course and evolution and devise strategies to restrain and manage COVID-19. This special issue brings together scientists and clinicians in artificial intelligence, mathematics and statistics, neural science, robotics, social sciences, computational biology, medical health care, psychiatry, and psychology, in order to promote COVID-19 research and stimulate collaboration between researchers in these diverse fields. This special issue intends to cover recent research directions and activities dedicated to analysis, diagnosis, treatment, and modeling of mechanisms related to COVID-19.
We welcome submissions of original research papers from basic and clinical sciences broadly related to the application of artificial intelligence, neuroscience, robotics, computational biology, and medical impact on COVID-19. Original research COVID-19-related articles in clinical and public health, case reports, methodology, and modeling as well as reviews are welcomed. Research articles related to experimental studies using state-of-the-art imaging and clinical measurements as well as experimentally based computational and biologically inspired neural networks are particularly appropriate for this issue.
Keywords: COVID-19, Artificial Intelligence, Machine Learning, Deep Learning, Economics, Medicine and Public Health, Genetics, Computational Neuroscience, Computational Biology
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