As new events become the focal point of news, so does the focus of many researchers. Ranging from automating detection to novel ways of predicting potential outbreak scenarios, these are some trending data science research papers on COVID-19, aka Coronavirus.
Rapidly developed AI-based automated CT image analysis tools can achieve high accuracy in the detection of Coronavirus positive patients as well as quantification of disease burden.
Is mass quarantine and isolation effective as a social tool in addition to its scientific use as a medical tool? In an effort to address this question, using an epidemiological model-driven approach augmented by machine learning, these researchers show that the quarantine and isolation measures implemented in Wuhan brought down the effective reproduction number
This study presents early phase detection of Coronavirus by machine learning methods, implemented on abdominal Computed Tomography (CT) images
This paper introduces COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest radiography images that is open source and available to the general public.
In this paper, these researchers built a publicly available COVID-CT dataset, containing 275 CT scans that are positive for COVID-19, to foster the research and development of deep learning methods which predict whether a person is affected with COVID-19 by analyzing his/her CTs.
The recent worldwide outbreak of the novel coronavirus (COVID-19) opened up new challenges to the research community. AI-driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. This research proposes a shallow Long short-term memory (LSTM)-based neural network to predict the risk category of a country. The researchers use a Bayesian optimization framework to optimize and automatically design country-specific networks.
Detection devices are generally expensive and difficult to obtain, therefore this paper introduces a new framework that is proposed to detect coronavirus disease COVID-19 using onboard smartphone sensors. The proposal provides a low-cost solution, since most of the radiologists have already held smartphones for different daily-purposes.
The use of infectious disease modeling to support public health decision making, referred to in this report as “outbreak science,” has increased in prominence in the past decade. The purpose of this report is to characterize the origin and implications of the disconnect between modelers and public health decision-makers and to develop a plan for the expansion of outbreak science as a capability to support public health.
This is only the tip of the iceberg for data science research on Coronavirus, and there’s plenty more out there. What would you add to this list? Are you working on anything interesting that you think we should know about? Email us firstname.lastname@example.org with your academic papers!
Want to learn more about these types of research papers from the experts themselves? Attend the ODSC East 2020 Virtual Conference and watch the COVID Track where experts will be discussing their latest projects into the pandemic. Some talks include:
CEDAR: Information Technology to Enhance Open Science in the Fight Against COVID-19, Mark Musen, PhD, Professor | Director. Stanford University | Stanford Center for Biomedical Informatics Research
In collaboration with the GO-FAIR International Support and Coordination Office, CEDAR is participating in the Virus Outbreak Data Network to develop robust approaches to the sharing of scientific datasets related to COVID-19.
COVID-19: Unprecedented Challenges and Opportunities for Data Science (and Scientists) — Voices, Visions, and Ventures form Harvard Data Science Review, Xiao-Li Meng, PhD, Professor | Founding Editor-in-Chief, Harvard University | Harvard Data Science Review
Coronavirus After the Curve, Roger W. Thomas, Senior Director, Growth & Strategy for Manufacturing & Diversified Industries, Oracle
While today’s focus is rightfully spent on flattening the curve, those in good health must start to ask what happens after the curve. This question is one for which data science can offer a unique perspective. In this talk, Roger will relate his Do’s & Don’ts experience on manufacturing analytics to reflect on the role data scientists can play in shaping the economic recovery so desperately needed.