Coronavirus Data Science Research Papers to Read Right Now Coronavirus Data Science Research Papers to Read Right Now
As new events become the focal point of news, so does the focus of many researchers. Ranging from automating detection to... Coronavirus Data Science Research Papers to Read Right Now

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. 

Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

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.

Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China

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

Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods

This study presents early phase detection of Coronavirus by machine learning methods, implemented on abdominal Computed Tomography (CT) images

Large-Scale Screening of COVID-19 from Community-Acquired Pneumonia using Infection Size-Aware Classification

By using computer vision and pattern recognition, these researchers have developed a process of detecting COVID-19 that could realistically be used by clinics for important decision-making.

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography 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.

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

 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.

Neural network-based country-wise risk prediction of COVID-19

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. 

A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study

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. 

Modernizing and Expanding Outbreak Science to Support Better Decision Making During Public Health Crises: Lessons for COVID-19 and Beyond

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  info@odsc.com 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-19Mark 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.



ODSC gathers the attendees, presenters, and companies that are shaping the present and future of data science and AI. ODSC hosts one of the largest gatherings of professional data scientists with major conferences in USA, Europe, and Asia.