Call for Papers: Data-Driven Artificial Intelligence approaches to Combat COVID-19

SCOPE AND MOTIVATION

COVID-19, the infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, has been recognised as a pandemic by the World Health Organisation. As of 6th May 2020, there have been over 3.8 million confirmed cases worldwide, with over 2,65K deaths and another over 48K critical cases. Almost every country has been affected significantly, and many have been in a state of lockdown to slow the spread of the infection. The scientific community has been working tirelessly to combat the disease and its adverse effects. Global research efforts are being made in a range of priority COVID-19 areas, including:  

  • Reduction of disease transmission.
  • Application of deep learning for COVID-19 diagnosis and treatment.
  • Identification of infection dynamics; pandemic hotspots and a focused population for screening.
  • Supporting diagnosis, treatment, and the recovery process.
  • Enabling appropriate drug and vaccine development processes.

This timely special issue invites research contributions (both original research and comprehensive survey articles) from all related areas, with a focus on data-driven artificial intelligence (AI) approaches and their applications in combatting COVID-19.

TOPICS

The topics of interest include, but are not limited to:

  • AI methods in COVID-19 related data collection, curation, and visualisation.
  • Application of deep learning for COVID-19 diagnosis and treatment.
  • Data-driven AI identification / tracking of COVID-19 infection transmission and dynamics.
  • Data-driven AI methodologies for future pandemic prediction/prevention.
  • Data-driven AI prediction and awareness measures - regional and global cases.
  • Intelligent and/or data-driven approaches for effective delivery of treatment.
  • Intelligent and/or data-driven approaches for monitoring COVID-19 patients at home.
  • Intelligent and/or data-driven methods to detect COVID-19.
  • Intelligent bioinformatics approaches towards drug design for COVID-19.
  • Intelligent health informatics for tackling COVID-19.
  • Intelligent hospital management for healthcare professionals during COVID-19.

DEADLINES

Submissions Deadline

15 July 2020

First notification of acceptance

31 August 2020

Submission of revised papers

30 September 2020

Final notification to authors

31 October 2020

Submission of final/camera-ready papers

30 November 2020

Publication of special issue (provisional)*

Dec 2020/Jan 2021

  *All accepted papers will appear immediately in the journal’s online topical collection


ORGANIZERS/GUEST EDITORS

Mufti Mahmud

(co-ordinator)

Nottingham Trent University, UK

muftimahmud@gmail.com

mufti.mahmud@ntu.ac.uk

M Shamim Kaiser

Jahangirnagar University, Bangladesh

mskaiser@juniv.edu  

Nilanjan Dey

Techno India College of Technology, India

nilanjan.dey@tict.edu.in

Newton Howard

University of Oxford, UK; MIT, USA

nhmit@mit.edu

Aziz Sheikh

University of Edinburgh, UK

aziz.sheikh@ed.ac.uk

Amir Hussain

Edinburgh Napier University, UK

A.Hussain@napier.ac.uk

Submission Guidelines

  • Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
  • All papers will be reviewed following standard reviewing procedures for the Journal.
  • Papers must be prepared in accordance with the Journal guidelines: www.springer.com/12559
  • Submit manuscripts to: http://COGN.edmgr.com  Select “Data Driven AI approaches to combat COVID-19” when asked if the article is for a special issue.
  • Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including  FAQsTutorials  along with  Help and Support.

Other links include:
-editorial policies  -publication policies  -copyright transfer -self-archiving  -OA funding  -open choice  -funder compliance  -read and publish agreements  -preprint sharing  -my publication process -production  -publication  -post-publication  -ORCID  -Publons  -article sharing  -citation alerts

The outbreak of the novel coronavirus in China (SARS-CoV-2) represents a significant and urgent threat to global health and as such Springer Nature has signed a joint statement committing to ensure that research findings and data relevant to this outbreak are shared rapidly and openly to inform the public health response and help save lives.