Data-Driven Artificial Intelligence approaches to Combat COVID-19

  in Special Issue   Posted on June 3, 2020

Information for the Special Issue

Submission Deadline: Wed 15 Jul 2020
Journal Impact Factor : 3.441
Journal Name : Cognitive Computation
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/12559/updates/17958332
Journal & Submission Website: https://www.springer.com/journal/12559

Special Issue Call for Papers:

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 19th April 2020, there have been 2,406,905 confirmed cases worldwide, with 165,058 deaths and another 54,225 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.
  • 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.
  • Data-driven AI prediction and awareness measures – regional and global cases.
  • Data-driven AI identification / tracking of COVID-19 infection transmission and dynamics.
  • Intelligent and/or data-driven approaches for monitoring COVID-19 patients at home.
  • Intelligent and/or data-driven methods to detect COVID-19.
  • Intelligent and/or Data-driven approaches for effective delivery of treatment.
  • Application of deep learning for COVID-19 diagnosis and treatment.
  • Intelligent bioinformatics approaches towards drug design for COVID-19.Intelligent hospital management for healthcare professionals during COVID-19.
  • Intelligent health informatics for tackling COVID19.
  • Data-driven AI methodologies for future pandemic prediction/prevention.

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
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.

Other Special Issues on this journal

Closed Special Issues

3.441

BICS 2020

Cognitive Computation
Mon 31 Aug 2020