[Air-L] Special Issue on Big Data in Epidemics

[Muhammad Imran]

[Apologies for cross-posting]

Special Issue on Big Data in Epidemics
===============================

Journal: Big Data Research (IF: 2.952; CiteScore: 7.43)
Link: https://www.journals.elsevier.com/big-data-research/call-for-papers/special-issue-on-big-data-in-epidemics

Introduction to the special issue
------------------------------------------

On February 11, 2020 the World Health Organization announced the official name for the disease that is causing the 2019 novel coronavirus outbreak, first identified in Wuhan China. More than 110 countries are affected thus far, raising concerns of widespread fear and increasing anxiety in individuals subjected to the threat of the virus.

Humankind has experienced epidemic diseases before, such as the Spanish flu, HIV, and SARS. With the development of science and technology and the continuous attempts of scientific research by universities, university hospitals, and medical research institutions, relevant data on epidemics and viruses accumulates at an increasing rate.

There are three main challenges associated with these rapidly growing and inherently complex and heterogeneous amounts of data:

- Gaps exist between researchers in different fields, and different approaches and methods make it difficult to understand the problem in depth;
- The accumulated data is vast and complex calling for novel methods for extracting valuable insights, removing irrelevant variables, and guiding potential applications in a targeted manner.
- There is still a lack of efficient methods, models, and tools in the field for data utilization and application in practice.

Hence, it is crucial that advances in Big Data research, Deep Learning, Data Analytics, and Data Science find their way to these applications, and that research results can be successfully integrated into drug screening, crowd disease prevention and control, trend prediction, epidemic surveillance and other fields.

In this special issue, we solicit papers that focus on deploying the potential of Big Data Research towards understanding, monitoring and diminishing the impact of disease outbreaks. This includes methods for processing related data in a fast and accurate manner, techniques for identifying useful data trends and epidemics in epidemiological, medical screening, and trend prediction environments, as well as data-driven studies on the implications of epidemic diseases and their countermeasures on real-world and online social behavior, transportation, industrial activity, and climate.

Topics for the special issue
-----------------------------------

Topics of interest include, but are not limited to:

. Big data in trend prediction of epidemic diseases and disease outbreaks.
. Big data in public health management for effective monitoring and control of epidemics and disease outbreaks.
. Geo-Social big data analytics for epidemic transmission routes analysis.
. Data-driven methods for large-scale health surveillance.
. Data-driven methods for the understanding of the mechanisms contributing to a disease outbreak.
. Data-driven methods for better understanding of diseases with outbreak potential.
. Data-driven methods in clinical decision support during disease outbreaks.
. Data-driven drug discovery for epidemic diseases.
. Methods for data-driven analyses of outbreak-related epidemiological, clinical and social data and of combinations thereof.
. Epidemic knowledge graph construction and their applications.
. Data-driven analysis of implications of epidemic diseases and their countermeasures on social behaviour, industrial practices, and environmental impact.
. Tools that support the above functionality using automated processing and machine learning pipelines, or novel visualizations, targeting both expert and lay users.
. Social media for predicting and tracking epidemics, pandemics, and disease outbreaks.
. Novel techniques for gaining situational awareness and extracting actionable information from social media during health emergencies such as COVID-19.
. Use of social media to determine outbreak hotspots and spread.

Paper Submission Format and Guidelines:
------------------------------------------------------
All submitted papers must be clearly written in English and must contain only original work, which has not been published by, or is currently under review for, any other journal, conference, symposium, or workshop. Submissions are expected to not exceed 30 pages (including figures, tables, and references) in the journal's single-column format using 11 point font. Detailed submission guidelines are available under "Guide for Authors" at: http://www.journals.elsevier.com/big-data-research/

All manuscripts and any supplementary material should be submitted through the Elsevier Editorial System (EES). The authors must select " SI: Big Data in Epidemics" as Article Type when they reach the Article Type step in the submission process. The EES website is located at: https://www.editorialmanager.com/BDR/default.aspx.

All papers will be peer-reviewed by at least two independent reviewers. Requests for additional information should be addressed to the guest editors. The decisions for papers submitted before July 15 will be made before September 15.

Important dates:
----------------------
. Paper submission due date: September 15, 2020
. Notifications: Papers will be reviewed as they arrive in the submission system
. Expected publication: Papers will be published electronically as soon as they are accepted. The estimated completion of the Big Data Research special issue is June 2021

The special issue may request documentation related to informed consent, ethics approval and donor organ/tissue source, including approved translations when original documents are in a language other than English. Failure to provide verifiable documentation may result in the withdrawal of a manuscript.

Guest Editors:
------------------
- Myra Spiliopoulou (myra at ovgu.de), Otto-von-Guericke Univ. Magdeburg, Germany
- Panos Papapetrou (panagiotis at dsv.su.se), Stockholm University, Sweden
- Muhammad Imran (mimran at hbku.edu.qa), Qatar Computing Research Institute, Doha, Qatar

Best,
-----
Dr. Muhammad Imran
Scientist
Qatar Computing Research Institute
Hamad Bin Khalifa University
Doha, Qatar.
Tel:  +974 4454 1521
https://mimran.me/ <http://mimran.me/>