We are living in unprecedented times and as the coronavirus pandemic deepens and more restrictions are put in place around the world, the impact of the Covid-19 crisis on our lives can be troubling and confusing. With the continuous attempts of scientific research institutions, advanced information fusion technologies have been actively leveraged to comprehensively harness the multi-source epidemical information from medical devices, biomedical sensors, mobile terminals, social networks, etc., improving the efficiency for epidemic monitoring, virus tracking, prevention, control and treatment, and resource allocation.
Although the information fusion technologies have unique advantages and can play an important role in responding to epidemic diseases, there are three main challenges to develop strategies in practice: i) gaps exist between researchers in different areas like computer science, bioinformatics, epidemiology and molecular modeling make it difficult to cognize the problem in depth from multi-source information; ii) epidemiological information is vast so that we need considerable and effective approaches to harness it; iii) there is still a lack of practical approaches, algorithms and tools for information fusion to fight the virus and save lives.
This special issue aims to explore recent advances and disseminate state-of-the-art research on multi-source information fusion for epidemic monitoring, virus tracking, prevention, control and treatment, and resource allocation. Original, unpublished technical papers with novel and important contributions will be considered for the special issue; submitted papers must not be published, accepted or under review by another journal, and extended version of a conference paper must be so indicated and the extension must include a substantial improvement to the technical content of the paper.
Some of the most important areas include, but are not limited to:
Multi-Source information fusion in public health management
Multi-Source information fusion for monitoring and predicting the spread of epidemic diseases
Multi-Source information fusion for tracking infections and epidemical/medical analysis
Multi-Source information fusion for rapid detection and diagnosis
Medical knowledge graph construction assisted by multi-Source information fusion
Information-fusion-aided drug discovery, treatment, prevention, and resource allocation
Multi-Source information fusion for measuring the damage of the epidemic disease in terms of social behavior, industrial practices, and environmental impact
Privacy and security in healthcare and medical information fusion
Multi-Communication technologies to support Multi-Source information fusion to handle epidemic disease and people tracking
Please prepare your paper along with all the supplementary materials for your submission. The papers submitted to this special issue must be original. Besides that, they must not be published, “under review”, or even be submitted in any other journal, conference, or workshop. Papers will be peer-reviewed by at least three independent reviewers and will be chosen based on contributions including their originality, scientific quality as well as their suitability to this special issue. The journal editors will make the final decision on which papers will be accepted.
Authors must ensure that you carefully read the guide for authors before submitting your papers. The guide for authors and link for online submission is available on the Information Fusion homepage at: https://www.journals.elsevier.com/information-fusion. Please select “SI: MSIFED” when you reach the “Article Type” step when submitting your papers. For any inquiry or question regarding this special issue, authors may contact directly via email to Yin Zhang at firstname.lastname@example.org.
Yin Zhang, University of Electronic Science and Technology of China, China
Email: email@example.com, firstname.lastname@example.org
Ala Al-Fuqaha, Western Michigan University, US
Iztok Humar, University of Ljubljana, Slovenia
Pasquale Pace, University of Calabria, Italy
Deadline for Submission: September 1, 2020