Call for Papers
Special Issue on Novel Informatics Approaches to COVID-19 Research
Due date for submissions: October 15, 2020
The outbreak of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-Co-V2) started in December 2019 and it was declared a pandemic by the World Health Organization (WHO) on March 11th 2020 . As of May 27th, over 5 million cases and 355,000 deaths have been reported worldwide . In addition to the human health burden, the COVID-19 pandemic has disrupted the global economy and daily life on an unprecedented scale.
Researchers worldwide have acted quickly to combat the pandemic of COVID-19, working from different perspectives such as omics, imaging, clinical, and population health research, to understand the etiology and to identify effective treatment and prevention strategies. Informatics methods and tools have played an important role in research about the COVID-19 pandemic. For example, using virus genomes collected across the world, researchers were able to reconstruct the early evolutionary paths of COVID-19 by genetic network analysis, providing insights to virus transmission patterns . In a clinical context, researchers have developed novel approaches to predict infection with SARS-Cov-2 accurately using lung CT scans and other clinical data . At a population scale, researchers have used Bayesian methods to integrate continental-scale data on mobility and mortality to infer the time-varying reproductive rate and the true number of people infected .
This Special Issue aims to highlight the development of novel informatics approaches to collect, integrate, harmonize, and analyze all types of data relevant to COVID-19 in order to accelerate knowledge acquisition and scientific discoveries in COVID-19 research, thus informing better decision making in clinical practice and health policies.
Investigators are encouraged to submit clear and detailed descriptions of their novel methodological results. Topics of interest include, but are not limited to, the following:
COVID-19 related data processing technologies such as data collection and normalization.
Novel visualization technologies for COVID-19 outbreak .
Data integration (e.g., linking different types of data) and data sharing (e.g., FAIR principles) for COVID-19 related datasets
Bioinformatics approaches for sequence analysis of COVID-19, or any omics analysis
Literature mining to better understand COVID-19 characteristics (e.g., risk factors, incubation time, susceptibility, etc.)
Computational approaches for drug and vaccine development for COVID-19
Imaging informatics approaches (e.g., chest X-ray and CT) for diagnoses or prognoses of COVID-19
Clinical informatics for observational studies using electronic health records, e.g., to support treatment and outcome studies 
Deep learning for predictive modeling of outcomes such as death, ICU admission, intubation, etc.
Deep phenotyping of COVID-19
Epidemic monitoring and prediction of transmission by linking multiple sources of data and application of statistical and machine learning methods
Mobile technologies for monitoring and intervention of COVID-19 transmission
Informatics approaches to address disparities, fairness and ethical issues of COVID-19 related research
Methods for determining fake news related to COVID-19 and their influence on public opinion and health policy
Methods for comparing different predictive models for COVID-19 transmission and evaluating their correctness 
Impact of cultural differences, government policies, geography, genetics, living conditions on human behavior and disease transmission patterns
Informatics strategies to support public health control activities such as contact tracing and epidemic detection.
Global tracking of COVID-19 control measures and impacts through the application of machine learning and other methods to online media and social media.
Peer Review Process
All submitted papers must be original and will go through a rigorous peer-review process with at least two reviewers. Papers previously published in conference proceedings will not be considered. JBI’s editorial policy will be strictly followed by special issue reviewers. Note in particular that JBI emphasizes the publication of papers that introduce innovative and generalizable methods of interest to the informatics community. Specific applications can be described to motivate the methodology being introduced, but papers that focus solely on a specific application are not suitable for JBI.
Authors must submit their papers via the online Elsevier Editorial System (EES) at http://ees.elsevier.com/jbi by October 15, 2020. Authors should select “Informatics in COVID-19 Research” as their submission category and note in a cover letter that their submission is for the “Special Issue on Novel Informatics Approaches to COVID-19 Research.” If the manuscript is not intended as an original research paper, the cover letter should also specify if it is, rather, a Methodological Review, Commentary, or Special Communication. Authors should make sure to place their work in the context of human-focused biomedical research or health care, and to review carefully the relevant literature.
JBI’s editorial policy, and the types of articles that the journal publishes, are outlined under Aims and Scope on the journal home page at https://www.journals.elsevier.com/journal-of-biomedical-informatics (click on “Read more” for full details). All submissions should follow the guidelines for authors at https://www.elsevier.com/journals/journal-ofbiomedical- informatics/1532-0464/guide-for-authors, including format and manuscript structure. If the authors speak a first language other than English, editorial assistance by a native English speaker is highly recommended prior to submission. Open-source software code and data should ideally be made available through Internet resources that are enduring. JBI is an international journal and generalizable contributions from throughout the world are highly encouraged.
Authors will have the opportunity to select whether their accepted paper will be published in JBI or in JBI-X – the new open-access mirror journal of JBI (https://www.sciencedirect.com/journal/journal-of-biomedical-informatics-x). For this Special Issue, the publication fee of $2300 will be waived, if authors of accepted articles select to publish in JBI-X.
JBI and JBI-X recognize that authors want their accepted papers published as soon as possible. Therefore it is JBI and JBI-X policy to publish accepted special issue papers in a regular issue of the journal upon acceptance. The full special issue is then compiled when the last paper has been accepted (see the collection of virtual special issues at https://www.sciencedirect.com/journal/journal-of-biomedical-informatics/specialissues).
Special issues contain a mix of papers from JBI and JBI-X, depending on the publication preference of the authors.
Questions Regarding the Special Issue
Please direct any questions regarding the special issue to Dr. Hua Xu ([email protected]).
1 Situation Summary | CDC. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/summary.html (accessed March 29, 2020).
2 Johns Hopkins Coronavirus Resource Center. https://coronavirus.jhu.edu/map.html (accessed May 27, 2020).
3. Forster, Peter, et al., Phylogenetic network analysis of SARS-CoV-2 genomes. Proceedings of the National Academy of Sciences, 2020.
4. Mei, Xueyan, Hao-Chih Lee, Kai-yue Diao, Mingqian Huang, Bin Lin, Chenyu Liu, Zongyu Xie et al. “Artificial intelligence–enabled rapid diagnosis of patients with COVID-19.” Nature Medicine (2020): 1-5.
5. Report 23 – State-level tracking of COVID-19 in the United States.
https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-23-united-states/ (accessed May 29, 2020).
6. Carroll, Lauren N., et al. “Visualization and analytics tools for infectious disease epidemiology: a systematic review.” Journal of biomedical informatics 51 (2014): 287-298
7. Cooper, Gregory F., et al. “A method for detecting and characterizing outbreaks of infectious disease from clinical reports.” Journal of biomedical informatics 53 (2015): 15-26
8. Chen, Yirong, et al. “The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison.” Journal of biomedical informatics 81 (2018): 16-30.
School of Biomedical Informatics
The University of Texas Health Science Center at Houston
Houston, TX USA
Email: [email protected]
Department of Epidemiology, Biostatistics and Occupational Health
Montreal, Quebec, Canada
Email: [email protected]
Department of Population Health Sciences
New York, NY USA
Email: [email protected]
According to JBI, one reason for returning papers without review is that a paper does not deal with the core informatics notions of information and knowledge management. We are looking for novel imaging informatics methods focusing on information/knowledge processing and management. Papers that focus on new numerical methods only (e.g., segmentation algorithms) or papers that apply standard methods (e.g., CNN) to COVID-19 tasks are out of the scope of this special issue.