The Corona Virus Disease 2019 (COVID-19) outbreak in 2019 has created a global health crisis of unforeseeable proportions. The World Health Organization (WHO)'s report issued on February 2, 2020, described the pandemic as an “information outbreak” (infodemic). All forms of information related to the outbreak brought about an information tsunami to the public, leaving the masses in complete bewilderment and panic. Therefore, out of this mass confusion come significances to study information acquisition, dissemination and sharing, early warning systems and methods, risk assessment, behavior patterns, and mental health during the global pandemic. Furthermore, constructing an emergency information management system for epidemic prevention and control will offer greater support for responding to such catastrophic public emergencies.
In the face of the pandemic and the ensuing health crisis, the following issues deserve our in-depth consideration and attention.
The first issue is the accurate collection and analysis of epidemic-related data. During the epidemic, epidemiological data are an extremely important source of information and reference for decision-making processes. The renowned online scientific publication Digital Trends published an article titled “The Most Reliable Coronavirus Dashboards” on March 28, 2020, which referenced six visual epidemic data notification platforms, including Johns Hopkins University's Dashboard, BBC News’ “Visual Guide to the Pandemic,” The New York Times’ Coronavirus Map, Centers for Disease Control (CDC) and Prevention's Dashboard, Microsoft's Bing COVID-19 Tracker, and the World Health Organization's Dashboard. Among them, the system of Johns Hopkins University received the highest evaluation. Since January 22, Johns Hopkins University has launched the “Coronavirus Visualization Map” and related web pages and APPs. The data come from WHO, CDC, China Center for Disease Control and Prevention (CCDC), China Emergency Response Office for National Health Commission (NHC), China's authoritative popular science website “DXY,” etc., which provide up-to-the-minute real-time data around the world. In addition to the real-time updates, this system also uses a random collection of population epidemiological simulation tools to simulate the epidemic dynamics. The border prevention mechanisms are also considered in the modeling process, which allows for higher accuracy and minimization of the margin of error.
In an online interview with “Ted Connects” on the global epidemic, Bill Gates stated that the data recorded on-a-daily-basis allowed everyone to closely monitor the epidemic trends, while also noting that this phenomenal ability of data visualization and sharing experience is a great advantage that was not available during the 1918 pandemic. Powerful and accurate multisource data collection, analysis, and prediction capabilities have undoubtedly become an important support for the world in fighting epidemics and health crises. The following types of data shall also be considered for this system: epidemic dynamics, health information, regional population flow, data from health and medical institutions, social media, and administrative mapping. Concurrently, the cooperation of data research institutions, science and technology companies, government departments, and other relevant organizations is also mandated.
Second, the issue of effective screening of erroneous/false information. Today, with the popularity of the Internet and social media, information can be widespread at an extremely rapid rate of speed, and incorrect messages and false information are flourishing. During epidemic and health crises, it becomes increasingly important to identify and distinguish these erroneous and false information because people's vital health and the stability of social order are closely related. In addition to the establishment of an automatic monitoring and identification system to control and prevent the spread of erroneous/false information on network media, information management scholars have also proposed targeted measures to mitigate the dissemination of false information. For example, authorization verification for information sources, evaluation of the accuracy of information content, close monitoring of the particularity of health/medical information, avoidance of sharing unverified information, and the provision of relevant practical tools. In conjunction, it is necessary to strengthen information literacy education and enhance the health information literacy of both individuals and groups in order to improve upon the public's ability to identify erroneous and false information.
A third issue is user information behavior during public health emergencies. Different from normal daily conditions, user information behaviors under public health emergencies and health crises often exhibit some “unconventional” characteristics. This scenario provides a unique perspective for the research of user information behavior and it demands a set of questions. During the epidemic, how do people implement diverse tools to obtain multisource information and data through numerous channels? Does the spread of information amongst people have a stronger “infectiousness” during this time? What is the difference between information behavior in these specific situations and past normalization behavior? What impacts do the “fear of missing out” and information avoidance have on people? The effects on people's behavior, psychology, emotions, and even the whole of society reflected by the information and data flows are all issues that are worth exploring.
The fourth and final issue is the open sharing of health research data. Open science and open research both advocate that research and scientific communication systems should serve the public interest, which becomes especially urgent under the current global health crisis. The “elite-style” orientation that is common in journals, languages, databases, etc. has become the current barrier to obtain research results related to the epidemic. To solve this problem, academic publishers such as Elsevier, Springer Nature, and Taylor & Francis, and some academic institutions and groups signed a joint statement on “sharing research data and findings relevant to the novel coronavirus (nCoV) outbreak,” promising to make all of their publications related to COVID-19 and coronavirus accessible and reusable. Their statement also assures support for major public health interests by ensuring wide and rapid data sharing. Moreover, the alliance of publishers has taken active measures to make the COVID-19 research content in their databases accessible free of charge. The Dimensions Platform of Digital Science integrates the resources of major publishing institutions and launches the integration platform of linked data and resources to aid researchers. In addition to these measures, how to rebuild an operationally enhanced scientific system is also a question worth taking into consideration. A sound system will allow researchers and the public more transparent and faster access to research results, which will better serve the public interest.
Data and Information Management (DIM) emphasizes the study of data and information-driven management that follows the theory, principles, doctrines, and methods of information science. DIM uses both contemporary information technology and big data technology to reveal the mechanisms and guidelines for data and information acquisition, dissemination, organization, sharing, and utilization, thus offering a reliable basis for decision-making. In the face of such major public emergencies as the new coronavirus, there are urgent issues to be studied, such as how to integrate multisource heterogeneous information rapidly and comprehensively, ameliorating social-risk perception and advance warning; how to manage and analyze emergency information to provide support for government decision-making; and how to conduct epidemic public-opinion analysis and false information identification to guide public behavior. As an academic journal in the field of data science and information management, it is of great significance to organize scholars to conduct prospective, basic, retrospective, and empirical joint research on the above issues, to contribute their professional knowledge and intelligence in the fight against the epidemic. In response to the nCoV epidemic, the journal collects manuscripts from experts in the field of data science and information management to conduct in-depth discussions on these issues, providing insights and policy advice for major epidemic prevention and control. For this reason, DIM has created a special issue that encompasses the topic of the epidemic to be released in the third issue of this year.
We are very grateful to the seven groups of experts invited to contribute to our journal. In this special publication, they expound on issues such as “Building an Open Resources Repository for COVID-19 Research,” “Implications of Knowledge Organization Systems for Health Information Exchange and Communication during the COVID-19 Pandemic,” “Beyond Information Organization and Evaluation: How Can Information Scientists Contribute to Independent Thinking?” “Translation and Expansion: Enabling Laypeople Access to the COVID-19 Academic Collection,” “Twelve Agendas on Interacting with Information: A Human-Engaged Computing Perspective,” “How American Academic Medical/Health Sciences Libraries Responded to the COVID-19 Health Crisis: An Observational Study,” and “Investigation into Information Release of Chinese Government and Departments on COVID-19.” These issues are raised in the perspective of information/data management and thoroughly cover a great extent of interdisciplinary research. What we are discussing here is not just about academic issues, but rather a rethinking of the mission of disciplines and journals, and of concern for human society as a whole.