Management and Data Sharing of COVID-19 Pandemic Information

Abstract

The coronavirus disease 2019 (COVID-19) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2. During the past 10 months, COVID-19 has killed over 1 million people worldwide. Under this global crisis, data sharing and management of the COVID-19 information are urgently needed and critical for researchers, epidemiologists, physicians, bioengineers, funding agencies, and governments to work together in developing new vaccines, drugs, methods, therapeutics, and strategies for the prevention and treatment of this deadly and rapidly spreading disease. The COVID-19 pandemic information includes the database of COVID-19-patient biospecimen resources in hospitals or biorepositories, electronic patient health records, ongoing clinical trials and research results on this disease, policies, guidelines, and regulations related to COVID-19, and the COVID-19 outbreak tracking records, and so on. A study of the current management and data-sharing approaches, tools, software, network, and internet systems developed in the United States is conducted in this article. Based on this study, it is revealed that the existing data-sharing and management systems are facing many big challenges and problems associated with data decentralization, inconsistencies, security and legal issues, limited financial support, international communications, standardization, and globalization. To overcome and solve these problems, several integrated platform models for national and international data-sharing and management are developed and proposed in this article to meet the unprecedented need and demand for COVID-19 pandemic information sharing and research worldwide.

Introduction

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China.¹ In the weeks that followed, the virus quickly spread to many other nations. The World Health Organization (WHO) declared the COVID-19 outbreak a Public Emergency of International Concern on January 29, 2020, and a pandemic on March 11.² COVID-19 is a highly contagious and rapidly expanding disease, resulting in high death rates. As of October 17, 2020, more than 38.7 million cases have been confirmed across 235 countries, areas, and territories in the world, causing over 1.095 million deaths. The United States has the highest number of cases followed by India and Brazil.³ Given such extreme circumstances, comprehensive data-sharing and management of the COVID-19 pandemic information are urgently needed and critical for researchers, epidemiologists, physicians, bioengineers, funding agencies, and governments to communicate and work together most effectively in developing new vaccines, drugs, methods, therapeutics, and strategies for the prevention and treatment of the deadly pandemic.^4,5

It is well known that the pandemic information is truly a “big data,” including at least the following: (1) COVID-19 patient specimen resources (including both sample materials and sample-tracking data) in biobanks, hospitals, or repositories; (2) patient electronic health records (EHRs) (including patient informed consent, laboratory diagnosis results, imaging records, medical history, and clinical outcomes); (3) ongoing research results on the coronavirus and the development of new vaccines and drugs in research institutions, biotechnology companies, and pharmaceutical corporations; (4) reports of ongoing clinical trials and treatment methods from medical centers; (5) policies and guidelines on COVID-19 research as well as sample handling and testing, established by leading global public health organizations; (6) laws and regulations for the pandemic prevention and treatment, established by governments; and (7) the COVID-19 outbreak tracking records with precautionary actions updated by local governments and international organizations in a timely fashion.

This review focuses on a study of the current COVID-19 data-sharing and management approaches, tools, software, and internet systems developed and applied in the United States, including Laboratory Information Management Systems (LIMSs), EHR networks, the REDCap software and communication system, Public COVID-19 heat maps, and government or leading health care organizations' internet database systems. In addition, it is imperative to emphasize and promote international data sharing. If executed well, such an approach could have far-reaching impacts on all countries and high at-risk territories in a quicker, safer, and most importantly a standardized way, such as when effective treatments are discovered, the results can be duplicated globally.⁶ Several integrated model platforms for national and international data sharing and management of COVID-19 pandemic information are proposed to advance the data-driven and evidence-based COVID-19 research collaborations for the development of both preventive strategies and therapeutic approaches.

Data-Sharing Networks and Approaches

Laboratory information management system

Generally speaking, a standardized, open, collaborative, and virtual data-sharing system is indispensable for timely and adequate distribution of information to relevant parties in research as well as for exchanging and developing clinical expertise and evidence-based solutions.^5,7 However, the data sharing and management among many institutions and laboratories, in large amounts, can be troublesome, complicated, and challenging to handle. LIMSs were developed to solve these problems and many other potential challenges. Specifically for COVID-19, an LIMS could integrate both instrument-side and software-side for COVID-19 testing-related laboratories, biobanks, research institutions, and hospitals.⁸ LIMS could use a built-in application programming interface (API) to automatically transfer analytical data gathered from COVID-19 specimen test instruments, such as real-time reverse transcription-polymerase chain reaction and next-generation sequencing instruments, which reduces the possibility of transcription errors and improves the data collection efficiency. To integrate the software side, LIMS could utilize their built-in API to connect with third-party software such as electronic medical record (EMR), EHR, laboratory information system (LIS), and patient registries. A study on eight LIMSs is presented in an alphabetical order below, giving an insight into the potential benefits of each LIMS, based on which one may make a more informed decision to select a suitable LIMS for the data sharing, research, and applications.

BaseSpace Clarity LIMS

BaseSpace Clarity LIMS is an LIMS that helps genomic laboratories track samples and manage workflows efficiently. This system does much more than tracking laboratory samples. It supports regulatory compliance yet remains flexible to accommodate new technologies and workflows. It scales and integrates with a laboratory's ecosystem and automates routine tasks. BaseSpace Clarity LIMS can help in many ways and is not limited to the following⁹:

Support compliance with eSignature, audit trails, reagent, and lot tracking, and privacy and security controls.

Help a laboratory to reduce time with straightforward implementation, preset protocols for laboratory preparation kits and instruments, and quality control features that flag poor-quality samples.

Promote accuracy with automation and built-in business logic and error checking.

Manage complexity and change with the ability to create new workflows in the user interface (UI) when needed.

Enable thorough extension and customization of the laboratory environment through the API.

Benchling

Benchling frames itself as the “Life Sciences R&D Cloud,” which gives users all levels of a completely seamless experience, without the need to switch back and forth between two different modules and without the need to continually re-enter data.¹⁰ Benchling combines the simple functionality of custom solutions with the ease of an out-of-the-box solution. Users can create custom dashboards to track R&D data. They can also adjust Benchling without coding experience to adapt to specific situations, and to integrate instruments or other software to automate manual data handling tasks. Overall, Benchling can help in the following ways¹⁰:

Model and interlink many items, from sequences to cell lines, to reagents.

Model any experimental process as digital, step-by-step workflow.

Generate reports based on samples and results that the workflows produce.

Meets R&D needs without any code—an extendable platform with custom software.

REpresentational State Transfer (REST) API¹⁰ and webhooks allow for seamless integration of Benchling with users' instruments and other software.

CloudLIMS

Marketed as a configurable “Software as a Service” (SaaS) tool, CloudLIMS enables laboratories to automate their workflows more efficiently. Not only does it help manage samples through their complete life cycle, but it also has the capabilities installed to facilitate collaborative studies, sample requisition, and inventory management. CloudLIMS offers an effective client portal, enabling users to provide online access of their LIMS to their clients, and giving them control. The study management system allows users to manage information of active, inactive, and completed studies, allowing them to add attachments to each study. CloudLIMS can help the consumers in the following ways¹¹:

Data import/export in multiple standard formats.

Full audit trail.

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) compliance plus group-based access permissions.

Customized report generation.

Client management and test management.

Core LIMS

Core LIMS is designed to meet the consumer needs for collecting, sharing, and analyzing data while maintaining flexibility and adaptability.¹² Core LIMS has prebuilt workflows that are simple and seamless to use, and as the complexity grows, these workflows can be easily adapted to keep pace with laboratory requirements. Core LIMS uses RESTful API and Core Scientific Data Management System (SDMS)¹² software to exchange and load data from external sources and instruments, and is helpful for the following¹²:

Know where everything has been and for how long with sample and container tracking, location audit logs, and so on; manage stock supplies and reagents; assign reorder alerts.

Create and manage laboratory project workflows of samples through assays, standardized methods, and procedures.

Associate any electronic file to any object within the LIMS using links. The Core LIMS is a unified repository for all the documents required to support a laboratory's research from raw data to final study reports.

Provide direct integration with data-generating instruments, custom file parsers and data loaders, and automatic data reduction.

Manage customer specifications and custom recipes—regulatory compliance.

Labgen LIS

Labgen LIS¹³ is fully automated to boast a fully integrated comprehensive LIS. Full automation reduces errors, improves efficiency, and reduces costs. Labgen LIS reduces overhead costs through automation of specimen reports, patient reports, work lists, and data integration. Like all of those above, this LIS is designed to easily interface with laboratory analyzers and other proprietary LIS, allowing easy data sharing and tight security and control combined with their analytical reports, enabling better real-time decision-making. Some other features include¹³:

Fully integrated billing program.

Patient access and control to LIS.

Reporting and business intelligence analytics.

Key performance indicator management.

Laboratory coding and billing.

LabWare LIMS

LabWare LIMS states that the companies who use the LIMS will have their business benefit from capabilities such as browser-based deployment, external-facing web portals, and fully configurable web services while at the same time having access to its laboratory management software functionalities.¹⁴ LabWare LIMS' value proposition becomes more magnified as it offers all LabWare LIMS modules as part of the single product license. The system is scalable and suitable for a laboratory with 10 users as it is for an enterprise with thousands of employees. In summary,¹⁴ this LIMS has the following features:

Built-in instrument integration.

License all-in-one.

Avoidance of platform or brand dependencies.

LIMS UI that can be deployed in zero-footprint browsers.

An extensive library of web services, which allows authoring of the API through powerful SAP¹⁴ and Waters Empower¹⁴ systems.

SLIMS Agilent

SLIMS Agilent is an LIMS and an electronic laboratory notebook (ELN) in a single system.¹⁵ This helps the laboratory workflow by combining these two most needed features. SLIMS is flexible and configurable, and when installed, SLIMS is ready to be adapted to the laboratory. The SLIMS supply contains a comprehensive collection of preconfigured packages ready to install and good to go right out of the box. Customizable, flexible, and easy to integrate are the main points of this system. In short, SLIMS Agilent serves the users well in the following instances:

ELN/LIMS all-in-one.

Track the complete sample life cycle from analysis to results.

Visualize workflows with the Workflow Management module.

SLIMS fully interfaces the instruments and software. It features three APIs (i.e., REST, Java, and Python), which allow communication with third-party systems.

STARLIMS

STARLIMS is a scalable web-based product that allows users to connect from anywhere online. STARLIMS can incorporate ELNs, LIMS, and SDMSs into the complete package application, thereby eliminating the need to build and maintain custom interfaces to third-party tools. STARLIMS is also one of very few systems that have a mobile app ready to use for iOS and Android phones. STARLIMS specializes in the following, as given in Ref.¹⁶:

Managing data by exception rather than reviewing results that are nominal in nature.

Automatically visually flagging and immediately comparing results in real time against defined specifications.

Managing and tracking auditable records.

Performing trend analysis and processing control charts.

Creating protocols, studies, and having control over when and what needs to be pulled and tested.

A comparative summary of the eight LIMSs is presented in Table 1.

Table 1.

A Summary of Eight Laboratory Information Management Systems

LIMS name	Company	Key features	Custom built or OOTB	Service or license fees
BaseSpace Clarity⁹	Illumina, Inc. (https://www.illumina.com/products/by-type/informatics-products/basespace-clarity-lims.html)	Receipt-to-receipt sample tracking and audit readiness.	OOTB	Yes
		Flexibility and scalability, comprehensive application programming interface.
		Preset protocols and out-of-the-box instrument integrations.
Benchling¹⁰	Benchling (https://www.benchling.com/life-sciences-cloud/)	Track every workflow, sample, and result in one place.	OOTB	Yes
		Iterate on processes with codeless configuration.
		Export data for investigational new drug application in days, not months.
CloudLIMS¹¹	CloudLIMS (https://cloudlims.com/specimen-tracker/lab-sample-tracking-software.html)	Customizable sample life cycle/source management.	OOTB	Yes
		Import test results and generate custom reports.
		Client management via a chain of custody.
Core LIMS¹²	Thermo Fisher Scientific (https://www.coreinformatics.com/products/core-lims-laboratory-information-management-system/)	The only LIMS provider on Amazon Web Services Life Sciences Competency Partner Program.	OOTB	Yes
		Manage sample life cycle to maintain high-quality data.
		Flexible ecosystem, adaptable, and grows with company needs.
Labgen LIS¹³	Comtron USA (www.labgenlis.com/)	Fully automated.	OOTB	Yes
		Advanced business intelligence for real-time decision making.
		Reduces operating costs, has a built-in, fully integrated billing program.
LabWare LIMS¹⁴	LabWare (https://www.labware.com/en/p/Products/LIMS-(Laboratory-Information-Management-System))	Boasts big-name clientele: Gilead and NIH.	Custom or OOTB	Yes
		License all-in-one, access to all platforms and products in one.
		Reduced costs and improved efficiency and flexibility for future workflows.
SLIMS Agilent¹⁵	Agilent (https://www.agilent.com/en/products/software-informatics/openlab-software-suite/lab-workflow-management/slims)	Automate external laboratory instruments.	OOTB	Yes
		Optimize sample repository management.
		Organize experiments by project name and create standard operation procedures.
STARLIMS¹⁶	Abbott Laboratories (https://www.informatics.abbott/us/en/offerings/starlims-integrated-solution)	A mobile app designed for ultimate portability and convenience.	OOTB	Yes
		Three platforms in one, cost-efficient and comprehensive.
		Chain of custody support for improving regulatory compliance.

LIMS, Laboratory Information Management System; LIS, Laboratory Information System; OOTB, out-of-the-box.

EHR system and network

EHR systems^16–20 are database and management networks with digital version of patients' personal medical and health records that are used and shared by health care providers to benefit patients and society. While computerization is only a portion of EHR, a fully functional EHR could achieve much more than the traditional paper version of the medical chart. EHR contains rich patient-related information, including a patient's medical history, diagnoses, medications, immunization dates, allergies, radiology images, and laboratory and test results.¹⁷ This information not only enables health care providers to apply an evidence-based and time-saving method to diagnose and better develop tailored treatment plans, but also saves patients' unnecessary expenses on testing.

A National Center for Health Statistics (NCHS) Physician workflow survey¹⁸ reveals the positive impacts of the EHR system as follows:

94% of the survey respondents indicate that their EHR makes records readily available at point of care.

88% report that their EHR produces clinical benefits for the practice.

75% of health care providers report that the EHR allows them to deliver better patient care.

Duffy et al.¹⁹ conducted a study about the effects of electronic prescribing on the clinical practice of a family medicine residency. A patient-oriented survey shows the patients' positive opinions on EHR:

A total of 91.7% of the respondents (patients) rarely or never have had their prescriptions sent to the wrong pharmacy.

76% reported it made obtaining medications easier.

92% were happy that their doctor used e-prescribing.

The Office of the National Coordinator for Health Information Technology (ONC) site is designed as a resource for the entire health system to support the adoption of health information technology and the promotion of nationwide health information exchange (HIE) to improve health care.²⁰ The Health IT playbook developed by ONC contains comprehensive guidelines, including strategies, recommendations, best practices, and certified vendors, to help health care providers implementing and using tools such as the EHR and HIE to “advance healthcare information and delivery.”²¹ During this COVID-19 pandemic, Health IT now uses EHR and HIE tools to collect and report COVID-19 data. With nation-wide EHR data, population management trended data and treatment, and outcome studies could be conducted to support the clinical community in this combat further and give access to these valuable clinical and medical treatment logs to all patients and health care providers.

Research Electronic Data Capture system

Research Electronic Data Capture (REDCap)²² is a secure web-based application software system for building and managing online surveys and databases. REDCap can be applied to collect virtually any type of data in any situation, including compliance with 21 Code of Federal Regulation (CFR) Part 11 (CFRs, which is a codification of the general and permanent rules published in the Federal Register by the executive departments and agencies of the U.S. Federal Government. Title 21 of the CFR is reserved for rules of the Food and Drug Administration). The Federal Information Security Management Act, passed by the U.S. Congress in 2002, requires federal agencies to implement information security plans to protect sensitive data. U.S. HIPAA and General Data Protection Regulation (which is a European Union law) designed to control online and offline data collection for research studies and operations.²² REDCap was initially created in 2004 at the Vanderbilt University to support a group of clinical researchers who needed a secure data collection method that met the HIPAA compliance standards. REDCap then quickly shifted to its top data collecting choice for supporting both single and multisite research studies.²²

Established in 2006, the REDCap consortium now comprises 4421 active nonprofit organization partners in 138 countries. Each partner site has full access to the REDCap system to fully customize the systems to meet local security policies and personalize features/functionality to address user needs for free.²² Researchers can design their projects both online and offline with REDCap, and then input and modify data on the web or mobile app. REDCap databases/surveys can be edited by researchers from multiple sites and institutions, enhancing the sharing of data and collaboration between partners within the research community. Its flexible UI provides researchers the capability to fully customize their project/survey and implement a concept-to-production database within a short period.²²

In response to the COVID-19 pandemic, researchers have been using REDCap worldwide in many instances to support COVID-19 surveillance and research. Public health institutions such as the Centers for Disease Control and Prevention (CDC) and the NIH are tracking and monitoring COVID-19 patients with REDCap in the United States.²³ The Health Department for symptomatic critical service workers uses REDCap Mobile App for drive-through COVID-19 testing in Tacoma, Washington.²⁴ REDCap has also been used to process electronic applications to rapidly develop telehealth capabilities with pediatric patient portal infrastructure for COVID-19 care.²⁵

Online COVID-19 public heat maps and networks

At the start of the COVID-19 outbreak, many universities and health organizations scrambled to get the pandemic data out and available to the public. Data were spread all around cyberspace at an alarming rate. Here, we address three platforms of the digital medium that serve to inform the public of real-time data during COVID-19. The first platform is the John Hopkins University (JHU) COVID-19 Map,²⁶ followed by the University of Washington's Novel Coronavirus Map,²⁷ and the University of Virginia (UVA)'s COVID-19 Surveillance Dashboard.²⁸

JHU COVID-19 Map

Researchers at JHU have developed an online interactive dashboard in response to the COVID-19 outbreak, hosted by their Center for Systems Science and Engineering. This dashboard serves to visualize and track reported cases of COVID-19 in real time.²⁹ The dashboard, first shared publicly on January 22, 2020, shows the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository.²⁶ As the volume and new case rates increase, the database has to be updated automatically at set time intervals, and other forms of social media are used to add timely numbers.²⁶

University of Washington Novel Coronavirus Map

Researchers and scientists at the University of Washington have also delved into the field of COVID-19 data sharing to help spread awareness of the outbreak. Sponsored by the Humanistic Geographic Information Systems (HGIS) laboratory, this online interactive map enables users to track the global and local trends of COVID-19.²⁷ The data points used here are collected from various sources, including, but not limited to, the National Health Commission of the People's Republic of China, WHO, U.S. CDC, and the Public Health Agency of Canada. Like the JHU COVID-19 Map, the data set points are free and open to public use and can be found and downloaded on their HGIS laboratory website.²³

UVA COVID-19 Surveillance Dashboard

In its support of the planning and response efforts for the recent coronavirus pandemic, the Network Systems Science and Advanced Computing division of the Biocomplexity Institute and Initiative at the UVA has created and developed a visualization tool that provides numerous ways of examining COVID-19 data.²⁸ The UVA data source similarly encompasses a wide range of factors, ranging from countries affected, including the United States, China, India, Canada, Germany, and Greece. Updates are all done twice a day at specified time intervals. Downloading of the data is free and open through their interactive website.²⁸

Networks of the CDC and 60 U.S.-affiliated jurisdictions

The CDC³⁰ is a national public health agency in the United States. It is an agency under the Department of Health and Human Services, and is headquartered in Atlanta, Georgia. It is a founding member of the International Association of National Public Health Institutes. The CDC's main goal is to protect and promote public health and safety through the control and prevention of disease, injury, and disability in the United States and internationally.³⁰ Currently, the CDC focuses its efforts on (1) preserving critical federal, state, and local resources that are needed to respond to COVID-19, as well as critical health care, emergency, and port resources, (2) preventing further spread of COVID-19 into and within the United States, (3) conducting research and providing timely information on the pandemic advancement, prevention, and control, and (4) timely reporting of new scientific research and clinical treatment results worldwide. The CDC also reports COVID-19 case counts, deaths, and laboratory testing numbers daily online. Data on the COVID-19 website and the CDC's COVID Data Tracker are based on the most recent numbers reported by states, territories, and other jurisdictions. Data are dependent on jurisdictions' timely and accurate reporting. In addition, the CDC regularly reports provisional death certificate data on the NCHS website. Reporting the number of deaths by using death certificates ultimately provides more complete information but is a longer process, and therefore, these numbers will be less than the death count on the CDC COVID-19 website. There are currently 60 U.S.-affiliated jurisdictions reporting local cases of COVID-19 with information provided for public precautions. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands, and three independent countries in compacts of free association with the United States (Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau). New York State's reported case and death counts do not include New York City's counts as they separately report nationally notifiable conditions to the CDC. The global data on the COVID-19 pandemic have also been monitored and announced by the WHO on a daily basis.

Policies and Guidelines Established by Leading Global Public Health Institutions

The severity of the current pandemic has prompted the leading global public health institutions to create COVID-19-specific interim policies and guidelines for collecting, processing, storing, shipping, and handling the suspected COVID-19 specimens for biobanking, testing, and research. These important policies and guidelines are web-broadcast and critical to ensure the quality, effectiveness, security, and safety of the national and international collaborations in COVID-19's research, diagnosis, new vaccine and drug development, prevention, clinical treatment, and logistics. A study of these important and complementary policies and guidelines has been reported recently.³¹ Six of the leading global public health organizations or institutions included in the study³¹ are the WHO,³² the Pan American Health Organization,³³ the U.S. CDC,³⁰ Public Health England (PHE),³⁴ the U.S. Food and Drug Administration,³⁵ and the Office of Research at the University of California, San Francisco.³⁶ Following the recommended guidance and policies with extreme precautions is essential to ensure the quality of the collected COVID-19 biospecimens and the accuracy of the conducted research, testing, or treatment, and to prevent any possible transmission.

Based on the study and analyses above, the current data-sharing networks and approaches have the following important features and similarities^28–30:

A user-friendly interface where users can view cumulative and daily progress data.

A time slider/line allows users to see how conditions have evolved over the course of the pandemic.

A search tool that allows users to focus on regions or topics of interest.

A search tool that allows users to filter data sets down to specific points and show specific records in research, testing, clinical trials, new vaccine-drug development, guidelines, policies, and so on.

An ability to add or remove attributes from charts by clicking on their associated icons.

A tool to sort and export subsets of data for analysis, investigation, and collaboration.

Data-Sharing Challenges, Problems, and Potential Solutions

It is always a difficult task to present a massive volume of data in a useful and effective way to the viewers. In addition, the sheer volume of data being collected will pose a problem, as well as the timeliness of data and resources and time invested in inputting big data.^28,29 For example, all of the aforementioned interactive pandemic maps use third-party data sources in some capacity and are all subject to the time lag and other forms of information bias.³⁰ Other challenges more specific to data sharing include obtaining informed consent from participants for data sharing and academic usage, and how to ensure that quantitative and qualitative data are legally and ethically shared while removing all forms of personally identifiable information.³⁷ With many challenges and problems being encountered daily, the importance of streamlined processes becomes more and more needed as the data volume grows higher and higher. In addition, decentralization and lack of communication also contribute to data inconsistencies. When national governments do not have accurate resources to respond to outbreaks appropriately, the consequences are catastrophic.

Most enterprises identify more general problems with their data-sharing tactics. In a 2018 report from Springer Nature, respondents who participate in data sharing cite five key challenges that hamper their ability to derive adequate insights in a timely manner as follows:

Organizing data in a presentable and useful way (46%).

Unsure about copyright and licensing (37%).

Not knowing which repository to use (33%).

Lack of time to deposit data (26%).

Costs of sharing data (19%).

For enterprises that have little or no experience with the data economy, they may have a few perceived barriers to data sharing that do not come to fruition once the process begins. These may include the fear of their own data being misrepresented or misused, policies that may limit their access to broader data streams, and an approval process that is overly long or not well defined. The barriers to data sharing that do tend to appear also include factors such as the following:

Data not appearing in electronic form.

Limited resources, rendering the data-sharing initiative worthless.

Lack of knowledge about where the data actually live.

Concerns about privacy, confidentiality, and informed-consent issues.

Ownership of intellectual properties that are generated based on the information from the date-sharing systems.

There are certainly many other challenges of data sharing, especially when conducted in a manner that is not governed by secure software. That is why a platform such as Data Republic's Senate³⁸ is able to support enterprises as they dive into the data economy—all within a comprehensive framework that manages legal, governance, and licensing workflows.

How can we turn data sharing challenges into solutions? There are a number of tactics for resolving such barriers to entry, from outlining formal agreements (that define the scope of use, confidentiality agreements, and Memorandum of Understanding [MOUs]), to deploying technical IT solutions for security and a free flow of the data, and to training employees on where and how to access the data. The most crucial elements in overcoming these data sharing challenges are collaboration and communication between the parties (individuals or enterprises) while being diplomatic about each party's wants and needs and employing a liaison if necessary.

Proposed Platform Models for National and International Data Sharing and Management

As many frontline COVID-19 researchers from around the world are dedicating their time and efforts to test the biospecimens and investigate the pandemic, the problem comes: the test results and data are dispersed across the globe, which cannot be effectively shared and investigated.³⁹ Currently, there is a lack of centralized and integrated network platforms at either the national or international levels, which are designed explicitly for data sharing of COVID-19-related research, testing, virtual sample analyses, clinical trials, prevention and treatment, policy guidelines, and management logistics. Establishment of such a national or international “all-in-one” data-sharing platform is crucial and very helpful for users to “one-step” enter the larger database to search, study, analyze, discuss, and exchange the information using the advanced technologies that are powered by artificial intelligence (AI) tools (i.e., machine learning, deep learning).^40,41 Utilizing these platforms, it would be possible to facilitate research and development of new approaches and technology for better early detection plans and methods, better diagnoses and treatments, and better management decisions for increased efficiency for virus containment no matter where around the world.⁴¹

A systematic, orderly, and integrated approach is proposed to establish the national and international data-sharing and management platform models as follows:

Step A: In every country, seven national database networks, named as National Special-database Networks (NSNs), should be first established through either government agencies or professional organizations to cover at least seven special areas, including (1) COVID-19 specimen biobanking, (2) research and development, (3) EHR-EMR, (4) clinical trials, (5) law and regulation, (6) policy guideline protocols, and (7) pandemic progress tracking.

Step B: As shown in Figure 1a, a proposed national COVID-19 data-sharing platform, named as the National Platform Registry (NPR), would be formed and made up of the seven NSNs established in Step A. The seven NSNs are (1) National Biobanking Network, (2) National Research and Development Network, (3) National EHR-EMR Network, (4) National Clinical Trial Network, (5) National Law and Regulation Network, (6) National Policy and Guideline Network, (7) National Pandemic Progress-Tracking Network, and a potential additional network for other issues (e.g., logistics, teleconferences, and special activities).

FIG. 1.

(a) Proposed structure of the National Data-Sharing Platform, named as the NPR, which is made up of seven NSNs. (b) Proposed structure of Model 1 of the International Data-sharing Platform, named as GPR. GPR, Global Platform Registry; NPR, National Platform Registry; NSNs, National Special-database Networks.

Step C: The international data-sharing platform, named as the Global Platform Registry (GPR), would be founded through two models. As shown in Figure 1b, GPR (Model 1) would connect and represent all NPRs, authorized and financially supported by all participating nations worldwide. The second model (Model 2) of the GPR is proposed as shown in Figure 2. As indicated in Figure 2a, seven Global Special Networks (GSNs) would be established separately, to connect and represent seven NSNs from all nations, respectively, and accordingly. For example, one of the seven GSNs, the Global Special-database Network for biobanking would connect and represent the National Biobanking Networks (i.e., one of the seven NSNs) of all nations, authorized and financially supported by all participating nations in the world. Each of the seven GSNs would provide services to one special area with the corresponding special interest group. Combining all seven GSNs together interactively, the second model (Model 2) of the GPR would be created as shown in Figure 2b. The GPR Model 1 or Model 2 could be organized and operated by the United Nations, WHO, other leading organizations, and so on.

FIG. 2.

(a) Proposed structure of a GSN. (b) Proposed structure of the Model 2 of the International Data-sharing Platform, named as GPR. GSN, Global Special-database Network.

The proposed global data-sharing platforms above may be used to improve the data-sharing and management quality, security, and efficiency, as well as to reduce costs for all research institutions, biotechnology companies, pharmaceutical corporations, governments, hospitals, and biobanks worldwide. It should be emphasized that the establishment and success of the NPR, GSN, and GPR will require intensive collaborations of many professional groups, associations, societies, corporations, institutions, governments, and international organizations to solve potential technical, ethical, racial, cultural, social, legal, financial, and political issues and problems worldwide. From Figures 1 and 2, the function of GPR Model 1 is the same as that of Model 2, although the establishment of the Model 1 requires the formation of the NPRs in all nations, while the Model 2 requires the formation of the seven GSNs. Before the GPR formation, a GSN-like global network is relatively flexible and easier to be first established for a specific interest group worldwide.

Most recently, BBMRI-ERIC,⁴² a European-based research infrastructure for biobanking, has partnered with the International Society for Biological and Environmental Repositories to establish a “GSN-like” platform for biobanking, named as the COVID-19-Ready Biobank Registry. This Registry connects all biobanks around the world that are offering samples and data sets available to assist with COVID-19-related research. For any individual or institute, after signing up on the BBMRI-ERIC Directory listing with all COVID-19-ready member biobanks, one can then use the Directory and its advanced COVID-19 filters to search for the needed data sets. Biobankers and researchers could use the built-in Negotiator communication platform for requesting biospecimens and/or their big data.⁴² Although this platform may not fully correspond with the proposed solution for the global data sharing in some aspects, it is a good starting point to build such an integrated system to ensure the efficiency and effectiveness of the utilization of COVID-19 pandemic information with biobanking specimens for high quality of research and applications. There is no cost to participate in this registry.⁴² The COVID-19-Ready Biobank Registry platform is now ready for use, which is truly an exciting initiative and a GSN model for the global date sharing associated with biobanking and its great applications.

In addition, it is well known that some Asian countries (e.g., China, Korea, and Singapore) have achieved remarkable success in the fight with COVID-19 by using data sharing and data-driven solutions early in the COVID-19 epidemic. Especially in China, a big country with the largest population in the world, their important actions included upgrading and expanding the existing digital technology ecosystem, most notably in facial recognition technology and communication applications such as WeChat. Data sharing and internet links between the government and institutions, companies, hospitals, or communities enabled the Chinese government to draw on large amounts of user data, often in real time. Data-sharing tools generated from mobile phone tracking data are at the heart of China's approach to epidemic prevention and control. On a national level, the mobile phone tracking has helped to monitor and visualize large-scale population movement as well as to display and make open to the world the spread of the virus. It has further bolstered screening and monitoring of high-risk groups. These tools have been developed either by telecom companies tracking registered phone numbers or by private corporations who draw on data collected through multifunctional apps (including online payments and GPS), providing the government and the public with easy access to these interactive services. For example, using a mobile phone, a person could enter his/her train or flight number to check if there were any infections reported related to his/her trip, assess a hospital's ICU capacity, or find the density of people at a given location.⁴³ By mid-February 2020, 40% of all COVID-19-related mined data were directed toward visualization and monitoring efforts to analyze the spread of the virus, according to the China Academy of Telecommunication Planning Research, an institution affiliated with the Ministry of Industry and Information Technology (MIIT).⁴⁴ In addition, these tools have also provided inputs to develop the Quick Response-code health apps that enabled quarantine restrictions to be lifted relatively swiftly. To identify potentially infected and at-risk people, the Chinese government deployed the refined facial recognition technology with added temperature sensors and infrared identification solutions (which can be used to identify a person wearing a face mask). Hospitals and doctors used data-sharing platforms for disease monitoring, hospital-wide management of staff and patient beds, interactions between hospitals, AI-based remote online consultations, big data resource management, on-site diagnosis, and patient care.⁴⁵ Developers of new contact tracing and health applications have been able to build on their existing user agreements and standard privacy policies that enable them to share and disclose personal information without additional consent in the interest of public safety and public health. In general, China's data-driven management of COVID-19 has been shown to be effective with great achievements, reviving public life in a strictly controlled form based on rapidly deployed solutions to trace people's movements, contacts, clinical data, and health status. The data-sharing and digital platform solutions developed in China have improved medical research, new vaccine and drug development, patient treatment, and resource management within the health sector.

Conclusion

The COVID-19 crisis is creating unprecedented needs and demand for information sharing and research. With the study of the existing approaches, tools, software, network-systems developed for data sharing and management in the United States, new integrated network platforms (i.e., the NPR, GSN, and GPR) for national and international data sharing and management are proposed in this article to overcome the problems associated with data decentralization, inconsistencies, financial support, international communication, standardization, and globalization. In general, this will enable faster transfer of data globally, and the scientists, researchers, and institutions associated with the COVID-19 data-sharing platforms will, in turn, be able to effectively communicate and collaborate in data-driven COVID-19 research, prevention, and treatment.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding support to declare for this work.

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