To many global health professionals, the emergence of the COVID-19 pandemic has not come as a complete surprise. The outbreak of SARS that occurred in the autumn of 2002 in Guangdong Province, China—characterized as the first near-pandemic in the era of globalization [1]—marked the beginning of a new century in which global health security events would become more frequent and escalate rapidly across the globe. Following SARS came the global pandemic of H1N1, outbreaks of Middle East Respiratory Syndrome, the Zika virus, and new Ebola outbreaks. These served as early warning signs of what would become the most significant human health emergency since the 1918 influenza pandemic: the current COVID-19 global pandemic that has, as of February 2021, resulted in at least 100 million cases and 2 million deaths worldwide [2].

Throughout this period of accelerated outbreaks of novel, emerging, and re-emerging infectious diseases, calls for sound public health policy and further expansion of public health surveillance capacity to prevent future pandemics have become more frequent and urgent [3]. However, investment in public health infrastructure, such as strengthening state capabilities under the World Health Organization International Health Regulations, did not heed these warnings: experts have painted a bleak picture of outbreak preparedness by characterizing the global pandemic response as cycles of panic succeeded by neglect [4]. Consequently, various systems for disease surveillance, including electronic public health reporting modalities, were challenged by the complex requirements associated with COVID-19-related data. Some of these challenges stemmed from the inability of the relevant public health agencies to receive and share electronic data at a pandemic scale [5], some from the use of inappropriate or outdated tools lacking interoperability [6], and some from failing to meet security and privacy requirements [7].

As COVID-19 cases continue to surge, national governments have attempted to invest in and deploy more robust digital disease surveillance systems. These encompass different forms of technology (eg, digital epidemiology, big data, machine learning, mobile apps, and distributed computing), which are now viewed as critical tools to explore in order to modernize the pandemic response [8]. While a rapid increase in innovation and investment in this area of technology has occurred, many of these technology-centric initiatives have encountered implementation barriers due to nontechnical challenges associated with data governance, user adoption, concerns about accountability and oversight, and patient privacy and social acceptance concerns [7,8]. An emerging technology that has been suggested in this context is blockchain, a form of distributed ledger technology that is maturing in several industries, including in areas of digital cryptocurrencies, financial transaction technology, and growing attention in industrial sectors, such as energy, transportation, supply chain, auditing, and health care [9].

The adoption of blockchain, which can be characterized as an append-only distributed database that is coordinated via a peer-to-peer protocol [10], removes the need for central operators and can offer potential improvements over traditional health care information management systems (eg, client-server systems) [9]. Blockchain allows for tamper-proof replication of data in an adversarial environment [11]. The technology is resilient to fault scenarios in which adversaries send conflicting information to different parts of the system [12], even if those adversaries present large numbers of pseudonymous identities with malicious intent [13]. Participants on a blockchain form consensus on whether a proposed record is admissible by adjudicating it using a consensus mechanism [14], thus ensuring only valid records agreed upon by network members are replicated.

Consensus on a blockchain network can be proof based (eg, proof-of-work consensus as used by the Bitcoin blockchain) or voting based (eg, proof-of-authority consensus) [15], with different hybrid forms being an emerging field of research [16]. Regarding access control, blockchain protocol taxonomies differentiate between public or private and permissionless or permissioned networks [17]. Public blockchains are open to participation by anyone, whereas private, or enterprise, blockchains employ access control mechanisms. In a permissionless system, all members have the same responsibilities in the consensus protocol, while permissioned networks assign different responsibilities in consensus to participants, depending on their role and authority.

Several use cases have emerged evidencing the potential utility of blockchain in health care data management. These include electronic health record (EHR) management and aggregation, privacy-preserving algorithms for health systems data, integration of blockchain systems with the Internet of Medical Things, enabling distributed patient-provider directories across multiple payers and providers, and enhancing management and security of health supply chains [18-21]. Accompanying this potential, blockchain also faces real-world implementation challenges, including storing and transferring data on- and off-chain, interoperability with other health information systems, managing permission structures, and ensuring scalability [22].

Blockchain has also been suggested as a potential solution in the context of COVID-19 algorithmic contact tracing by promising protection from cyberattacks [23], allowing for global monitoring of social encounters to inform health policies [24], enabling privacy [25,26], preventing the falsification of diagnoses [27], allowing users to retain ownership of personal data [28], and ensuring the trustworthiness of that data [29], while maintaining a record of its provenance [26]. While none of the popular algorithmic contact tracing frameworks on the market today [30] uses blockchain, the growing number of academic works [23,25,27,31-38] suggests significant interest. Hence, this viewpoint aims to critically examine the potential utility and technical feasibility of blockchain technology for pandemic algorithmic contact tracing. This is accomplished by applying a blockchain evaluation framework that assesses the suitability of using the technology for specific use cases based on seven key questions. The viewpoint concludes with some recommendations of whether blockchain is a viable application for this critical public health use case and other observations about how to leverage this technology in the ongoing fight against COVID-19.

Contact tracing is an epidemiological control measure aimed at identifying all the people with whom an individual who contracted an infectious disease has been in contact, and who are, in turn, at risk of being infected with and transmitting that disease to other close contacts [39]. It has pronounced benefits in controlling infections that remain undetected in the population [40], such as the transmission of COVID-19, in which a large proportion of cases could be asymptomatic [41]. Quick, reliable, and accurate tests to confirm cases are a prerequisite for successful contact tracing [42,43], as, without them, infectious individuals can remain unidentified and continue to serve as human vectors sustaining community transmission. Insufficient testing can lead to underreporting of the true prevalence of the disease and its attack rate, as well as limit the effectiveness of nonpharmaceutical interventions, such as masking, social distancing, and other crucial public health interventions [44]. Contact tracing, however, represents only one single stage in the process of effective outbreak control and response, which is only effective when combined with quarantine and isolation procedures [45].

Contact tracing has a rich history dating back to the late nineteenth century, when UK medical officers responded to infectious disease outbreaks such as smallpox with surveillance systems involving notification, isolation, disinfection, and case finding [46]. The information age brought digital case management systems and other innovations (eg, digital epidemiology via mobile apps, internet surveillance, and disease modeling and forecasting using artificial intelligence) that are now being leveraged by health authorities. Yet, traditional interview-based approaches remain a mainstay [8,47,48]. Here, contact information is collected by health care professionals or volunteers who discover the contact history of individuals affected by an infectious disease through interviews with patients, families, or health care professionals or by analyzing medical records, tracking data, or surveillance data [49].

Where an interview-based contact tracing technique is used, its success relies on the ability of those affected to recollect their contact history. The reliability of such self-reported data is, however, questionable [50]. Moreover, contact with unknown persons cannot be discovered through this approach. Furthermore, conventional contact tracing regimens are labor intensive, are associated with high costs per case, and yield diminishing disease prevalence reductions under incremental investments [51]. Consequently, doubts have been raised about whether these traditional methods alone can be effective in the context of a large-scale pandemic. As a reaction, digital epidemiology methods, including algorithmic contact tracing, have been proposed to reduce virus transmission more effectively [8]. Digital epidemiology, or “the use of data generated outside the public health system for disease surveillance” [8], has been discussed since the 1990s [52]. Most of the early approaches to digital epidemiology use passive methods by repurposing data from “a range of sources most of which do not relate to healthcare utilization” [supplementary material, 8]. In contrast, most modalities of algorithmic contact tracing can be considered active methods, as users have to enable data monitoring and sharing consciously.

Algorithmic contact tracing automates the conventional contact tracing process by allowing for the collection, aggregation, and analysis of automatically generated data about a case’s contacts by a public health agency, thus eliminating the need for laborious interviews. The large volume and multimodal nature of the clinical informatics systems and epidemiological data that go along with this approach constitute a challenge that new technologies can help address [53]. This applies particularly to algorithms that can quickly assess potential exposure and risk patterns while enabling faster notification to suspected contacts [54]. Countries that have applied algorithmic contact tracing aggressively, by making the use of mobile phone tracing apps compulsory (eg, China and South Korea), were able to reduce daily positive cases more effectively than those that used approaches where participation was voluntary [55]. While it is unclear whether the containment of case numbers can be directly attributed to algorithmic contact tracing, the use of big data to trace individuals is a commonality of the pandemic containment strategies applied by these countries [56]. Further, real-world experiences with the deployment of algorithmic contact tracing illustrate the complexity of the ethical issues associated with these technologies, including the need to balance individual privacy and autonomy concerns with the utility of such data to prevent disease spread during a public health emergency [8,56].

To reiterate, the goal of contact tracing is to identify people that had contact with each other, thereby identifying a potential path of exposure and infection. While a definition of what can be considered contact in the context of COVID-19 is still evolving [57], active algorithmic contact tracing commonly uses physical proximity and duration of exposure [58] as an approximation. Data gathered for algorithmic contact tracing commonly takes one of three forms: (1) proactively reported data (ie, manual digital check-ins that require participants’ compliance [59]), (2) active sensor data (ie, information about an encounter with a different device utilizing the same tracing app), and (3) passive sensor data (ie, information about the geographic position of the device). These are commonly generated by devices using Bluetooth, including Bluetooth Low Energy; GPS; and Wi-Fi signal strength information [60]. Bluetooth allows for active sensing, delivering information about the proximity of two sensors with submeter accuracy [61,62]. Passive sensing through GPS and Wi-Fi uses environmental data to approximate the geographic position of a device with relatively high precision under good conditions [63,64], but insufficient precision under suboptimal conditions [65-67]. Based on this information, an algorithmic contact tracing system can reconstruct when individual devices have been close, thereby allowing it to proactively alert those who are identified as being at risk of infection once confirmation of a positive case is made known to the system.

As previously discussed, algorithmic contact tracing is now an emerging use case for blockchain technology. However, little had been published on this topic and its application to infectious disease control before the COVID-19 pandemic, despite prior outbreaks of other diseases. One of the first contributions in this space is by Kangbai et al [68], who proposed a “Blockchain platform to conduct real-time Ebola contact tracing” in the context of the 2018 outbreak of this highly virulent virus in the Democratic Republic of Congo. Subsequently, the COVID-19 pandemic has led to increased interest in the topic and several publications and preprints in the medical and engineering literature. From a systemic perspective, the dominant function of blockchains in algorithmic contact tracing is that of tamper-proof, distributed data stores for managing contact data [23,25,27,31-38]. A less pronounced function is that of a data integration layer that allows for the exchange of medical and public health information from different sources among different actors in health care settings [23,25,27,32]. Data that are exchanged in this way take the shape of certified COVID-19 diagnostic data [23,25,32] or immunization data [27].

Proposed algorithmic contact tracing blockchain systems appear as distributed architectures consisting of varying numbers of nodes in a network (see Figure 1). Commonly, these systems do not employ access control but, instead, grant read and write access to the public [23,25,31,33-36,38]. Less commonly, read access is provided to the public but write access is restricted [37], yet other architectures operate as private systems [27,32]. In most public systems, each node stores a replica of all data network-wide. Here, data are potentially encrypted and stored according to a format specific to the protocol used. In some architectures, external entities like hospitals or laboratories access the blockchain to obtain data for analysis [37], potentially correlating them with data held by external EHR systems. The consensus mechanisms used for data replication between nodes are rarely discussed. Where they are, mechanisms are selected either for their performance characteristics [25] or to implement authority-based forms of consensus [23,35,37]. While some proposals do not discuss the role of smart contracts [31,37,38], many employ smart contracts to validate data on the chain [23,32,34-36], mostly to prevent malicious users from inserting fabricated records of positive diagnoses into the system [23,32].

Proposed blockchain-based algorithmic contact tracing systems (see Figure 1) also generally cater to two types of actors: subjects that use contact tracing apps and, except for cases in which only self-reported data are used [34,36], medical professionals that digitally attest to positive cases [23,25,32]. Through their mobile devices, subjects obtain environmental signals from passive sensors [33], proximity signals from active sensors [23,31,32,34,36-38], or a combination of both [25,27,35,36]. The data received are converted into a target format to be stored on the blockchain, the particulars of which are a key differentiator between protocols. While some protocols disregard information privacy [33], more commonly, the confidential nature of tracing data is recognized and addressed by proposing formats that are deemed to prevent a subject’s privacy from being compromised.

The goals of privacy-focused blockchain architectures are as follows: preventing manipulation of diagnostic data [23], preventing impersonation of health authorities [31], protecting the identity of infected persons [25,36], or, most commonly, precluding mass surveillance through the derivation of movement or contact profiles from stored data [25,31,32,34,35,37,38]. To approach these goals, various models to capture data relevant to contact tracing are proposed. Contact data commonly come in the form of contact lists using one-time pseudonyms [23], pseudonymized user data combined with encrypted location information [25], pseudonymized or encrypted diagnoses [31,32], or encrypted epidemiological data [37].

Irrespective of the format, after being generated on the user’s device, contact data are sent to the blockchain. This can happen via submission of a transaction to a public network [23,25,31,33-36,38] or, specifically in the case of private systems where subjects do not have direct access to the blockchain [27,32,37], by passing through an upstream application programming interface that can be operated by government bodies [27,37] or by a consortium of otherwise trustworthy entities [32]. The data are then replicated between nodes. In the case of a positive diagnosis for a subject, there are two alternative patterns. First, subjects can reveal their positive status on the chain proactively, oftentimes by providing some form of proof [23,32]. Second, a pseudonymized diagnosis can be uploaded to the chain [31,33] or endorsed on-chain by an authorized diagnostician [25]. Subjects can then query the blockchain at intervals to obtain a risk assessment based on their previous contacts [32,38] or to receive notifications [23,36,37]. The overall purpose of these proposed systems is to enable decentralized networks that can share trusted data relevant to contact tracing efforts, including self-reported data and environmental signals. Nevertheless, parameters around data storage, computation, and measures to ensure privacy-preserving processing vary and can be further modulated by developers should these be implemented.