4

New Technologies and Data Systems

The COVID-19 pandemic brought unprecedented challenges in disease detection and mitigation efforts due to the high volume and rapid spread of infections and insufficient public health resources with which to address them. These challenges exposed major gaps in national and global health capacities for early detection and swift response to an emerging pathogen of pandemic potential. Efforts to address these challenges resulted in advances in innovative technology for detecting, monitoring, and even predicting COVID-19. Among the many recent innovations in disease surveillance and control, this chapter focuses on those technologies that are relevant for the activities carried out by the Division of Global Migration and Quarantine (DGMQ). These include innovations for digital contact tracing, symptom reporting and monitoring, digital health certificates, digital data collection, data dashboards, and novel surveillance capabilities that have been developed and implemented at various scales and in different locations around the world since the outset of the COVID-19 pandemic.

In addition to highlighting some of these innovations and implementation examples, this chapter explores capabilities and concerns associated with novel digital data streams and collection. Although digital tracking and data collection can be more scalable, comprehensive, and expeditious than manual strategies, these technologies raise serious concerns regarding infringement on data privacy and pose ethical risks regarding personal and potentially sensitive data. This chapter considers the inherent tension between defending individual rights and liberties and protecting collective well-being. Ethics concerns associated with digital technologies for data collection and infectious disease control are explored in relation to the

ethical considerations, such as protecting privacy, maintaining autonomy, promoting equity, minimizing risk of error, and ensuring accountability. Strategies to address ethical concerns while capitalizing on the benefits these technologies offer are discussed.

In addition to ethical considerations, the adoption of disease surveillance and monitoring technologies faces logistical challenges. Although incorporating digital technologies may allow the DGMQ to improve its capability to collect health data from travelers, trace transmission, and alert travelers of exposures, adoption depends on the public’s trust and confidence in these interventions. Furthermore, data collection systems must be interoperable in order for the numerous stakeholders across various sectors to carry out their responsibilities in controlling major disease events. Key components needed to achieve interoperability of data systems are outlined. Opportunities for the DGMQ to leverage technology innovations hold potential to mitigate scale limitations of current screening and data collection processes to increase capacity to address a broad range of infection control purposes—including future pathogens of pandemic potential.

COVID-19 DETECTION TECHNOLOGIES

COVID-19 has resulted in numerous advances in technology, primarily in detection technology. Besides the gold standard of reverse transcriptase polymerase chain reaction (RT-PCR) in detecting COVID-19, new technologies have sought to improve on the limitations of RT-PCR, while keeping speed of detection a priority (Zhao et al., in press). These high-technology solutions are wide ranging—from other nucleic acid amplifications such as loop-mediated isothermal amplification (LAMP), serology-based assays, CRISPR-based assays, metagenomics next-generation sequencing (mNGS), aptamer-based assays, and lateral-flow technologies—to artificial intelligence- (AI-) assisted diagnoses, various spectroscopies including infrared spectroscopy, and nanotechnology-based approaches such as electrochemical sensors (Han et al., 2021; Li et al., 2020; Lukose et al., 2021). High-tech solutions tend to compensate along four dimensions, by providing (1) more information (e.g., miniaturized and multiplexed CRISPR [Zusi, 2020]), (2) more speed (e.g., field-effect transistor-based biosensors [Seo et al., 2020]), (3) more convenience (e.g., face masks with tiny, disposable sensors [Trafton, 2021], wearable device-detecting heart rate variability [Hirten et al., 2021]), or (4) more wide-ranging environmental methods of detection (e.g., detection of volatile organic compounds exhaled by positive cases [Giovannini et al., 2021], bioaerosol sensors). Beyond high-tech, additional nonintuitive and public-facing methods to detect, monitor, and even predict COVID-19 have emerged: sniffer dogs (Lippi et al., 2021), smartphone-app tracking (Verma and Mishra, 2020), and surveillance of sewage (CDC, 2022a;

Sweetapple et al., 2022). Future uses of these technologies include acting as tools to deal with future pandemics, especially as technologies such as the combined application of air collection and viral detection, whether it be polymerase chain reaction (PCR) analysis (BioFlyte, 2021) or cell analysis, will allow us to detect airborne disease of any kind in real time, decrease the spread/exposure early on, and formulate policies that pose the least interference to normal life. Outside COVID-19 and future pandemics, these advances also have the potential to assess populations’ risks of exposure to infectious agents, through wastewater monitoring of infectious agents in a building or community, smartphone tracking for contact tracing, and crowd movement data for predicting outbreaks and hot spots (Zhao et al., in press). In addition, the role of AI–assisted radiologic computerized tomography (CT) scan (Harmon et al., 2020) and X-ray (Baltazar et al., 2021) readings developed during COVID-19 suggests that AI has supplemented, and will continue to supplement, our health decisions in the future.

USE OF INNOVATIVE AND INTEGRATIVE DIGITAL TECHNOLOGIES

Innovative digital technologies for collecting and aggregating data are essential tools for protecting the public’s health from the introduction of diseases through international borders. During the COVID-19 pandemic, these types of technologies have been developed, refined, and implemented in countries around the world. The data collected using these technologies, as well as other novel data streams, can be used for a broad range of infection control purposes, including (1) contact tracing and proximity tracking to identify and monitor individuals potentially exposed to SARS-CoV-2 infection; (2) symptom reporting, monitoring, and tracking; (3) digital health certification, and (4) situational awareness and rapid epidemic intelligence. Coupled with advances in machine learning, AI, and other advanced analytical techniques for operationalizing the data, these new digital technologies and novel data streams provide public health authorities with a more powerful set of tools for surveillance and response than ever before. They also offer a range of opportunities for the DGMQ to leverage these innovative approaches to mitigate scale limitations of the current processes for implementing health screening and data collection at U.S. airports, as well as approaches to support health departments with post-arrival monitoring and follow-up of travelers.

Digital Technologies for Contact Tracing and Proximity Tracking

The World Health Organization (WHO) defines contact tracing as “the process of identifying, assessing, and managing people who have

been exposed to a disease to prevent onward transmission” (WHO, 2020). When implemented systematically and comprehensively, contact tracing can contribute to the control of infectious disease outbreaks by breaking chains of transmission in a community through the identification and subsequent isolation and management of infectious individuals. However, successful contact tracing strategies must be bolstered by adequate health system capacity and resources to conduct contact investigations and then rapidly test, treat, and monitor potential cases (WHO, 2020).

Advantages of Digital Contact Tracing

Traditional manual contact tracing involves conducting interviews with people who are infected to identify other individuals with whom they have been in close-enough contact that the infection could potentially have been transmitted (Barrat et al., 2021). Identified contacts are then notified, generally by phone, that they may have been infected and are advised about appropriate measures, such as quarantine and symptom monitoring. This manual process has long been used as a public health strategy to help control the spread of infectious pathogens, but it is limited by its labor intensiveness, slowness, and reliance on the infected individual’s recollection of their recent contacts (Barrat et al., 2021; Rodríguez et al., 2021). During the COVID-19 pandemic, the success of manual contact tracing efforts was undermined by a range of factors, including the volume of infections, insufficient public health resources and experienced contact tracing staff, lack of cooperation by contacts, and mistrust of government (Lo and Sim, 2021).

Digital contact tracing and proximity tracking technologies¹ can mitigate certain barriers to manual contact tracing by leveraging the ubiquity of data collected from smartphones to support efforts to control the spread of infectious diseases. As mobile phones have become an increasingly ubiquitous part of human lives, they are the most preferred implementation platform for digital contact tracing and tracking systems (Chowdhury et al., 2020). These applications are downloaded onto an individual’s personal device and are used to determine whether that individual has come into contact with individual(s) who may by infected. The application then notifies the exposed individual and/or a public health agency with guidance about subsequent testing, treatment, isolation, monitoring, or other infection control measures (Ada Lovelace Institute, 2020).

___________________

¹ Although the two are often conflated, digital proximity tracking differs from digital contact tracing in that the latter is a newer approach to augment the former long-established public health practice. Proximity tracking involves the measurement of signal strength to ascertain whether two personal devices—typically smartphones, although wearable devices can also be tracked—have been in sufficiently close contact to risk the transmission of an infectious pathogen (WHO, 2020).

Digital contact tracing and proximity tracking applications can be used in several different ways to report cases of infection: (1) users can self-report infection through the application, with or without clinical or diagnostic confirmation, (2) a health care provider or test provider can report confirmed cases to the service operating the application, or (3) public health agencies or other authorities can input lists of individuals with confirmed infection (Ada Lovelace Institute, 2020). In addition, these digital technologies can be used to identify potentially infectious individuals with very large numbers of contacts, which could help to contain so-called “superspreader” events (Elmokashfi et al., 2021).

Digital contact tracing and tracking can be more scalable, comprehensive, and expeditious than manual strategies alone (Barrat et al., 2021; Grekousis and Liu, 2021). Although the superiority of digital contact tracing and tracking alone over traditional manual strategies has yet to be clearly established, digital solutions can complement and expedite manual strategies, particularly in the context of an accelerating outbreak (Anglemyer et al., 2020; Barrat et al., 2021; Elmokashfi et al., 2021; Ferretti et al., 2020; Grekousis and Liu, 2021). The scalability and speed of these technologies could be particularly advantageous for infection control efforts conducted by the DGMQ at borders and ports of entry, given the potential for large numbers of travelers quickly dispersing to different locations (Ferretti et al., 2020). It is also important to note that the use of these technologies is reliant on individuals’ willingness to give permission for notifications.

Proximity and Location Awareness Technologies

Digital tracing and proximity tracking technologies rely on various types of device-based proximity and location awareness technologies that can be used to monitor individuals’ movement, location, and proximity to other devices (see Table 4-1) (Grekousis and Liu, 2021). Location awareness technology is designed to indicate the precise location of a user—e.g., global navigation satellite systems (GNSS) or global positioning system (GPS). Technology such as WiFi (including the Encounter-Based Architecture for Contact Tracing [ENACT] and WifiTrace), Bluetooth Low Energy (BLE), Beacons, and Quick Response (QR) codes typically collect data only regarding devices’ proximity to each other, although in some cases they can be used to detect a user’s precise location as well (Grekousis and Liu, 2021).

Other technologies that can be leveraged for location and proximity tracking for public health purposes include cellular networks used by mobile phone providers, radio-frequency identification (RFID)—a wireless communication modality that utilizes radio frequency—and near-field com-

TABLE 4-1 Proximity and Location Awareness Technologies Used in Digital Contact Tracing and Tracking

NOTE: WB, wideband; UWB, ultra-wideband.

SOURCE: Data from Grekousis and Liu, 2021.

munication (NFC), which is applied in smart technologies such as access control systems and wireless payment and ticketing systems. Each technology has technical limitations in detecting distance in different scenarios, but most contact tracing system developers prefer BLE as the proximity sensing technology due to its cost-effectiveness, as BLE functionality is already built into smart devices by manufacturers (Min-Allah et al., 2021). Beyond case and contact identification, proximity and location awareness technologies on personal smartphones, as well as cameras, can also be used for monitoring individuals during isolation or following up with individuals who have traveled to settings with high risk of infectious disease transmission (Mbunge, 2020).

Comparing Centralized and Decentralized Architectures for Digital Data Management

Digital contact tracing and proximity tracking technologies can be distinguished by whether they use a centralized or decentralized system architecture to manage and share the collected data (Russo et al., 2021). A centralized system comprises a top-down architecture whereby data collected from smartphones or other peripheral devices are consolidated and stored in a central remote server, where the data are analyzed to inform public health actions (Grekousis and Liu, 2021; Russo et al., 2021). Decentralized systems utilize a bottom-up architecture in which data collected are retained and managed by the smartphones themselves, through technological infrastructure developed and provided by third-party companies, such as Apple or Google (Russo et al., 2021). The decentralized approach empowers individual users to control their own data and determine whether the data are uploaded to a central server (Grekousis and Liu, 2021). The centralized approach has the advantage of being able to collect greater volumes of data, but gives rise to serious concerns around the infringement on data privacy; the decentralized approach is less prone to privacy invasion, but may not be able to collect sufficient data for effective contact tracing and tracking unless an adequate number of users consent to share their data with the central repository (Grekousis and Liu, 2021).

Examples of Digital Contact Tracing and Tracking Technologies during the COVID-19 Pandemic

During the COVID-19 pandemic, multiple countries worldwide introduced digital contact tracing and proximity tracking using mobile devices to support efforts to curb the transmission of SARS-CoV-2. During the early phase of the pandemic, South Korea (Whitelaw et al., 2020), China (Golinelli et al., 2020), Taiwan (Golinelli et al., 2020), and Singapore (Savona, 2020) deployed digital contact tracing and tracking technologies that—in combination with strict transmission control measures, such as lockdowns and quarantine—appeared to contribute to limiting the spread of infection relatively successfully at the outset. As the pandemic continued through 2020 and 2021, many other countries also rolled out smartphone-based digital contact tracing applications. In Norway, for example, the introduction of a nationwide contact tracing application was found to have a tracing efficacy of 80 percent, with an estimated 11 percent of close contacts identified through digital tracing that could not have been identified via manual tracing (Elmokashfi et al., 2021).

A review of the use of COVID-19 contact tracing applications in nine countries found that they had variable and generally limited success in terms of public uptake and effectiveness in controlling transmission of SARS-CoV-2 in their populations (Russo et al., 2021). In the implementation of the National Health Service (NHS) COVID-19 app in England and Wales, it was found that for every percentage point increase in app use, there was a decrease in number of cases by 0.8 percent (Wymant et al., 2021). Additionally, based on the analysis and use of the SwissCOVID app in Switzerland, the digital contact tracing app was as effective as classic contact tracing (Salathe et al., 2020). However, subsequent reviews have suggested that if certain barriers related to ensuring data privacy, increasing effectiveness, encouraging population uptake, and addressing technical limitations are surmounted—discussed later in this chapter—the implementation of digital contact tracing and tracking efforts can be an effective component within a suite of public health infection control measures (Elmokashfi et al., 2021; Grekousis and Liu, 2021).

Digital Technologies for Symptom Reporting and Monitoring

During the COVID-19 pandemic, a number of countries have implemented digital systems and technologies for symptom reporting and monitoring. Symptom reporting and monitoring applications can be accessed on a website or installed on a personal smart device. Users can input details about their symptoms and, if they choose to do so, other personal information including demographics, geographic location, medical history, household information, and so forth (Ada Lovelace Institute, 2020). Data collected regarding individuals’ symptoms can be used by public health authorities, for individuals that chose to do so, to support case identification and to initiate the processes of testing, treatment, and isolation of index patients and their contacts as appropriate.

The use of smart wearable technology—in conjunction with AI—is also emerging as a cost-effective strategy for screening, triaging, and remotely monitoring patients’ symptoms and vital signs, such as blood pressure, electrocardiography (ECG), heart rate, and fever (Channa et al., 2021). Symptom checkers such as digital “smart” thermometers can be used to collect, analyze, and share health data, especially body temperature. Remote symptom monitoring via wearables is of particular value in settings where people have limited physical access to health care facilities, scenarios in which health care facilities are overwhelmed beyond capacity, and/or settings where the risk of infectious disease transmission is high in facilities (Channa et al., 2021; Mbunge, 2020). Moreover, symptom data can be used to understand disease transmission patterns and bolster epidemiologi-

cal surveillance efforts as part of early warning systems, discussed in the next section.

Although the use of digital technologies for symptom reporting and monitoring, including wearables, is relatively new, it holds great promise in supporting the response to infectious disease outbreaks. Wearable digital technologies are associated with relatively fewer privacy risks than digital contact tracing and tracking, the data collected still need to be appropriately and robustly protected, particularly if they are collected in a centralized system (Ada Lovelace Institute, 2020). Other limitations include risks related to the quality of data collected (e.g., self-reported symptoms, false reporting), need to ensure linkages to care once symptoms detected, the impact of the digital divide in excluding vulnerable populations and worsening inequities, the need for more research to validate their efficacy and clinical utility, and the lengthy processes involved in gaining regulatory approval for medical devices (Ada Lovelace Institute, 2020; Channa et al., 2021).

Digital Health Certificates

Digital health certification is another application of novel digital health technology that emerged during the COVID-19 pandemic. Like data collected via other digital health technologies, digital health certification can be housed within a centralized system—such as electronic health records, health information systems, or public health authority databases—or a decentralized system, such as a physical record or digital token on an individual smart phone.

A number of countries have considered or implemented digital immunity certificates, a specific type of digital health certificate, to authenticate that a person has either already been infected with SARS-CoV-2 or has some other putative form of immunity against COVID-19 disease, such as vaccination (Ada Lovelace Institute, 2020). Generally, the aim of digital immunity certification is to ensure that a person can safely return to work, school, or other social settings. However, there is not yet a scientifically robust and empirically well-established way to establish that an individual has immunity against SARS-CoV-2 infection. Thus, the use of digital immunity certificates has garnered criticism and lack of support in some countries, such as the United Kingdom, due to the risk of negative societal consequences—for example, undermining of personal rights and freedoms, discrimination, and stigmatization (Ada Lovelace Institute, 2020). Additionally, the use of digital health certificates will require a standardization of health data collected and utilized by all institutions (Marios Angelopoulos et al., 2020). Further in this chapter, we discuss the importance of interoperability and the ethical consideration to ensure protection of patient information and rights.

Barriers to and Supports for the Use of Digital Technologies

Although innovative digital contact tracing and tracking technologies can serve as powerful mechanisms for collecting data to support the response to an infectious disease outbreak, epidemic, or pandemic, there are a range of technical and logistical barriers associated with their deployment in a population. Major technical barriers of interoperability and standardization are discussed in detail in the next section. Other technical limitations include digital technologies’ imprecision in consistently and accurately detecting distance,² the ambiguity in defining what constitutes a “contact” at risk of transmitting or acquiring for a given infectious pathogen, inability to detect and trace contacts of asymptomatic patients, the potential for smartphone location or proximity technology to be deactivated or inaccurate, the need for skilled expertise to implement and maintain the system, the need to integrate complex security algorithms to ensure data protection and guard against fraud and abuse, and lack of supporting information and communication technology infrastructure and electronic health policy (Ada Lovelace Institute, 2020; Mbunge, 2020).

The use of digital contact tracing and tracking technologies are also associated with multiple ethics risks and legal challenges to individuals and societies, because the applications have access to individuals’ personal and potentially sensitive data pertaining to their health behaviors, household location, traveling history, and other private information (Mbunge, 2020). A cross-country comparison of digital contact tracing apps used during the COVID-19 pandemic found that their success was limited due to concerns about data privacy (Russo et al., 2021). Another cross-country survey on the user acceptability of contact tracing apps deployed in France, Germany, Italy, the United Kingdom, and the United States reported that the main barriers to uptake were concerns about cybersecurity, privacy infringement, and lack of trust in the government (Altmann et al., 2020).

A fundamental logistical challenge in successfully deploying digital technologies for public health purposes is the need for a sufficiently widespread population uptake. For instance, some modeling studies have estimated that a digital proximity tracking technology would need to be adopted by 60–75 percent of a country’s population to be maximally effective for contact identification during the COVID-19 pandemic (WHO, 2020). Other reviews have suggested that the uptake rate would need to

___________________

² Proximity of digital devices serves as the proxy for contact, but it depends upon measurable vectors (e.g., distance, time) that are inevitably imprecise to determine “contact”; this runs the risk of both false positives and false negatives. Additionally, is difficult for digital contact tracing to control for variables that underpin manual contact tracing (e.g., environment, ventilation, wind direction) (Ada Lovelace Institute, 2020).

reach at least 90 percent to control the epidemic if digital contact tracing is deployed in the absence of manual tracing (Grekousis and Liu, 2021). Digital and manual contact tracing are best used as complementary approaches to increase their overall effectiveness (Wang, 2021). However, widespread population uptake requires public trust and confidence that (1) the technology is effective in reducing the transmission of infectious disease and (2) the use of the technology does not compromise privacy, autonomy, or any other human rights and liberties (Ada Lovelace Institute, 2020; Nuffield Council on Bioethics, 2020; The Lancet Digital Health, 2020; WHO, 2020). Issues related to data security, privacy, ethics, equity, and autonomy—along with strategies to mitigate them—are discussed further later in this chapter.

Building trust and confidence in the technologies’ effectiveness to encourage uptake is a reciprocal undertaking, because “…evidence of effectiveness directly affects uptake, while uptake directly affects effectiveness” (The Lancet Digital Health, 2020). Moreover, rolling out an untested and ultimately ineffective digital technology the first time can irrevocably undermine public trust in future interventions (Ada Lovelace Institute, 2020). Thus, an effective strategy for building public trust would involve a gradual process of robust research about the effectiveness of a new technology—and the minimum rate of population uptake required—that is coupled with clear and transparent communication to the public about its effectiveness, as well as its associated risks and uncertainties (Ada Lovelace Institute, 2020; Grekousis and Liu, 2021; The Lancet Digital Health, 2020).

Opportunities for the DGMQ to Leverage Innovative Digital Technologies

The DGMQ has the opportunity to incorporate and improve on the use of these novel digital technologies to gather health data from travelers, trace transmission, and alert exposures to travelers. This would also contribute to the development of scalable approaches to disease control strategies for large numbers of incoming travelers at borders and ports of entry. However, the successful use of these digital technologies is contingent on their integration within a strong network of supportive process and services for testing, treatment, and follow-up of people who have been or may have been exposed to infection. Success also depends on the public’s trust and confidence in these interventions, as previously discussed. Thus, the DGMQ needs to consider strategies to engender public trust in the rollout and implementation of these technologies. The DGMQ is positioned to collaborate with other divisions within the U.S. Centers for Disease Control and Prevention (CDC) to lead conversations with stakeholders and local public health departments around innovative health technologies in order to increase public trust and lower barriers to uptake.

LEVERAGING NOVEL DIGITAL DATA STREAMS TO IMPROVE SITUATIONAL AWARENESS

To be of actionable value, data gleaned and aggregated from innovative digital technologies and novel digital data streams must be integrated into existing public health surveillance systems to detect outbreaks as early as possible and inform the appropriate response efforts to halt transmission. These surveillance systems often rely on natural language processing and machine learning to process, filter, analyze, and operationalize the enormous amount of data now available through these novel digital technologies and data sources (Allam et al., 2020; Jose et al., 2021). These new surveillance techniques could be leveraged by the DGMQ to improve its readiness and develop more flexible and targeted strategies for the control of infectious diseases at borders and ports of entry to the United States.

Novel Digital Data Streams

To support detection, monitoring, and public health decision making during an outbreak, these novel streams of data can be used to improve situational awareness at the early stage of a disease outbreak and to evaluate the risk of introduction of new pathogens through data dashboards, models, simulations, and other novel surveillance approaches to bolster rapid epidemic intelligence. In addition to those described in the previous section, other novel digital data streams that are increasingly being leveraged for public health surveillance of infectious diseases include online news websites, news aggregation services, internet search queries, video surveillance, participatory web platforms for self-reporting symptoms, and other streams of open-source and crowdsourced data (Aiello et al., 2020; Mello and Wang, 2020). Geospatial temporal data gleaned from smartphone geographic information system (GIS) functionality represent a vast and rich novel source of data about users’ location and movements, which far exceeds the information that can be captured about geospatial and temporal patterns of infectious disease transmission using traditional surveillance methods (Hswen et al., 2022). The web-based application COVIDseeker, for example, captures and processes continuous fine-grained geospatial temporal data from smartphones to elucidate transmission of COVID-19 (Hswen et al., 2022).

In the context of an infectious disease outbreak, social media can serve as a powerful tool to disseminate public health information; data from social media platforms have also been used to help detect and predict cases of infectious diseases, such as influenza and malaria, prior to the COVID-19 pandemic (Tsao et al., 2021). During the COVID-19 pandemic, data from social media platforms were harnessed in a range of new ways for public

health purposes. For example, the COVID-19 Surveiller³ system was developed, using novel deep-learning models, to utilize social media users as “social sensors” for predicting pandemic trends—including new cases and death rates—as well as identifying potential risk factors to inform public health interventions (Jiang et al., 2022).

Innovations in Surveillance, Outbreak Analytics, and Early Warning Systems

The COVID-19 pandemic exposed major gaps in national and global health systems’ capacities for early detection and swift response to an emerging pathogen of epidemic or pandemic potential. This has underscored the need for more effective early warning systems, given that epidemic and pandemic events are likely to become more frequent due to expanding urbanization, increasing global interconnectedness through travel and trade, the effects of climate change, and pervasive socioeconomic inequities (Carroll et al., 2021). As mentioned in Chapter 3, the CDC has established the new Center for Forecasting and Outbreak Analytics, which will bring together next-generation public health data, expert disease modelers, public health emergency responders, and high-quality communications to meet the needs of decision makers.

Early warning systems and other core capacities for surveillance can be strengthened by leveraging—in an effective and ethical way—the wealth of information that can be gleaned from novel data streams to augment traditional surveillance strategies. Early warning systems can be bolstered by rapid epidemic intelligence, which mines open-source data in tandem with traditional surveillance methods to detect early epidemic signals. Machine learning, AI, and algorithms for clinical diseases and syndromes are used to establish the baseline against which abnormal signals can be detected (Allam et al., 2020; Kogan et al., 2021). For example, at the outset of the COVID-19 pandemic, epidemiologists in China and Indonesia used data from the EpiWatch open-source observatory to detect early signals of pneumonia or severe acute respiratory illnesses as a proxy for COVID-19 (Kpozehouen et al., 2020; Thamtono et al., 2021). Computational approaches have been used to provide real-time risk assessment of case importation and their origin by leveraging on human mobility data. In particular, modeling and data analytics have been used to provide importation risk and estimates of the volume of imported cases during emerging health threats such as Ebola, Zika, and COVID-19 (Bogoch et al., 2015, 2016; Pullano et al., 2020).

___________________

³ COVID-19 Surveiller is available at http://scaiweb.cs.ucla.edu/COVIDsurveiller (accessed February 22, 2022).

Other innovations in surveillance—leveraging novel digital data streams developed, refined, and implemented during the COVID-19 pandemic—include data dashboards, crowdsourcing, nowcasting, forecasting, and wastewater analysis. Early warning systems based on novel digital data sources, AI, and modeling approaches can provide real-time situational awareness and risk analysis.

Data Dashboards

During an outbreak, the synthesis of diverse data streams within broadly accessible online data dashboards can be powerful and dynamic tools for monitoring a rapidly evolving event, communicating epidemiological information, and informing decision making at all levels (Ivankovi et al., 2021). A data dashboard has been defined as a “visual representation of the most critical information required to fulfill one or more objectives, condensed on a single screen so that it can be monitored and understood at a glance” (Zhao et al., 2021). Using these dashboards, data can be represented in a range of visual formats including lists, tables, graphs, and maps.

Multiple dashboards were developed during the COVID-19 pandemic, as well as previous major infectious disease outbreaks, providing a range of critical information for monitoring and response. These dashboards include features such as (1) real- or near-real-time maps of cases and deaths, (2) predictive risk maps based on population geospatial mobility data, (3) maps of superspreader trajectories and contacts, (4) vaccine-related information, and (5) reactions to the evolving pandemic on social media platforms (Kamel Boulos and Geraghty, 2020; Zhao et al., 2021). For instance, Canada’s Global Public Health Intelligence Network (GPHIN),⁴ part of WHO’s Global Outbreak Alert and Response Network (GOARN), was developed in 1997 and now monitors internet media from around the world to help detect potential infectious disease threats (Carter et al., 2020). In the United States, all 50 states developed their own publicly accessible dashboards for tracking and responding to the COVID-19 pandemic, albeit with significant variation in their design, content, and functions due to the lack of guidance for harmonization (Fareed et al., 2021). Other key examples of dashboards that leverage novel data streams to support rapid epidemic intelligence and the dissemination of information include:

___________________

⁴ More information about the Global Public Health Intelligence Network is available from https://gphin.canada.ca/cepr/aboutgphin-rmispenbref.jsp?language=en_CA (accessed February 22, 2022).

• Johns Hopkins University’s Center for Systems Science and Engineering (JHU CSSE) dashboard⁵ (Kamel Boulos and Geraghty, 2020)
• A WHO Coronavirus (COVID-19) Dashboard⁶ (Kamel Boulos and Geraghty, 2020)
• United States’ HealthMap⁷ (Kamel Boulos and Geraghty, 2020)
• Epidemic Intelligence from Open Sources (EIOS)⁸
• The International Society for Infectious Diseases’ Program for Monitoring Emerging Diseases (ProMED)⁹

Crowdsourcing Surveillance

Innovations implemented during the COVID-19 pandemic demonstrated the value of crowdsourcing surveillance efforts in strengthening situational awareness, even before digital tracing and tracking applications or symptom reporting platforms were made available. In the early stages of an outbreak, compiling line lists of persons with suspected, probable, and confirmed infection—based on the evolving case definitions, in the case of a novel pathogen like SARS-CoV-2—is critical for initial assessment of the potential for epidemic growth and spread, as well as determining the appropriate infection control measures such as isolation and quarantine (Leung and Leung, 2020).

A crowdsourced surveillance approach was implemented in China in January 2020, when researchers aggregated daily case counts reported at the province level with individual-level data about patients with COVID-19 drawn from a Chinese social media network used by health care providers (Sun et al., 2020). This synthesized crowdsourced line list was consistent with the official national epidemiological reports provided by the Chinese government. In Japan, a crowdsourced data stream strengthened the national COVID-19 surveillance system, called “COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking” (COOPERA). The system, which was implemented by a popular mobile messenger app used by the majority of the Japanese population, collected

___________________

⁵ Johns Hopkins University’s Center for Systems Science and Engineering dashboard is available at https://coronavirus.jhu.edu/data (accessed February 22, 2022).

⁶ The WHO Coronavirus (COVID-19) Dashboard is available at https://COVID19.who.int (accessed February 22, 2022).

⁷ HealthMap is available at https://www.healthmap.org/en (accessed February 22, 2022).

⁸ The Epidemic Intelligence from Open Sources platform is available at https://www.who.int/initiatives/eios (accessed February 22, 2022).

⁹ The International Society for Infectious Diseases’ Program for Monitoring Emerging Diseases is available at https://promedmail.org/about-promed (accessed February 22, 2022).

information about users’ COVID-19–like symptoms. Strong correlations between clusters of self-reported symptoms and outbreaks of confirmed cases highlighted crowdsourced data’s value as an early warning system for impending outbreaks (Desjardins, 2020). In the United States, Outbreaks Near Me uses crowdsourced data to help individuals and public health agencies map the recent pandemic coronavirus, COVID-19, and the annual influenza (Outbreaks Near Me, 2022). This crowdsourced approach relies on voluntary participation from the general public that is asked to report their health status. Anybody can actively participate, providing weekly updates on their health status, even if no symptoms are experienced, and the data are used to provide a real-time awareness for the disease spread and prevalence. Similar approaches have been developed in Europe, Australia, and New Zealand (FluTracking, 2022; GrippeCOVIDnet, 2022).

Nowcasting and Forecasting

Timeliness is critical in detecting and responding appropriately to contain an infectious disease outbreak, but there are multiple points of potential delay from the point of symptom onset through care seeking, testing, and eventual reporting to public health authorities (Greene et al., 2021). Another innovation in epidemiological surveillance spurred by the COVID-19 pandemic is epidemic “nowcasting”—or “predicting the present”—to enhance situational awareness and inform response efforts during a rapidly evolving outbreak or epidemic by synthesizing real- or near-real-time data from novel data streams (Greene et al., 2021; Wu et al., 2021). One approach to nowcasting is to monitor prediagnostic streams of data—such as self-reported symptoms on participatory crowdsourced platforms and data from wearables and smart thermometers, internet searches, and social media posts—to gain a timely understanding of an evolving outbreak, albeit with a lack of specificity in distinguishing diseases such as SARS-CoV-2 from other respiratory illnesses with similar symptoms (Greene et al., 2021). A more specific approach is to draw upon partially reported disease data (e.g., near-real-time Google Trends data) and, accounting for reporting delays, using statistical methods and modeling to estimate cases and deaths that have not yet been reported (Greene et al., 2021). During the early months of the COVID-19 pandemic, the New York City Department of Health and Mental Hygiene effectively used nowcasting to support monitoring of reportable COVID-19 disease data (Greene et al., 2021). Modeling and artificial intelligence approaches are also used to generate insight into infectious disease outbreaks either through short-term forecasts or longer-term scenario analysis that assumes specific interventions or policies, such as in the specific CDC COVID-19 Forecasting Initiative (Allam, 2020, Biggerstaff et al., 2022). Advanced analytics have been used to anticipate the locations

where the COVID-19 pandemic was expanding, estimate the international dissemination of cases, forecast the variation in time of the sources of new introductions of infections, and as well as for estimating the effect of border and travel restrictions policies (Bogoch et al., 2020; Chinazzi et al., 2020; Russell et al., 2021; Wells et al., 2020).

Wastewater Surveillance

Innovations in the use of wastewater as a novel data source for epidemiological surveillance were catalyzed by the discovery of SARS-CoV-2 in infected patients’ feces and wastewater during the COVID-19 pandemic (Polo et al., 2020). This approach, which involves near-source tracking of sewage drains, municipal wastewater, sludge, and other sources to detect individual cases or small clusters of cases (Hassard et al., 2021; Philo et al., 2021), holds promise for facilitating early detection of infectious disease transmission dynamics, particularly in scenarios with limited testing and diagnostic reporting capacities (Peccia et al., 2020). Current research shows the utility of wastewater surveillance in static populations such as hospitals, prisons, and schools, and it could be beneficial for migrating populations if implemented in ports of entry, planes, cruise ships, or other modes of transportation (Hassard et al., 2021). The DGMQ should consider any forthcoming research on wastewater surveillance in migrating populations.

Opportunities for the DGMQ to Adopt or Leverage Surveillance Innovations

To enable detection of signals of outbreaks, the DGMQ could leverage these novel digital data sources and surveillance innovations to develop early warning systems that are integrated into border control. Data dashboards, developed extensively in the pandemic, could be adapted and expanded for the purposes of (1) collating real-time public health data, (2) keeping the public informed about an event as it evolves, and (3) supporting the clear and transparent communication of border policies.

INTEROPERABILITY OF DATA SYSTEMS

To effectively control a major infectious disease event by breaking the chains of transmission, a broad range of individuals, businesses, and institutions require up-to-date public health information about the epidemiology of the outbreak. These stakeholders span multiple sectors, including patients and their families, the health care sector (e.g., providers, administrators, laboratory staff, facility staff), public health authorities at the national level (e.g., the CDC) as well as at the state, tribal, local, and territorial

levels, the transportation industry (e.g., airlines, cruise lines, other forms of public transport), and federal border control agencies. In the absence of efficient and harmonized channels of communication, critical information remains siloed within those various domains (Huang et al., in press).

To build a strong and effective health care and public health system capable of flexing to respond to a public health emergency, such as an infectious disease outbreak of pandemic potential, interoperability is an essential feature across channels of communication and other platforms for information exchange. Interoperability, which can be broadly defined¹⁰ as the ability of two or more systems to exchange and utilize information, is the foundation of effective communications between data systems. Interoperability requires harmonization and standardization across all facets of digital systems, including data, content, platform, protocols, and downstream services (Savona, 2020). Issues that can impede interoperability include heterogeneous networking standards and communication protocols, differences in data semantics and ontology, nonstandardized data formats and structure, and diverse operating systems and programming languages, among others. It will be important for data formats and structure to be standardized across all platforms to avoid missing or redundant data, and to improve data quality (Mbunge, 2020).

Ideally, various digital contact tracing and tracking applications, regardless of their respective platforms, would be interoperable and easily integrated into health information systems to allow for rapid exchange of critical and timely information (Mbunge, 2020). However, these applications are generally developed independently using different protocols, data formats, and application programming interfaces (described in the following), thus are not necessarily interoperable (Mbunge, 2020). Interoperability is also essential for building public trust and confidence in the use of digital technologies for contact tracing and surveillance: “[i]nteroperability is widely considered as contributing to a transparent, trustworthy environment for citizens who face the choice of opting into a contact (or symptom, or immunity) tracing app, across devices, and countries” (Savona, 2020).

___________________

¹⁰ The joint technical committee (JTC) of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) defines interoperability as “the capability to communicate, execute programs, or transfer data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units” (ISO/IEC 2382-01 in Noura et al., 2018). The Institute of Electrical and Electronics Engineers (IEEE) defines the concept as the “ability of two or more systems or components to exchange information and to use the information that has been exchanged” (Noura et al., 2018).

Key Components of Interoperable Information Systems

Investments in data system interoperability can be lifesaving during a pandemic, but they can also improve day-to-day care coordination and reduce administrative costs in the U.S. health system (Carroll, 2020). Key components of interoperable health systems capable of timely, accurate, and comprehensive information exchange include application programming interfaces, data exchange platforms, electronic case reporting systems, and electronic laboratory reporting systems.

Application Programming Interfaces

At the core of interoperability are application programming interfaces (APIs). APIs serve as communication channels between databases, allowing for the electronic exchange of information. In order for two systems to communicate through an API, they must utilize a shared data standard. This shared data standard is similar to a language that two individuals can use to speak to one another. If the API supports the data standard that the two systems can read, it can be used for the exchange of information.

Data Exchange Platforms

During an infectious disease crisis, the ability to share data on clinical outcomes from electronic health record (EHR) patient registries seamlessly and efficiently through interoperable EHR system health care could contribute to strengthening response efforts both across and within health care systems (Jose et al., 2021). Data exchange networks have been developed to allow for the secure and rapid cross-organizational and vendor-neutral exchange of patient health information. One such data exchange network that has seen considerable success is Commonwell,¹¹ which was developed jointly by several health IT companies and launched in 2013. Currently, more than 25,000 providers have joined Commonwell and have received over 2 billion health records in total using the platform (CommonWell Health Alliance, n.d.). This platform enables the exchange of consolidated-clinical document architecture (C-CDA) documents. C-CDA is a data standard that enables the sharing of patient information such as encounters and clinical narratives between provider EHRs, regardless of vendor. By joining Commonwell, health care providers need not rely on dated technology, such as fax, to share patient medical records, allowing them to coordinate patient care more easily.

___________________

¹¹ More information about Commonwell is available from https://www.commonwellalliance.org (accessed February 22, 2022).

As different types of data—including mobile health and genomic data—become more widely available, C-CDA standards are being gradually replaced by the “fast healthcare interoperability resources” (FHIR) standard.¹² FHIR utilizes data elements, or “resources,” that can be linked to other resources called references. Together, resources and their references make up data covering a wide range of health care scenarios. For instance, a resource such as “patient” can be linked to a reference such as “diagnosis” in order to store data about a patient’s diagnosis by his or her health care provider. Health care APIs that use FHIR allow mobile or web-based applications to extract data from EHRs. Finally, to help facilitate electronic exchanges, the U.S. Department of Health and Human Services (HHS) recently released the first version of the Trusted Exchange Framework and Common Agreement (TEFCA) as a common set of guidelines for health information networks (HINs) (HealthIT, 2022).

Electronic Case Reporting

Timely, accurate, and efficient communication between health care providers and public health authorities is also of utmost importance during a pandemic. To report cases of infectious disease, health care providers can utilize a system called electronic case reporting (eCR), which facilitates the automated, real-time exchange of case report information from EHRs to public health agencies. Typically, eCR is not built into EHR systems, so health care providers may need to first integrate their EHR with the eCR Now FHIR App before they are able to use eCR.¹³ The EHR then needs to be linked with the appropriate public health agency that receives the reports. The eCR system runs in the background of EHR systems, recognizing relevant information that must be reported and automatically sending that information to the appropriate public health agency via the Association of Public Health Laboratories (APHL) Informatics Messaging Services (AIMS) data exchange platform.¹⁴ Once a public health agency receives a report, it conducts a review and analysis, and can make appropriate decisions to determine the necessary public health interventions.

As of November 12, 2021, more than 9,600 health care facilities have acquired eCR functionality. To encourage even more widespread use of eCR, the Centers for Medicare & Medicaid Services (CMS) Promoting Interoperability Program has included eCR in its criteria starting January 1,

___________________

¹² More information about the Fast Healthcare Interoperability Resources standard is available from https://www.altexsoft.com/blog/fhir-standard (accessed February 22, 2022).

¹³ More information about the eCR Now FHIR App is available from https://ecr.aimsplatform.org/ecr-now-fhir-app (accessed February 22, 2022).

¹⁴ More information about the Association of Public Health Laboratories (APHL) Informatics Messaging Services (AIMS) data exchange platform is available from https://www.aphl.org/programs/informatics/pages/aims_platform.aspx (accessed February 22, 2022).

2022.¹⁵ The success of eCR implementation has the potential to drastically improve case reporting during a pandemic, allowing public health officials to quickly respond to outbreaks in communities through public health action such as contact tracing and quarantine. However, according to the 2018 American Hospital Association Annual Survey and IT Supplement, 41.2 percent of respondent hospitals reported that they had issues sending electronic data to public health agencies because these agencies could not receive the data. Some states had the majority of hospitals reporting this capability issue; only one state had zero hospitals reporting this problem (Holmgren et al., 2020). This highlights the need for the federal government to invest in public health IT infrastructure in a way commensurate to its investment in health care IT infrastructure.

Currently, the CDC’s Data Modernization Initiative includes plans to integrate nationwide standards for data access and exchange that includes eCR (CDC, 2021a). This modernization of data systems was fueled by the COVID-19 pandemic but can be beneficial for broad disease surveillance (CDC, 2021a).

Electronic Laboratory Reporting

Data exchange between laboratories and public health agencies utilizes a system called electronic laboratory reporting (ELR). The ELR system is critical in ensuring efficient communication with public health agencies so that they can follow up with individuals with COVID-19 and carry out informed public health actions. Similar to eCR, ELR reports are sent to public health agencies through the AIMS platform using the HL7 v2.5.1 data standard.¹⁶ Laboratory reports for SARS-CoV-2 are first sent to state and local public health agencies. This report contains identifying information about the patient, such as name and date of birth, that can aid state and local public health agencies in following up with the patient for contact tracing or other public health action. Before it is sent to the CDC, the ELR data are deidentified by removing information that could be used to determine the patient’s identity (HHS, 2021).

Electronic Passenger Reporting

The CDC works with airlines and U.S. Customs and Border Protection (CBP) to collect passenger contact information to support aircraft contact investigations by state, tribal, local, and territorial public health partners.

___________________

¹⁵ More information about the inclusion of electronic case records in CMS’s Promoting Interoperability Program is available from https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms (accessed February 22, 2022).

¹⁶ More information about the HL7 v2.5.1 data standard is available from https://www.hl7.org/implement/standards/product_brief.cfm?product_id=98 (accessed February 22, 2022).

These investigations enable public health responses to inform exposed passengers, facilitate contact tracing to reduce the risk of subsequent disease transmission from disease exposures on commercial aircraft, and inform estimates of disease transmission risk on aircrafts. Aircraft contact investigations are typically initiated after the CDC receives a report from a health department or foreign ministry of health of a communicable disease case in a person with recent travel on a commercial aircraft. A Quarantine Station or headquarters staff member enters these reports into the Quarantine Activity Reporting System (QARS) and consults with medical officers and Quarantine Branch Aviation Activity staff to determine whether the case meets CDC criteria to initiate a contact investigation. If criteria for initiating a contact investigation are met, the CDC requests passenger manifest information from the airline for the passengers seated around the individual(s) reported to have had a communicable disease on board. The CDC uses disease-specific protocols that establish the exposure zone around an infectious passenger. A manifest is a document that contains the names, U.S. address for noncitizens or permanent residents, date of birth, gender, country of citizenship, and travel document type of individuals aboard the flight and assigned seat numbers on the flight. The airline manifest request timeline can be lengthy and the CDC may need to incorporate data from various formats used by different airlines. Additionally, accompanying contact information provided by airlines with manifests are frequently incomplete or incorrect. Airlines currently must also send passenger and crew manifests to CBP before departure to the United States; however, airlines are only required to transmit the name, date of birth, and gender for U.S. citizens and legal permanent residents. This process is primarily used to confirm the identities of travelers for national security purposes. CBP receives the manifests through the Advance Passenger Information System (APIS), which is an electronic data interchange system, and the airline submits the report through the APIS (or eAPIS¹⁷) portals (CBP, 2014). Additional contact information may be included from CBP’s Automated Targeting System (ATS), which has the capacity to automatically generate a comprehensive record containing important information regarding the at-risk passengers on board the flight. To improve the quality of available traveler contact information, the CDC issued an order to require airlines to collect and maintain contact information, which became effective in November 2021 (Federal Register, 2021). Airlines have the option to share this information through their preestablished mechanism with the U.S. Department of Homeland Security (DHS) (e.g., APIS, eAPIS, or PNRGOV), through which the CDC can access data elements for public health contact investigations, or directly with the CDC on request.

___________________

¹⁷ eAPIS is a web-based system that is available for use by smaller carriers. https://www.cbp.gov/travel/travel-industry-personnel/apis/eapis-transmission-system.

Cruise Ship Enhanced Data Collection

From April 2020 to January 15, 2022, all cruise ships operating or intending to operate in U.S. waters were required to submit the “Enhanced Data Collection (EDC) during COVID-19 Pandemic Form” daily (HHS and CDC, 2021). Upon the expiration of the Temporary Extension and Modification of Framework for Conditional Sailing Order (CSO) on January 15, 2022, the CDC implemented a voluntary COVID-19 risk mitigation program for cruise ships operating in U.S. waters that have chosen to participate in the CDC’s COVID-19 Program for Cruise Ships (CDC, 2022b). As of May 2022, the CDC continues to require daily submission of the “Enhanced Data Collection (EDC) during COVID-19 Pandemic Form.” If a cruise ship opts out of the program, then it is required to use the Maritime Conveyance Illness or the Death Investigation Form to report individual cases of COVID-19 and is asked to submit the Maritime Conveyance Cumulative Influenza/Influenza-Like Illness (ILI) Form (CDC, 2021b).

Efforts to Increase Interoperability

Over the past decade, the United States has made considerable progress in pushing forth efforts to increase interoperability. According to the 2019 National Electronic Health Records Survey, conducted prior to the COVID-19 pandemic, 89.9 percent of respondent physicians were using EHRs (CDC, 2019). In addition, 72.3 percent were using certified EHRs (CEHRs) that are eligible to receive financial benefits from the Medicare/Medicaid Promoting Interoperability Program based on adherence to EHR standards and program criteria. However, fax machines and email used in individual offices continue to be a significant portion of health information exchange, despite the wide adoption of EHRs. Fax machines are paper based and unreliable, often met with busy signals, and can run out of paper. Transmitting by email can also lead to delays—an important email sitting in an individual’s inbox can also be easily missed or remain unread. Lack of interoperability between EHR systems and reliance on outdated and siloed technologies, such as email and fax, reduces the quality and efficiency of care coordination to successfully track, diagnose, and treat infectious diseases.

The Office of the National Coordinator for Health Information Technology (ONC), working with the CDC, has developed a framework to advance the health IT ecosystem for public health. On the top level, the TEFCA facilitates public health access to interoperability networks, which includes query capability to request records; core infrastructure to support exchange, including FHIR API; and consolidation of public health reporting over time. In the middle layer, clinical resources are among the health resources shared under the 21st Century Cures Act Final Rule of 2020, which prohibits information

blocking when not sharing information (e.g., public health reporting data) as required by law. Finally, the fundamental point-to-point interoperability is supported by standard FHIR APIs, which allows push and pull of data in certified EHR systems with the additional ability to integrate decision support with passive triggers for important patient or public health events of interest.

Crowdsourcing Solutions to Interoperability Challenges

Like crowdsourcing epidemiological surveillance, crowdsourcing research and development of solutions to difficult technological challenges can be an effective strategy. “[H]arnessing the power of crowds and online communities […] can help tackle the COVID-19 pandemic, by providing original, actionable, quick, and low-cost solutions to the challenges of the current health and economic crisis” (Vermicelli et al., 2020, p. 183). For example, connectathons offer a powerful platform for crowdsourcing solutions to challenges related to interoperability. Connectathon events focus on developing an open, consensus-built interoperability specification that is both complete and demonstrates that it is possible for implementations written to that specification to connect with each other. Importantly, to encourage innovation, connectathons offer a safe venue for failure free of negative consequences of mistakes (Moehrke, 2013). Organizations such as Integrating the Healthcare Enterprise (IHE) convene these events regularly across the world (IHE, 2022). Box 4-1 provides more information on IHE Connectathons.

BALANCING ETHICAL RISKS WITH PUBLIC HEALTH BENEFITS

In considering whether to implement broad public health measures, there is an inherent tension between the need to defend individual rights and liberties and the responsibility to preserve the collective well-being. While the use of digital technologies to collect data for infectious disease control is a powerful tool for protecting public health, it also raises multiple ethical issues. Thus, deploying such measures inevitably requires a trade-off between the benefits of protecting the public’s health and the potentially deleterious consequences for individuals. In the context of the COVID-19 pandemic, the use of digital technologies for measures like contact tracing, detection of individuals at risk of infection, quarantine enforcement, or epidemiological surveillance has sparked robust dialogue about how to ensure that these measures are taken in accordance with ethical principles.

Foundational Ethical Principles

The committee identified a set of foundational ethical principles that warrant close consideration by the DGMQ regarding the use of data collected via innovative digital technologies, novel data streams, and interoperative public health information systems for infection control measures. These principles include (1) protecting privacy, (2) maintaining autonomy, (3) promoting equity, (4) minimizing the risk of error, and (5) ensuring accountability.

Protecting Privacy

The nature of the data collected through these digital technologies makes it especially important to ensure data privacy and confidentiality, since there are multiple avenues for potential data misuse (e.g., governments targeting political opponents, use of data for migration policy enforcement) (Mello and Wang, 2020; WHO 2020). Given that concerns about privacy infringement and misuse of personal data pose major barriers to the implementation and uptake of infection control measures, the DGMQ needs to make every reasonable effort to protect data privacy and confidentiality.

Some of the privacy risks associated with data collected by public health authorities through these new digital technologies are similar to those associated with the huge volumes of personal data already collected by private companies on a large scale through their platforms, services, and products. For instance, some private companies are already aggregating and analyzing data collected by their own digital proximity tracking application—in some cases, they may even be doing so at the behest of public health agencies and sharing the data (WHO, 2020). However, there are some critical differences between the data use by public health authorities and that by private companies. For example, the former may require personal identifiers or take action based on the data received (Mello and Wang,

2020); the latter may not be aimed primarily at promoting collective public health, but instead driven by financial or other interests (Savona, 2020). Public perceptions about the use of personal data collected by public versus private entities also differ: “[w]hile accepting that their personal data are under the control of internet companies, most citizens seem unamenable to sharing their data for the public interest” (Russo et al., 2021).

Robust efforts to protect data privacy and confidentiality are critical for gaining public trust and encouraging uptake of public health measures. To engender trust, privacy protection can be incorporated into technological solutions—i.e., privacy by design—such that new developments ensure privacy protections by extracting data without sharing personal sensitive information (Nanni et al., 2021). For instance, the Decentralized Privacy-Preserving Proximity Tracing (DP-3T)¹⁸ repository offers a secure, decentralized, privacy-preserving proximity tracing system that can support digitally enabled contact tracing while also providing the highest level of privacy protection and minimizing the data privacy and security risks to individuals (Ada Lovelace Institute, 2020).

Maintaining Autonomy

Respect for autonomy is a bioethics principle. Overriding the respect for autonomy should only be considered when the actions of the individual would seriously affect collective health (Dawson and Verweij, 2007). To adhere to the principle of respecting autonomy, the right of individuals to consent or right to refuse a public health measure should be preserved as far as possible when considering collective well-being. Through explicit and transparent practices to respect individual autonomy, the DGMQ can contribute to bolstering public trust and building “[…] a sense of collective interest in objectives that require individual commitment to be fully achieved” (Russo et al., 2021).

Individual autonomy can be undermined if the use of a digital data collection technology is made mandatory, if there are penalties for refusing to adopt it, or if appropriate informed consent policies are not in place (Savona, 2020). Informed consent policies should mitigate barriers related to language, accessibility, and health and technological literacy; they should also allow an individual to stop participating in data sharing at any time, despite the implications of individual withdrawals on the public health effectiveness of the broader contact tracing effort (Mbunge, 2020). User agreements can provide additional information and another layer of protection for users. With regard to digital contact tracing, informed consent is

___________________

¹⁸ More information about the Decentralized Privacy-Preserving Proximity Tracing repository is available from https://github.com/DP-3T/documents (accessed February 22, 2022).

easier to obtain; however, for digital surveillance or digital fence it would require additional input from the public.

Not all digital data collection technologies currently in use allow people to decide whether to consent to their use (Mello and Wang, 2020), but there are others that require user consent before sending the data for central analysis (Wang, 2021). In a recent paper, Nanni et al. (2021) argue for a decentralized architecture for digital contact tracing, which could improve the autonomy of individuals. They also propose not to limit the data that the individual collects, but provide the individual with full control of that data—so the individual can decide what to share—through a “Personal Data Store” (Nanni et al., 2021). This contrasts with a centralized model for aggregating and analyzing digital contact tracing data used in China, based on the government’s existing big data platform (Mao et al., 2021).

Promoting Equity

The DGMQ has an opportunity to reflect on how infection control measures informed by data collected through novel digital technologies and data steams can promote equity and avoid worsening existing inequities. Different social groups have varying degrees of access and capability to use digital technologies—that is, the “digital divide.” For instance, use of applications for digital contact tracing often requires a smartphone, internet access, and technological acumen, which may exclude groups that are already vulnerable from participating in and realizing the benefits of digital contact tracing efforts (WHO, 2020). Moreover, overreliance on new digital modalities without the option to participate in traditional nondigital public health measures could further exacerbate existing health and socioeconomic inequities—for example, among older people who are not comfortable with smartphones or those who cannot afford them. Additionally, following the ethical principle of “ought implies can,” those with access difficulties should never be penalized for lack of compliance with technology-based solutions. For example, it is unjust to penalize people, or place an undue burden on them without appropriate mechanisms for social support, whose socioeconomic conditions prevent them from self-isolating or complying with other public health measures based on digital contact tracing (The Lancet Digital Health, 2020).

Minimizing Risk of Error

Mistakes in identifying areas and individuals at high risk of infection could have negative public health and social consequences; thus, it is critical for the DGMQ to consider strategies to minimize the risk of error in the use of data collected via novel digital modalities (Mello and Wang, 2020).

Three factors—scope, speed, and sources—contribute to increasing the risk of such errors (Mello and Wang, 2020). Scope is an issue given the huge volumes of data that can be accrued through digital sources, because a relatively small percentage of errors in a dataset can parlay into a very large number of affected individuals. During an infectious disease emergency, the pressure to roll out digital technologies at high speed can undercut time needed for appropriate testing and evaluation, increasing the potential for errors in identifying individuals and communities at high risk; subsequent unnecessary public health measures, such as lockdowns, can have substantial social and economic consequences. Additionally, some sources of information in new digital datasets—for example, internet news sources, social media posts, self-reported symptoms—will inevitably be less reliable than traditional sources, which could contribute to the dissemination of misinformation (Mello and Wang, 2020). If digital technology is used, there should be systems of monitoring and correction in order to reduce burden of mistakes and bias in algorithms. Lastly, communities that are potentially impacted by mistakes in digital technologies should be a part of the decision process, so they are able to tolerate any consequences from mistakes and understand potential benefits of their use.

Ensuring Accountability

It is important to ensure that governments and private companies involved in the development and implementation of novel digital technologies are accountable for what they do with the data collected (Mello and Wang, 2020). In public health emergency situations, such as the COVID-19 pandemic, normally transparent democratic processes for ensuring accountability—which include opportunities for public discourse—may be temporarily overridden based on the need for rapid decision making and deployment of public health measures. This can lead to the development of technological solutions by small groups of public- and private-sector leaders operating outside of normal processes to ensure accountability (Mello and Wang, 2020).

Previous Recommendations to Mitigate Ethical Risks

As previously discussed, there are important ethical risks to be considered when designing and implementing digital technologies to collect and analyze large volumes of personal data for disease control. However, although “ . . . these new uses of people’s data can involve both personal and social harms . . . so does failing to harness the enormous power of data to arrest epidemics” (Mello and Wang, 2020, p. 952). In its deliberations, the committee considered the recommendations set forth by other groups

who have also sought to strike the appropriate balance between mitigating these ethical risks and leveraging the full potential of data accrued through these new digital modalities. This section highlights previous recommendations for the use of digital technologies that the committee found helpful for its work.

Evaluate Proportionality

A common theme across previous recommendations is the need to evaluate and ensure the proportionality of public health interventions—particularly those that are novel and associated with ethical risks. This requires evaluating the burden, intrusiveness, and other risks posed by the measures with the potential for those measures to feasibly and effectively achieve their intended public health objectives (WHO, 2020). This assessment can guide decisions about whether the measure’s effectiveness and impact require a trade-off relative to privacy protection that is proportional and commensurate to its public health benefits (WHO, 2020). According to the Nuffield Council on Bioethics,

Similarly, a recommendation made in a review by the Ada Lovelace Institute maintains that assessments of necessity and proportionality should consider not only the effectiveness of an intervention in achieving its objective, but also whether the aim could be achieved by less intrusive measures (Ada Lovelace Institute, 2020). That is, more intrusive interventions are unlikely to be proportionate if there are less intrusive interventions available that are likely to be just as effective (Nuffield Council on Bioethics, 2020).

After assessing the risks of using a technology compared to the impact of choosing not to use it—for example, evaluating whether digitally enabled surveillance can help to avoid strict lockdown measures—the least burdensome and intrusive alternative should be chosen (Mello and Wang, 2020). Assessing the least restrictive and burdensome options may require both a global assessment across populations most likely to be affected and those most likely to experience severe hardship. If a technology proves to be insufficiently effective to warrant ethical risks, it should be phased

out (WHO, 2020). The WHO also recommends that measures should be temporary, with data collected via digital technologies and sources being deleted after a certain period—and that privacy-preserving measures should be used in technology design (e.g., avoiding the use of geographic position tracking for digital proximity tracking) (WHO, 2020).

Maintaining Autonomy

Previous recommendations commonly hold that measures should be taken to maintain and improve individual autonomy. For example, the WHO maintains that governments should not mandate the use of digital technologies for public health purposes; instead, individuals should be empowered with the ability to make an informed and voluntary—to the extent possible—decision about whether to download and utilize those technologies (WHO, 2020). Given the lack of robust evidence for their effectiveness, mandating the use of digital contact tracing and tracking applications would contravene the principles of necessity and proportionality (Ada Lovelace Institute, 2020). Moreover, mandating their use would likely be unenforceable and undermine their effectiveness and uptake (Ada Lovelace Institute, 2020). The WHO further recommends against the provision of incentives or other inducements for individual use offered by public authorities or private entities, nor should individuals be denied benefits or services for refusal, discontinuation, or withdrawal of consent at any time. Additionally, individuals should have the autonomy to delete any personal data that have been collected or stored by the technology (WHO, 2020).

Protecting Privacy

Previous recommendations focus heavily on the critical need to protect the privacy and confidentiality of personal data collected via digital technologies and novel data streams (Ada Lovelace Institute, 2020; WHO, 2020). Many different measures can be taken to protect privacy, including the use of privacy-preserving data storage infrastructure, ensuring the security of data from various types of misuse, and retaining data for a minimal and limited period of time (WHO, 2020).

A major privacy-related issue to consider is the choice between using a centralized or decentralized architecture to store digital data (see previous section). Both approaches can potentially be privacy preserving, albeit with respective vulnerabilities that would have to be addressed. The WHO’s guidance highlights an emerging consensus among data protection authorities toward decentralized approaches as the preferred option for protecting privacy (WHO, 2020). Within a decentralized structure, users have a

greater degree of autonomy to grant or withdraw consent for their personal data to be shared with health authorities or other parties (WHO, 2020).

Regardless of the storage infrastructure used, it is also crucial that the appropriate data protection and privacy laws are in place and adhered to, ideally supported by regulations for legal and limited data processing, restrictions to prevent data misuse, oversight processes, and sunset clauses for discontinuing and dismantling technology after the emergency period has passed (WHO, 2020). The WHO guidance also recommends aggregating and anonymizing data collected (when possible), strictly limiting the retention of data to the emergency response period, and then subsequently deleting all data collected for those purposes (WHO, 2020).

Promoting Equity

Previous studies recommended that measures should be taken to promote equity and avoid exacerbating existing inequities (Ada Lovelace Institute, 2020; Nuffield Council on Bioethics, 2020; WHO, 2020). To that end, the WHO (2020) recommended that strategies should be designed specifically to reach marginalized populations and vulnerable communities. To improve access to technologies among people with limited resources, potential approaches include lowering mobile data costs for digital public health technologies and making certain types of smart devices more affordable and accessible (WHO, 2020). Another approach is to develop digital contact tracing applications based on unstructured supplementary service data (USSD), which does not require internet access, thus enabling people living in areas without internet access to participate in digitally enabled public health measures (Mbunge, 2020).

Minimizing Data and Restricting Use

The WHO’s guidance strongly recommends implementing strategies to minimize the amount of personal data collected through digital modalities and to restrict data use to the extent possible (WHO, 2020). Specifically, the collection, retention, and processing of data should be limited in scope to the minimum amount necessary to achieve the intended public health objective. For digital proximity tracking, for example, data collection practices should not require users to disclose their identities, locations, or the specific timing of a proximity event (WHO, 2020). The WHO also calls for the strict prohibitions on the sale or use of collected data for any type of commercial purposes or the sharing of data with government entities or third parties that are not directly involved in the ongoing public health response, including law enforcement or immigration agencies (WHO, 2020).

Testing and Evaluation

The WHO recommends conducting careful testing and evaluation of novel technological solutions both before and after implementation, as most of these technologies currently lack a sound evidence base for their effectiveness across different settings and scenarios (WHO, 2020). Prior to widespread rollout of technologies, they should be robustly tested to ensure they are functionally effective, technically robust, and without security flaws. After implementation, it is critical to continuously evaluate and monitor the performance of the technology on the ground in real-world conditions. Ideally, an independent party would conduct the monitoring and evaluation, then publish and publicly disseminate the results (WHO, 2020).

Integrating the Use of Digital Data in Interventions

The use of digital data collection technologies—such as digital contact tracing and proximity tracking applications—should be situated within a broader set of public health interventions, practices, investments, and policies (WHO, 2020). Additionally, the WHO recommends that governments and health systems should carefully consider and clearly communicate to the public how the chosen suite of policies, interventions, and technologies integrate and complement each other within the broader strategy (WHO, 2020). Similarly, a review by the Ada Lovelace Institute maintains that these digital technologies can be effective as part of an emergency or transition strategy, but cautions that they are not a replacement for sound policy. Instead, “[t]echnologies must form a part of holistic public health surveillance strategies and other pandemic response initiatives; without supporting evidence, they can and should not replace other proven methods” (Ada Lovelace Institute, 2020, p. 9).

Ensuring Public Engagement, Transparent Accountability, and Strong Governance

Previous recommendations highlight the importance of ensuring robust public engagement, transparent accountability, and strong governance in implementing digital technologies for disease control (Ada Lovelace Institute, 2020; Mello and Wang, 2020; Nuffield Council on Bioethics, 2020; WHO, 2020).

“Effective policy interventions using technology take account of the social dimension of technology and its societal impact, are designed with the input and involvement of people across society, and are monitored and evaluated to assess their social impact on individuals and communities” (Ada Lovelace Institute, 2020, p.9). Thus, digital technologies should be implemented using a transparent process governed by independent over-

sight bodies that include members of the public (including representatives of marginalized groups), public health authorities, ethics experts, data and technology experts, and civil society organizations (Ada Lovelace Institute, 2020; Mello and Wang, 2020; WHO, 2020). In addition to being an ethical imperative, this type of participatory approach to oversight can also promote voluntary uptake and participation (WHO, 2020).

The DGMQ needs to design and implement clear, inclusive, and trustworthy public communication strategies to explain the rationale for implementing digital technologies for the common good, as well as providing justification for the collection and use of personal data (Nuffield Council on Bioethics, 2020; WHO, 2020). Data-processing agreements also need to disclose to users whether and why personal data may be shared with third parties (Ienca and Vayena, 2020). Ensuring accountability also requires appropriate safeguards against abuse, with individuals provided the opportunity to challenge data collection and use practices. Persons who are subject to unwarranted surveillance should have access to “effective remedies and mechanisms of contestation” (WHO, 2020).

CONCLUSIONS AND RECOMMENDATIONS

Conclusions

Recommendations

REFERENCES

Anglemyer, A., T. H. Moore, L. Parker, T. Chambers, A. Grady, K. Chiu, M. Parry, M. Wilczynska, E. Flemyng, and L. Bero. 2020. Digital contact tracing technologies in epidemics: A rapid review. Cochrane Database of Systematic Reviews 8(8):CD013699. https://doi.org/10.1002/14651858.cd013699.

Baltazar, L. R., M. G. Manzanillo, J. Gaudillo, E. D. Viray, M. Domingo, B. Tiangco, and J. Albia. 2021. Artificial intelligence on COVID-19 pneumonia detection using chest xray images. PLOS ONE 16(10):e0257884. https://doi.org/10.1371/journal.pone.0257884.

Barrat, A., C. Cattuto, M. Kivela, S. Lehmann, and J. Saramaki. 2021. Effect of manual and digital contact tracing on COVID-19 outbreaks: A study on empirical contact data. Journal of the Royal Society Interface 18(178):20201000. https://doi.org/10.1098/rsif.2020.1000.

Biggerstaff, M., R. B. Slayton, M. A. Johansson, and J. C. Butler. 2022. Improving pandemic response: Employing mathematical modeling to confront coronavirus disease 2019. Clinical Infectious Diseases 74(5):913-917. https://doi.org/10.1093/cid/ciab673.

BioFlyte. 2021. Bioflyte launches first field deployable airborne COVID-19 detection system to improve school and workplace safety. https://bioflyte.com/2021/05/26/bioflyte-launches-first-field-deployable-airborne-COVID-19-detection-system-to-improve-school-and-workplace-safety (accessed February 23, 2022).

Bogoch, I. I., M. I. Creatore, M. S. Cetron, J. S. Brownstein, N. Pesik, J. Miniota, T. Tam, W. Hu, A. Nicolucci, S. Ahmed, J. W. Yoon, I. Berry, S. I. Hay, A. Anema, A. J. Tatem, D. MacFadden, M. German, and K. Khan. 2015. Assessment of the potential for international dissemination of Ebola virus via commercial air travel during the 2014 West African outbreak. The Lancet 385(9962):29-35. https://doi.org/10.1016/s0140-6736(14)61828-6.

Bogoch, I. I., O. J. Brady, M. U. G. Kraemer, M. German, M. I. Creatore, M. A. Kulkarni, J. S. Brownstein, S. R. Mekaru, S. I. Hay, E. Groot, A. Watts, and K. Khan. 2016. Anticipating the international spread of Zika virus from Brazil. The Lancet 387(10016):335-336. https://doi.org/10.1016/S0140-6736(16)00080-5.

Bogoch, I. I., A. Watts, A. Thomas-Bachli, C. Huber, M. U. Kraemer, and K. Khan. 2020. Potential for global spread of a novel coronavirus from China. Journal of Travel Medicine 27(2):taaa011. https://doi.org/10.1093/jtm/taaa011.

Carroll, D., S. Morzaria, S. Briand, C. K. Johnson, D. Morens, K. Sumption, O. Tomori, and S. Wacharphaueasadee. 2021. Preventing the next pandemic: The power of a global viral surveillance network. BMJ 372:n485. https://doi.org/10.1136/bmj.n485.

Carroll, L. 2020. More than a third of U.S. healthcare costs go to bureaucracy. Reuters Health. https://www.reuters.com/article/idUSKBN1Z5261 (accessed March 4, 2022).

Carter, D., M. Stojanovic, P. Hachey, K. Fournier, S. Rodier, Y. Wang, and B. de Bruijn. 2020. Global public health surveillance using media reports: Redesigning GPHIN. Studies in Health Technology and Informatics 270:843-847. https://doi.org/10.3233/SHTI200280.

CBP (U.S. Customs and Border Protection). 2014. eAPIS Online Transmission System. https://www.cbp.gov/travel/travel-industry-personnel/apis/eapis-transmission-system (accessed May 10, 2022).

CDC (Centers for Disease Control and Prevention). 2019. National Electronic Health Records Survey public use file national weighted estimates. Atlanta, GA: CDC National Center for Health Statistics. https://www.cdc.gov/nchs/data/nehrs/2019NEHRS-PUF-weighted-estimates-508.pdf (accessed May 19, 2022).

CDC. 2021a. CDC’s COVID-19 data improvement. https://www.cdc.gov/coronavirus/2019-ncov/science/data-improvements.html (accessed March 4, 2022).

CDC. 2021b. Cruise ships: Reporting maritime death or illness (non-gastrointestinal) to DGMQ. https://www.cdc.gov/quarantine/cruise/reporting-deaths-illness/index.html (accessed March 15, 2022).

CDC. 2022a. National wastewater surveillance system (NWSS). https://www.cdc.gov/healthy-water/surveillance/wastewater-surveillance/wastewater-surveillance.html (accessed February 23, 2022).

CDC. 2022b. Technical instructions for CDC’s COVID-19 program for cruise ships operating in U.S. waters. https://www.cdc.gov/quarantine/cruise/management/technical-instructions-for-cruise-ships.html (accessed May 10, 2022).

Channa, A., N. Popescu, J. Skibinska, and R. Burget. 2021. The rise of wearable devices during the COVID-19 pandemic: A systematic review. Sensors 21(17):5787. https://doi.org/10.3390/s21175787.

Chinazzi, M., J. T. Davis, M. Ajelli, C. Gioannini, M. Litvinova, S. Merler, Y. P. A. Pastore, K. Mu, L. Rossi, K. Sun, C. Viboud, X. Xiong, H. Yu, M. E. Halloran, I. M. Longini, Jr., and A. Vespignani. 2020. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368(6489):395-400. https://dx.doi.org/10.1126/science.aba9757.

Chowdhury, M. J. M., M. S. Ferdous, K. Biswas, N. Chowdhury, and V. Muthukkumarasamy. 2020. COVID-19 contact tracing: Challenges and future directions. IEEE Access 8:225703-225729. doi:10.1109/ACCESS.2020.3036718.

CommonWell Health Alliance. n.d. Frequently asked questions. https://www.commonwellalliance.org/about/faq (accessed February 22, 2022).

Dawson, A., M. Verweij, and M. Verweij. 2007. Ethics, prevention, and public health. New York, NY: Oxford University Press on Demand.

Desjardins, M. R. 2020. Syndromic surveillance of COVID-19 using crowdsourced data. The Lancet Regional Health Western Pacific 4:100024. https://doi.org/10.1016/j.lanwpc.2020.100024.

Elmokashfi, A., J. Sundnes, A. Kvalbein, V. Naumova, S. A. Reinemo, P. M. Florvaag, H. K. Stensland, and O. Lysne. 2021. Nationwide rollout reveals efficacy of epidemic control through digital contact tracing. Nature Communications 12(1):5918. https://doi.org/10.1038/s41467-021-26144-8.

Fareed, N., C. M. Swoboda, S. Chen, E. Potter, D. T. Y. Wu, and C. J. Sieck. 2021. U.S. COVID-19 state government public dashboards: An expert review. Applied Clinical Informatics 12(2):208-221. https://doi.org/10.1055/s-0041-1723989.

Federal Register. 2021. Temporary suspension of dogs entering the United States from high risk rabies countries. https://www.govinfo.gov/content/pkg/FR-2021-06-16/pdf/2021-12418.pdf (accessed March 10, 2022).

Ferretti, L., C. Wymant, M. Kendall, L. Zhao, A. Nurtay, L. Abeler-Dörner, M. Parker, D. Bonsall, and C. Fraser. 2020. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368(6491):eabb6936. https://doi.org/10.1126/science.abb6936.

FluTracking. 2022. https://info.flutracking.net (accessed March 10, 2022).

Giovannini, G., H. Haick, and D. Garoli. 2021. Detecting COVID-19 from breath: A game changer for a big challenge. ACS Sensors 6(4):1408-1417. https://doi.org/10.1021/acssensors.1c00312.

Golinelli, D., E. Boetto, G. Carullo, A. G. Nuzzolese, M. P. Landini, and M. P. Fantini. 2020. Adoption of digital technologies in health care during the COVID-19 pandemic: Systematic review of early scientific literature. Journal Medical Internet Research 22(11):e22280. https://doi.org/10.2196/22280.

Greene, S. K., S. F. McGough, G. M. Culp, L. E. Graf, M. Lipsitch, N. A. Menzies, and R. Kahn. 2021. Nowcasting for real-time COVID-19 tracking in New York City: An evaluation using reportable disease data from early in the pandemic. JMIR Public Health and Surveillence 7(1):e25538. https://doi.org/10.2196/25538.

Grekousis, G., and Y. Liu. 2021. Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: A systematic review. Sustainable Cities and Societies 71:102995. https://doi.org/10.1016/j.scs.2021.102995.

GrippeCOVIDnet. 2022. https://www.grippenet.fr (accessed March 10, 2022).

Han, T., H. Cong, Y. Shen, and B. Yu. 2021. Recent advances in detection technologies for COVID-19. Talanta 233:122609. https://doi.org/10.1016/j.talanta.2021.122609.

Harmon, S. A., T. H. Sanford, S. Xu, E. B. Turkbey, H. Roth, Z. Xu, D. Yang, A. Myronenko, V. Anderson, and A. Amalou. 2020. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications 11(1):4080. https://doi.org/10.1038/s41467-020-17971-2.

Hassard, F., L. Lundy, A. C. Singer, J. Grimsley, and M. Di Cesare. 2021. Innovation in wastewater near-source tracking for rapid identification of COVID-19 in schools. The Lancet Microbe 2(1):e4-e5. https://doi.org/10.1016/S2666-5247(20)30193-2.

HealthIT.gov. 2022. Trusted Exchange Framework and Common Agreement (TEFCA). https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement-tefca (accessed February 26, 2022).

HHS (U.S. Department of Health and Human Services). 2021. COVID-19 pandemic response, laboratory data reporting: CARES Act Section 18115. Washington, DC: U.S. Department of Health and Human Services. https://www.hhs.gov/sites/default/files/COVID-19-laboratory-data-reporting-guidance.pdf (accessed February 14, 2022).

HHS and CDC. 2021. Temporary extension & modification of framework for conditional sailing order (CSO) for cruise ships operating or intending to operate in U.S. waters. https://www.federalregister.gov/documents/2021/10/28/2021-23573/temporary-extension-and-modification-of-framework-for-conditional-sailing-order-cso-for-cruise-ships (accessed February 14, 2022).

Hirten, R. P., M. Danieletto, L. Tomalin, K. H. Choi, M. Zweig, E. Golden, S. Kaur, D. Helmus, A. Biello, R. Pyzik, A. Charney, R. Miotto, B. S. Glicksberg, E. P. Bottinger, L. Keefer, M. Suarez-Farinas, G. N. Nadkarni, and Z. A. Fayad. 2021. Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: Observational study. Journal of Medical Internet Research 23(2):e26107. https://preprints.jmir.org/preprint/26107.

Holmgren, A. J., N. C. Apathy, and J. Adler-Milstein. 2020. Barriers to hospital electronic public health reporting and implications for the COVID-19 pandemic. Journal of the American Medicinal Informatics Association 27(8):1306-1309. https://doi.org/10.1093/jamia/ocaa112.

Hswen, Y., E. Yom-Tov, V. Murti, N. Narsing, S. Prasad, G. W. Rutherford, and K. Bib-bins-Domingo. 2022. COVIDseeker: A geospatial temporal surveillance tool. International Journal of Environmental Research and Public Health 19(3):1410. https://doi.org/10.3390/ijerph19031410.

Huang, J., N. R. Shah, and C. J. Wang. In press. Interoperability of healthcare and public health data: Key to future of pandemic preparedness.

Ienca, M., and E. Vayena. 2020. On the responsible use of digital data to tackle the COVID-19 pandemic. Nature Medicine 26(4):463-464. https://doi.org/10.1038/s41591-020-0832-5.

IHE International. 2022. IHE Connectathon: A unique testing opportunity. https://www.ihe.net/participate/connectathon (accessed May 15, 2022).

Ivankovi ć, D., E. Barbazza, V. Bos, Ó. Brito Fernandes, K. Jamieson Gilmore, T. Jansen, P. Kara, N. Larrain, S. Lu, B. Meza-Torres, J. Mulyanto, M. Poldrugovac, A. Rotar, S. Wang, C. Willmington, Y. Yang, Z. Yelgezekova, S. Allin, N. Klazinga, and D. Kringos. 2021. Features constituting actionable COVID-19 dashboards: Descriptive assessment and expert appraisal of 158 public web-based COVID-19 dashboards. Journal of Medical Internet Research 23(2):e25682-e25682. https://doi.org/10.2196/25682.

Jiang, J. Y., Y. Zhou, X. Chen, Y. R. Jhou, L. Zhao, S. Liu, P. C. Yang, J. Ahmar, and W. Wang. 2022. COVID-19 Surveiller: Toward a robust and effective pandemic surveillance system based on social media mining. Philosophical Transactions of the Royal Society A 380(2214):20210125. https://doi.org/10.1098/rsta.2021.0125.

Jose, T., D. O. Warner, J. C. O’Horo, S. G. Peters, R. Chaudhry, M. J. Binnicker, and C. D. Burger. 2021. Digital health surveillance strategies for management of coronavirus disease 2019. Mayo Clinical Proceedings: Innovations, Quality, and Outcomes 5(1):109-117. https://doi.org/10.1016/j.mayocpiqo.2020.12.004.

Kamel Boulos, M. N., and E. M. Geraghty. 2020. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. International Journal of Health Geographics 19(1):8. https://doi.org/10.1186/s12942-020-00202-8.

Kogan, N. E., L. Clemente, P. Liautaud, J. Kaashoek, N. B. Link, A. T. Nguyen, F. S. Lu, P. Huybers, B. Resch, and C. Havas. 2021. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Science Advances 7(10):eabd6989. https://doi.org/10.1126/sciadv.abd6989.

Kpozehouen, E. B., X. Chen, M. Zhu, and C. R. Macintyre. 2020. Using open-source intelligence to detect early signals of COVID-19 in China: Descriptive study. JMIR Public Health and Surveillience 6(3):e18939. https://doi.org/10.2196/18939.

Leung, G. M., and K. Leung. 2020. Crowdsourcing data to mitigate epidemics. The Lancet Digital Health 2(4):e156-e157. https://doi.org/10.1016/S2589-7500(20)30055-8.

Li, H. Y., W. N. Jia, X. Y. Li, L. Zhang, C. Liu, and J. Wu. 2020. Advances in detection of infectious agents by aptamer-based technologies. Emerging Microbes & Infections 9(1):1671-1681. https://doi.org/10.1080/22221751.2020.1792352.

Lippi, G., C. Mattiuzzi, and B. M. Henry. 2021. Are sniffer dogs a reliable approach for diagnosing SARS-CoV-2 infection? Diagnosis 8(4):446-449. https://doi.org/10.1515/dx-2021-0034.

Lo, B., and I. Sim. 2021. Ethical framework for assessing manual and digital contact tracing for COVID-19. Annals of Internal Medicine 174(3):395-400. https://doi.org/10.7326/M20-5834.

Lukose, J., S. Chidangil, and S. D. George. 2021. Optical technologies for the detection of viruses like COVID-19: Progress and prospects. Biosensors and Bioelectronics 178:113004. https://doi.org/10.1016/j.bios.2021.113004.

Mao, Z., H. Yao, Q. Zou, W. Zhang, and Y. Dong. 2021. Digital contact tracing based on a graph database algorithm for emergency management during the COVID-19 epidemic: Case study. JMIR Mhealth Uhealth 9(1):e26836. https://doi.org/10.2196/26836.

Marios Angelopoulos, C., A. Damianou, and V. Katos. 2020. DHP framework: Digital health passports using blockchain—use case on international tourism during the COVID-19 pandemic. arXiv: 2005.08922v2.

Mbunge, E. 2020. Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14(6):1631-1636. https://doi.org/10.1016/j.dsx.2020.08.029.

Mello, M. M., and C. J. Wang. 2020. Ethics and governance for digital disease surveillance. Science 368(6494):951-954. https://doi.org/10.1126/science.abb9045.

Min-Allah, N., B. A. Alahmed, E. M. Albreek, L. S. Alghamdi, D. A. Alawad, A. S. Alharbi, N. Al-Akkas, D. Musleh, and S. Alrashed. 2021. A survey of COVID-19 contact-tracing apps. Computers in Biology and Medicine 137:104787. https://doi.org/10.1016/j.compbiomed.2021.104787.

Moehrke, J. 2013. What is connectathon? Healthcare Exchange Standards. https://healthcaresecprivacy.blogspot.com/2013/11/what-is-connectathon.html (accessed February 26, 2022).

Nanni, M., G. Andrienko, A. L. Barabasi, C. Boldrini, F. Bonchi, C. Cattuto, F. Chiaromonte, G. Comande, M. Conti, M. Cote, F. Dignum, V. Dignum, J. Domingo-Ferrer, P. Ferragina, F. Giannotti, R. Guidotti, D. Helbing, K. Kaski, J. Kertesz, S. Lehmann, B. Lepri, P. Lukowicz, S. Matwin, D. M. Jimenez, A. Monreale, K. Morik, N. Oliver, A. Passarella, A. Passerini, D. Pedreschi, A. Pentland, F. Pianesi, F. Pratesi, S. Rinzivillo, S. Ruggieri, A. Siebes, V. Torra, R. Trasarti, J. V. D. Hoven, and A. Vespignani. 2021. Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. Ethics and Information Technology 23(1):1-6. https://doi.org/10.1007/s10676-020-09572-w.

Noura, M., M. Atiquzzaman, and M. Gaedke. 2018. Interoperability in internet of things: Taxonomies and open challenges. Mobile Networks and Applications 24(3):796-809. https://doi.org/10.1007/s11036-018-1089-9.

Nuffield Council on Bioethics. 2020. Ethical considerations in responding to the COVID-19 pandemic. London, UK: Nuffield Council on Bioethics.

Outbreaks Near Me. 2022. https://outbreaksnearme.org/us/en-U.S. (accessed March 10, 2022).

Peccia, J., A. Zulli, D. E. Brackney, N. D. Grubaugh, E. H. Kaplan, A. Casanovas-Massana, A. I. Ko, A. A. Malik, D. Wang, M. Wang, J. L. Warren, D. M. Weinberger, W. Arnold, and S. B. Omer. 2020. Measurement of SARS-CoV-2 RNA in wastewater tracks community infection dynamics. Nature Biotechnology 38(10):1164-1167. https://doi.org/10.1038/s41587-020-0684-z.

Philo, S. E., E. K. Keim, R. Swanstrom, A. Q. W. Ong, E. A. Burnor, A. L. Kossik, J. C. Harrison, B. A. Demeke, N. A. Zhou, N. K. Beck, J. H. Shirai, and J. S. Meschke. 2021. A comparison of SARS-CoV-2 wastewater concentration methods for environmental surveillance. Science of the Total Environment 760:144215. https://doi.org/10.1016/j.scitotenv.2020.144215.

Polo, D., M. Quintela-Baluja, A. Corbishley, D. L. Jones, A. C. Singer, D. W. Graham, and J. L. Romalde. 2020. Making waves: Wastewater-based epidemiology for COVID-19—approaches and challenges for surveillance and prediction. Water Research 186:116404. https://doi.org/10.1016/j.watres.2020.116404.

Pullano, G., F. Pinotti, E. Valdano, P.-Y. Boëlle, C. Poletto, and V. Colizza. 2020. Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020. Euro-surveillance 25(4):2000057. https://doi.org/10.2807/1560-7917.ES.2020.25.4.2000057.

Rodríguez, P., S. Graña, E. E. Alvarez-León, M. Battaglini, F. J. Darias, M. A. Hernán, R. López, P. Llaneza, M. C. Martín, RadarCOVIDPilot Group, O. Ramirez-Rubio, A. Romaní, B. Suárez-Rodríguez, J. Sánchez-Monedero, A. Arenas, and L. Lacasa. 2021. A population-based controlled experiment assessing the epidemiological impact of digital contact tracing. Nature Communications 12(1):587. https://doi.org/10.1038/s41467-020-20817-6.

Russell, T. W., J. T. Wu, S. Clifford, W. J. Edmunds, A. J. Kucharski, and M. Jit. 2021. Effect of internationally imported cases on internal spread of COVID-19: A mathematical modelling study. The Lancet Public Health 6(1):e12-e20. https://doi.org/10.1016/s2468-2667(20)30263-2.

Russo, M., C. Ciccotti, F. De Alexandris, A. Gjinaj, G. Romaniello, A. Scatorchia, and G. Terranova. 2021. A cross-country comparison of contact-tracing apps during COVID-19. VOXEU CEPR.

Salathe, M., C. Althaus, N. Anderegg, D. Antonioli, T. Ballouz, E. Bugnon, S. Capkun, D. Jackson, S. I. Kim, J. Larus, N. Low, W. Lueks, D. Menges, C. Moullet, M. Payer, J. Riou, T. Stadler, C. Troncoso, E. Vayena, and V. von Wyl. 2020. Early evidence of effectiveness of digital contact tracing for SARS-CoV-2 in Switzerland. Swiss Medical Weekly 150:w20457. https://doi.org/10.4414/smw.2020.20457.

Savona, M. 2020. The saga of the COVID-19 contact tracing apps: Lessons for data governance. SPRU Working Paper Series (SWPS) 2020-10:1-12. https://ssrn.com/abstract=3645073.

Seo, G., G. Lee, M. J. Kim, S.-H. Baek, M. Choi, K. B. Ku, C.-S. Lee, S. Jun, D. Park, and H. G. Kim. 2020. Rapid detection of COVID-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano 14(4):5135-5142.

Sun, K., J. Chen, and C. Viboud. 2020. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: A population-level observational study. Lancet Digital Health 2(4):e201-e208. https://doi.org/10.1016/S2589-7500(20)30026-1.

Sweetapple, C., P. Melville-Shreeve, A. S. Chen, J. M. Grimsley, J. T. Bunce, W. Gaze, S. Fielding, and M. J. Wade. 2022. Building knowledge of university campus population dynamics to enhance near-to-source sewage surveillance for SARS-CoV-2 detection. Science of the Total Environment 806:150406. https://doi.org/10.1016/j.scitotenv.2021.150406.

Thamtono, Y., A. Moa, and C. R. MacIntyre. 2021. Using open-source intelligence to identify early signals of COVID-19 in Indonesia. Western Pacific Surveillence and Response Journal 12(1):40-45. https://doi.org/10.5365/wpsar.2020.11.2.010.

The Lancet Digital Health. 2020. Contact tracing: Digital health on the frontline [Editorial]. The Lancet Digital Health 2(11):e561. https://doi.org/10.1016/S2589-7500(20)30251-X.

Trafton, A. 2021. New face mask prototype can detect COVID-19 infection. MIT News. https://news.mit.edu/2021/face-mask-COVID-19-detection-0628 (accessed February 23, 2022).

Tsao, S. F., H. Chen, T. Tisseverasinghe, Y. Yang, L. Li, and Z. A. Butt. 2021. What social media told us in the time of COVID-19: A scoping review. The Lancet Digital Health 3(3):e175-e194. https://doi.org/10.1016/s2589-7500(20)30315-0.

Verma, J., and A. S. Mishra. 2020. COVID-19 infection: Disease detection and mobile technology. PeerJ 8:e10345. https://doi.org/10.7717/peerj.10345.

Vermicelli, S., L. Cricelli, and M. Grimaldi. 2020. How can crowdsourcing help tackle the COVID-19 pandemic? An explorative overview of innovative collaborative practices. R&D Management 51(2):183-194. https://doi.org/10.1111/radm.12443.

Wang, C. J. 2021. Contact-tracing app curbed the spread of COVID in England and Wales. Nature 594(7863):336-337.

Wells, C. R., P. Sah, S. M. Moghadas, A. Pandey, A. Shoukat, Y. Wang, Z. Wang, L. A. Meyers, B. H. Singer, and A. P. Galvani. 2020. Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak. Proceedings of the National Academy of Sciences of the United States of America 117(13):7504-7509. https://doi.org/10.1073/pnas.2002616117.

Whitelaw, S., M. A. Mamas, E. Topol, and H. G. C. Van Spall. 2020. Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health 2(8):e435-e440. https://doi.org/10.1016/s2589-7500(20)30142-4.

WHO (World Health Organization). 2020. Ethical considerations to guide the use of digital proximity tracking technologies for COVID-19 contact tracing: Interim guidance, 28 May 2020. Geneva, Switzerland: World Health Organization.

Wu, J. T., K. Leung, T. T. Y. Lam, M. Y. Ni, C. K. H. Wong, J. S. M. Peiris, and G. M. Leung. 2021. Nowcasting epidemics of novel pathogens: Lessons from COVID-19. Nature Medicine 27(3):388-395. https://doi.org/10.1038/s41591-021-01278-w.

Wymant, C., L. Ferretti, D. Tsallis, M. Charalambides, L. Abeler-Dorner, D. Bonsall, R. Hinch, M. Kendall, L. Milsom, M. Ayres, C. Holmes, M. Briers, and C. Fraser. 2021. The epidemiological impact of the NHS COVID-19 app. Nature 594(7863):408-412. https://doi.org/10.1038/s41586-021-03606-z.

Zhao, B., M. Kim, and E. W. Nam. 2021. Information disclosure contents of the COVID-19 data dashboard websites for South Korea, China, and Japan: A comparative study. Healthcare 9(11):1487. https://doi.org/10.3390/healthcare9111487.

Zhao, D., F.-S. W. Lee, and C. J. Wang. In press. COVID-19 detection technologies in pandemic control. JAMA.

Zusi, K. 2020, April 19. New technology could provide rapid detection of COVID-19. The Harvard Gazette. https://news.harvard.edu/gazette/story/2020/04/new-technology-could-provide-rapid-detection-of-covid-19 (accessed May 19, 2022).