Epidemiologists, public health specialists, data scientists, and professionals with expertise in the various components of digital health were recruited using convenience sampling. To gather a global perspective, potential study participants were recruited from Africa, North America, Oceania, and Europe.

For recruitment of potential participants from Africa, an initial email was sent to the general membership of the pan-African health informatics association called Health Informatics in Africa. For the United States, initial recruitment of study participants involved sending an email to members of the American Medical Informatics Association. Snowball sampling method was also used. The initial email to potential study participants described the study objectives and the role of a potential study participant. Study data were collected and managed using the Research Electronic Data Capture (REDCap; Vanderbilt University) tool hosted at the Children’s Hospital of Philadelphia [15]. The email package also included a link to an electronic questionnaire developed using the REDCap platform. The electronic questionnaire included a consent form that a potential study participant had to agree to before proceeding to answer the questionnaire.

Recruitment of potential study participants was also done at the Medical Informatics Europe 2023 conference held in Göteborg, Sweden. Paper flyers about the study were placed at convenient places throughout the conference venue. In addition to containing details about the study objectives and participant’s role, the paper flyers also included a QR code with a link to the electronic questionnaire developed using REDCap.

Recruitment of study participants continued until data saturation was reached, defined for this study as the point at which additional data no longer generated new insights. As an electronic questionnaire was used for this study, the authors aimed to comply with the Checklist for Reporting Results of Internet E-Surveys [16]. The consolidated criteria for reporting qualitative research were followed for reporting of this study. The completed checklist is provided in Multimedia Appendix 1.

The initial draft of the study questionnaire was designed based on a review of relevant literature. Following an iterative process, the authors developed a study questionnaire aimed at capturing participants’ opinions on effective approaches to improving global health security using digital epidemiology within the context of the current global landscape of digital determinants of health.

A panel consisting of 5 staff members from the Department of Biomedical and Health Informatics at the Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania was consulted to ensure content validity of the study questionnaire. The panel received the initial draft questionnaire via email and could make suggestions for improvement where needed. Their suggestions were discussed and agreed upon among the authors and final updates were made on the questionnaire. The structured questionnaire included both closed-ended (Likert-scale responses) and open-ended questions, allowing for analysis of trends as well as insights into experts’ recommendations. This approach facilitated a comprehensive evaluation of participants’ responses.

Thematic analysis was used to analyze the qualitative responses from the questionnaire, following an iterative process [17]. All textual data were imported into NVivo software (version 12; Lumivero) to facilitate systematic coding and theme identification. Initial codes were generated based on both predefined study objectives and emerging patterns from the data. Through repeated review and refinement by all authors, broader themes and subthemes were developed to capture key insights. Consensus on the final themes was reached through discussion among all authors.

Descriptive statistics were computed to summarize responses. Participants’ responses were analyzed by applying the strengths, weaknesses, opportunities, and threats (SWOT) analysis framework [18] to the digital epidemiology approach. The responses were independently reviewed by 2 coauthors (KLM and BM) and assigned to one of the SWOT categories. Any disagreements or discrepancies in the assignment of responses to SWOT categories were discussed, and consensus on the final categorization of each response was reached by the authors.

This study involved seeking experts’ opinion through the use of an electronic questionnaire—what is commonly classified as a minimal risk study, in which the probability and magnitude of harm or discomfort arising from participation were not greater, in and of themselves, than those ordinarily encountered in daily life or during the performance of routine physical or psychological examination or tests. Nevertheless, ethics approval for the study was obtained from the institutional review board at the Children’s Hospital of Philadelphia (institutional review board protocol number: 22-020667). Participants received the survey electronically using REDCap, and consent to participate was obtained from all participants through a consent form embedded in the electronic questionnaire (REDCap). Only participants who provided consent were able to proceed and complete the questionnaire. Participants’ privacy and confidentiality were ensured throughout the study. REDCap is a secure, web-based platform designed to support electronic data capture for research and operational projects, and it is compliant with the Health Insurance Portability and Accountability Act and the General Data Protection Regulation. The study did not collect any personally identifiable information. All data collected were anonymized and reported in aggregate form only. Participants did not receive any compensation for taking part in the study.

This qualitative study gathered insights from 22 experts (Table 1). Although the sample size may be considered relatively small, it is appropriate for the exploratory nature of the research and emphasizes achieving information power [19]. This approach provides a rich, in-depth, context-specific preliminary evidence rather than statistical generalization. In qualitative research designed to identify patterns across data, recruitment of between 15 and 30 participants has been commonly found to be adequate [20].

Digital epidemiology is a relatively new field that has grown rapidly in recent years, with its humble beginnings traced back to when researchers began mining the increasing amount of internet data for epidemiological purposes [21]. The growth of the field, and thus increased awareness, has been fueled by the growing utility of new data sources and artificial intelligence (AI) in epidemiology [22]. The ad hoc use of digital epidemiology methodologies and techniques during the COVID-19 pandemic likely contributed to the heightened awareness of the field [23]. This may explain why most study participants, especially in light of their professional backgrounds, reported being aware of the concept of digital epidemiology (Table 2). However, it is important to note that the attitude of stakeholders, rather than their awareness, may play a more crucial role in driving sustainable use of digital epidemiology. The technology acceptance model features prominently among the methodologies used to measure attitudes toward technology adoption [24,25]. According to the technology acceptance model, perceived usefulness (PU) is characterized by an individual’s belief that using technology improves their job performance, while perceived ease of use (PEOU) refers to their belief that using technology requires minimal effort [24]. Both PU and PEOU are primary drivers that influence attitudes toward using technology [25]. Most study participants agreed that digital data can enhance epidemiological purposes (PU), that digital epidemiology can be effectively applied at a global level toward coordinated global health surveillance (PU), and that digital epidemiology is reliable (PU+PEOU). This is illustrated in Table 4 and reflects participants’ positive attitudes toward digital epidemiology, particularly regarding its potential impact and effectiveness in improving global health security.

The Riyadh Declaration on digital health is a global call to action promoting the development of infrastructure and best practices necessary for sharing high-quality, real-time data both locally and globally in the fight against pandemics. Its goal is to enable health systems to access actionable information that can inform timely responses to public health threats [26]. Similarly, the US Centers for Disease Control and Prevention launched the Data Modernization Initiative to enhance the quality, availability, and use of data related to pandemics and public health emergencies [27]. Together, these frameworks serve to guide the integration of digital innovations into national and global health systems as part of coordinated efforts to strengthen preparedness and response to future pandemics. A shared theme emphasized across these frameworks is that leveraging digital epidemiology to address global health threats requires a more robust and integrated global health architecture that is capable of better preparing local, national, regional, and global public health partners to predict, prevent, detect, assess, and respond effectively to emerging threats. This calls for intentional efforts by governments and public health authorities to mitigate digital exclusion at all levels (global, regional, national, and local) and to address data governance and interoperability challenges inherent in using and sharing diverse data sources [28].

Privacy and security, along with ethical and legal considerations, were ranked by most experts as high priority areas that must be considered by decision-makers and implementers to ensure sustainable integration of digital epidemiology tools in preparedness and response to future pandemics (Figure 1). Digital epidemiology, as previously explained, makes effective use of data not originally intended for epidemiological purposes. Examples of such data include internet searches, social media posts, webpage access logs, mobile phone network data, mobile health apps, wearable devices, sensor generated data, call center records, health and patient registries, and environmental monitoring data [21,23]. As some of these data are generated outside traditional public health systems and those involved in generating such data may not have consented to or even be aware of their use for epidemiological purposes, indeed this raises legitimate concerns related to potential data privacy breaches, security vulnerabilities, and ethico-legal issues. The pertinent literature suggests that data on one’s health status have the potential to be reused by third parties to discriminate and restrict individual rights, and that institutional and government access to personal data (eg, geospatial data) could inhibit the exercise of basic freedoms, if individuals feel watched as to what they do or with whom they spend time [29]. In the context of the existing global digital divide and digital access gap, reliance on digital epidemiology to drive pandemic preparedness and response strategies may inadvertently perpetuate existing health disparities, benefiting overrepresented populations in the data, while not providing gains to and even possibly harming populations whose data are underrepresented [30]. In light of the current fragmented regulatory landscape and in the absence of a common set of principles on data privacy, security, and ethics, a global coordinated approach is required to establish frameworks that will ensure safe and responsible use of digital epidemiology as part of a unified global effort toward future pandemic preparedness and response [31]. Findings from a study based on the unified theory of acceptance and use of technology framework indicated that cost did not moderate behavioral intention to adopt the technology [32]. This may help explain why, in this study, experts ranked cost and economic aspects lower among the key readiness factors for the adoption of digital epidemiology (Figure 1).

The WHO’s Global Strategy on Digital Health 2020 to 2025 provides a structured road map to guide member states in systematically integrating digital health technologies into their national health systems. According to WHO, the vision of the global strategy is to improve health for everyone, everywhere by accelerating the development and adoption of appropriate, accessible, affordable, scalable, and sustainable person-centric digital health solutions to prevent, detect, and respond to epidemics and pandemics [33]. Existence of such a document is indeed a strength in creating an enabling environment for the use of digital epidemiology in future pandemic preparedness and response. Increasing investments in digital technologies by countries and existence of digital health strategies by countries (Table 5) are part of the strategic objectives advanced by the document. The 4 strategic objectives outlined in the Global Strategy on Digital Health are as follows: (1) promote global collaboration and advance the transfer of knowledge on digital health, (2) advance the implementation of national digital health strategies, (3) strengthen governance for digital health at global, regional, and national levels, and (4) advocate people-centered health systems that are enabled by digital health [32]. These strategic objectives align with concerns highlighted as weaknesses by experts in Table 5. In addition to data privacy, security, and ethics already discussed, experts identified sociopolitical challenges and economic constraints as potential threats to a global approach to digital epidemiology (Table 5). This aligns with the study by Davis [34], which discusses the political determinants of digital health, defining them as the intersecting areas of national and international law and policy, local and global governance, civic engagement, and the influence of transnational commercial interests. The author explains that these factors collectively shape access to, uptake of, and the design of digital health systems [34]. As a case point, global geopolitical tensions and varying digital health maturity levels of nations can significantly impact data sharing in public health, resulting in lapses in global health security.

The full potential of digital epidemiology in achieving coordinated pandemic preparedness and response can indeed be leveraged through advancements in global design and international collaborations on digital health–related issues (Table 5). Opportunities exist in open health data standards [35], which promote interoperability and enable effective data sharing across diverse settings. The WHO’s extension of the Global Strategy on Digital Health 2025 to 2027 endeavors to provide critical guidance on AI in health, including the ethics and governance of AI for health, and aims to support member states through global workshops on ethical AI implementation [36] and the code of ethics for health information professionals of the International Medical Informatics Association [37], are but examples of opportunities that offer a pathway toward safe and responsible use of digital epidemiology as part of a unified global effort for future pandemic preparedness and response. As previously explained, digital epidemiology uses data sources beyond the traditional health sector to gain insight into disease patterns and trends. As such, a significant opportunity exists in the continued efforts by global health actors to engage representation from nonhealth sectors in plans for future pandemic preparedness and response (Table 5). These sentiments are emphasized in the WHO Pandemic Agreement which sets out principles, approaches, and tools for better international coordination across a range of areas to strengthen the global health architecture for pandemic prevention, preparedness, and response [38]. The WHO Pandemic Agreement perhaps holds great promise toward significantly contributing to the creation of an enabling environment for the effective role of digital epidemiology in future pandemic preparedness and response efforts.

This study is subject to limitations inherent in its design. The use of convenience and purposive sampling methods limits the generalizability of the findings. The sample may have excluded critical perspectives from underrepresented geographic regions or professional backgrounds. For instance, within the North American region, participants from the United States were heavily overrepresented, with only 1 participant from Canada, thereby excluding perspectives from other countries in the region. The composition of professional backgrounds represented in the sample may have influenced participant responses, potentially skewing perceptions toward more favorable views of digital epidemiology. These notable imbalances may have constrained the diversity and balance of expert opinions captured.

This study explored experts’ perspectives on the potential, readiness factors, and challenges associated with integrating digital epidemiology into global pandemic preparedness and response strategies. The findings underscore a growing recognition of digital epidemiology as a critical tool for enhancing global health security, particularly using nontraditional data sources and emerging technologies such as AI. Participants demonstrated high awareness and positive attitudes toward the utility and applicability of digital epidemiology, especially in advancing coordinated global health surveillance. However, the study also identified key barriers that must be addressed to ensure sustainable integration of digital epidemiology into health systems. These barriers include concerns related to data privacy, security, ethics, sociopolitical influences, and digital inequities across and within countries. The study affirms the need for a globally coordinated approach to governance, regulation, and investment in digital health infrastructure. By highlighting the complex interplay between technological, institutional, political, and social determinants of digital epidemiology, this research contributes important insights into how global health actors can leverage digital innovations responsibly and effectively for epidemiological purposes. Future research directions should focus on developing and validating context-specific frameworks that offer clear guidance for the responsible and sustainable integration of digital epidemiology into existing health systems, to support preparedness for and response to future pandemics.