Public health surveillance has been defined as “the ongoing, systematic collection, analysis and interpretation of health-related data with the a priori purpose of preventing or controlling disease or injury and identifying unusual events of public health importance, followed by the dissemination and use of such information for public health action” (1). Public health surveillance data are often collected without requiring individual patient consent. This practice is ethically and legally justified as a part of a government’s responsibility to protect the public’s health (2) and as a basic interest of persons in a pluralistic society (3). These justifications are tempered by the state’s responsibility to use data for public health purposes only, engage stakeholders, and ensure protection of personal information.

Healthcare professionals are traditionally mandated to notify public health authorities about cases of specified diseases within a certain timeframe. The authorities then analyze the data and take appropriate action. Surveillance systems therefore tend to be the responsibility of the government. Most countries provide routine surveillance data to multilateral agencies (4), which analyze and disseminate information on disease trends at the regional or global level. These agencies also receive data from countries when the impact of a public health event crosses national borders, a standard of practice codified by the 2005 International Health Regulations (IHR 2005) (5), the international legal instrument aimed at assisting the global community to prevent and respond to public health threats that have the potential to affect populations worldwide.

Public health authorities increasingly complement notifications with laboratory data (6), although in practice, this practice is often limited to high-income countries because it requires considerable laboratory capacity and advanced information technology infrastructure. Syndromic surveillance, in which health-related data such as the number of consultations for a specific diagnosis are reported, is used in high- and low-income settings. In some low-income settings, nongovernment actors, such as nongovernmental organizations (NGOs), academic institutions, private companies, or foreign medical teams, sometimes fill surveillance gaps (7), in particular during public health emergencies in which temporary, early warning surveillance systems based on syndromic surveillance are deployed in response to an increased outbreak risk (8).

Increasingly, online data not necessarily collected with an a priori health objective are used for public health purposes. Online technologies that provide data for disease or event detection are known as digital disease detection (DDD) systems. Data sources include search engine and social media queries (9,10), crowd-sourced event-based information queries (11), machine learning (12), natural language processing, and geolocalization (13). In 2014, DDD identified the resurgence of poliomyelitis and the Ebola outbreak in West Africa before the World Health Organization (WHO) officially reported them (14), 2 events WHO later declared as Public Health Events of International Concern. As DDD data quality and accuracy improve, DDD will likely assume a more prominent role, particularly in settings where the infrastructure to support public health surveillance systems is lacking (10). However, DDD needs more systematic integration into the formal, government-owned surveillance landscape as well as ties to response mechanisms to maximize its potential (10,15). The systematic use of nongovernment, informal surveillance systems is beginning to gain traction with surveillance systems such as the WHO’s new Epidemic Intelligence from Open Sources, an event-based system receiving alerts from a range of informal sources, planned for launch in the near future (16). DDD data raise new ethical and legal challenges that need to be addressed as they become integrated into conventional surveillance systems (17). These data also create competition for resources or the generation of data that are not consistent with data from conventional surveillance systems; these factors can give rise to trust and acceptability issues. Nevertheless, evidence for the added value of these data is building, and they are increasingly being incorporated into conventional surveillance systems.

Public health surveillance data require timely sharing to ensure more coordinated and effective risk management for public health response (18). Sharing public health surveillance data between countries improves capacity for disease detection and response (19) and can help identify an outbreak source when national-level data cannot (20). This sharing can also reduce the potential or actual impact of a global health crisis. For example, the Global Influenza Surveillance and Response System, a laboratory network that shares information to detect the emergence of novel influenza viruses with pandemic potential (21), helped prevent SARS from becoming endemic after the 2003 outbreak (22). This network also improved the timeliness of the response to the 2009 influenza A(H1N1) pandemic (21). Besides being useful for outbreak management, sharing of routine public health surveillance data enables national and international collaboration, capacity strengthening, insight into public health system performance, and ultimately better control of infectious diseases (18).

Real or perceived risks, in particular those risks linked to travel and trade restrictions, can lead to a reluctance by governments to share data, leading to adverse public health and economic consequences. The 2003 SARS outbreak cost an estimated US $40–$80 billion to the global economy, with travel and tourism industries badly affected (23). China’s delay in sharing information about the 2003 SARS outbreak contributed to the disease’s spread and the delayed global response (24) as well as economic and reputation damage to China (25). With Middle East respiratory syndrome, the incomplete assessment of the disease origin and source has largely been attributed to a reluctance to share public health surveillance data in a timely fashion (26). Sharing of data helps achieve appropriate public health action while limiting risks to travel and trade. IHR 2005 is designed to ensure maximum public health benefit while keeping restrictions to a minimum (5). Nevertheless, public health surveillance data are not always freely shared because of perceived or real technical, political, economic, motivational, ethical, and legal barriers (27). Sharing public health surveillance data must become the norm.

Figure

Figure. The global public health surveillance landscape, 2018.

Government actors implement most conventional public health surveillance systems and generate most data and can be complemented by nongovernment actors. In addition to having value at the national level, a country’s routine public health surveillance data enable multilateral organizations to generate intelligence on specific diseases at the regional and global level. These organizations provide standards and advice on data sharing to facilitate the process by individual countries and conduct their own surveillance activities (28); examples include the WHO’s global measles surveillance system (4) and the European Union’s surveillance system (TESSy), which has standardized surveillance across the European Union (29). Such supranational systems come with their own challenges, such as the additional burden placed on individual countries to report data already analyzed nationally and the difficulties associated with comparing different types of data resulting from surveillance systems with different national legal bases. Institutions that do not generate data themselves but seek to reuse data for academic or public health purposes are also part of this data-sharing landscape (Figure).

Stakeholders can be divided into 3 groups that need to be engaged for optimal data sharing: 1) data providers, who generate public health surveillance data either from the community, the healthcare system, or nonhealth sources; 2) data recipients, who interpret and use data generated by others; and 3) data sharing facilitators, those who make sharing between data providers and recipients possible.

Individual stakeholders can commonly belong to >1 group at a time and can assume a different role in different situations. For example, a country might provide surveillance data to a multilateral agency and receive data from a neighboring country simultaneously; alternatively, a multilateral agency might provide data sharing guidelines to countries, acting as facilitator, while also receiving and analyzing event-based data.

Disease outbreaks caused by Ebola virus (2014–2016) and Zika virus (2016), among others, have increased awareness of the importance of data sharing among global health stakeholders. In addition to several other recent calls to share research data during emergencies (30,31), in 2016, a group of ≈40 international public health leaders published a statement calling on stakeholders to share “all public health surveillance data, as necessary to improve and protect public health” (18; Technical Appendix). This statement further outlined that public health surveillance data sharing should be the norm, rather than the exception, with public health surveillance data made accessible in a timely manner while taking appropriate steps to safeguard the privacy of individuals and other legitimate public interests. Accordingly, the statement asked stakeholders to commit to 1) sharing public health surveillance data by default when a public health need is identified; 2) using public health surveillance data responsibly, with the intention of protecting and improving the health of the population; 3) making the benefits explicit; 4) ensuring that public health surveillance data are shared with as few restrictions as possible and in an ethical way.

Formulating principles to promote and facilitate data sharing in public health is not a new concept. An example is the Pandemic Influenza Preparedness Framework (32). This guidance document is restricted to 1 disease and resulted only after extensive negotiations (32). In addition to such international efforts, individual funders often have specific data-sharing policies (33).

The principles we outline here are the result of a consultation process with ≈100 experts, including those from the fields of public health, law, ethics, politics, and data sharing, including experts from WHO, the World Organisation for Animal Health (OIE), and the Food and Agriculture Organization of the United Nations. The consultation was convened by the Chatham House Centre on Global Health Security (34). The principles form the basis of a guide to sharing public health surveillance data and benefits. This guide, available in hard copy and as an online interactive tool (https://datasharing.chathamhouse.org), addresses perceived and real barriers and is intended to facilitate equitable sharing of public health surveillance data and benefits. Equitable in this context is taken to mean that data and benefits are shared among stakeholders according to individual, organizational, and public health needs. The guide enables sharing without the need for prolonged negotiation by creating an environment conducive to sharing data and achieving good practice.

This approach is governed by 7 principles. Each incorporates the ethical concepts most relevant to data sharing: social beneficence, respect, justice, and transparency. Those principles also strive to ensure that sharing data when a need is identified leads to equitable sharing of public health benefits and capacity-building where necessary and appropriate. This component is particularly important when the parties sharing the data have different capacities to benefit because of unequal resources. The principles encourage parties who are better resourced to ensure that others benefit from the process according to need.

Sharing surveillance data improves public health. We propose an approach to data sharing that creates an environment conducive to sharing, encourages good practice, and ensures that the benefits derived from the sharing process are equitably distributed.

The public health surveillance landscape is complex, with a range of government and nongovernment stakeholders who can provide and receive data as well as facilitate sharing. Optimal sharing requires an understanding of the roles and responsibilities of these stakeholders. The 7 principles for public health surveillance data sharing we propose address good practice for the sharing of public health surveillance data. Those principles serve as the basis for comprehensive guidance with actionable recommendations for all stakeholders. The complete guidance is available online (https://datasharing.chathamhouse.org).

Sharing of public health surveillance data is best done with an agreement that takes into account those principles, which will help to ensure that data are shared optimally and ethically, while fulfilling the expectations of stakeholders and facilitating equitable distribution of benefits. We encourage stakeholders, and in particular multilateral organizations, to consider these principles when strengthening frameworks and capacity for data sharing.