# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 2 Disease Surveillance  
Surveillance is already a core function of both public health and global health practice. It is defined by the World Health Organization (WHO) as the \"continuing scrutiny of all aspects of the occurrence and spread of disease that are pertinent to effective control\". It is characterised by \"methods distinguished by their practicability, uniformity, and frequently by their rapidity, rather than complete accuracy\" [8]. The enabling components of surveillance systems may include laboratory diagnostics to detect or confirm health conditions; information technologies to support the surveillance processes of data collection, analysis, and dissemination; clinician consultation and reporting; public health education and training; and legislation, regulations, and policies that support the conduct of surveillance and response. Communicable disease surveillance can operate at international, national, state, and local levels. The primary responsibility for public health action often lies with state and national health departments or public health agencies (e.g., Public Health England in the UK). For the purposes of this paper, WHO [9] has defined a disease epidemic as \"the occurrence of cases of disease in excess of what would normally be expected in a defined community, geographical area, or season\". In practice, infectious disease data collection and analysis is complicated and encompasses a multi-stage process with several stakeholders across various organizational boundaries [10]. The main objective of infectious disease surveillance is to identify changes in incidence, either in the form of an acute outbreak (e.g., COVID-19) or a change in long-term trends [11]. Disease surveillance intelligence includes activities related to the prompt identification of potential health hazards and their verification, assessment, and investigation to enable public health control recommendations [12]. The 2005 International Health Regulations (IHR) are designed to ensure the timely recognition of outbreaks of infectious disease with the potential to spread widely [13]. The IHR 2005 also serves as a foundation for the Global Health Security (GHS) agenda [14]. The GHS agenda is \"an effort by nations, international organizations, and civil society to accelerate progress toward a world safe and secure from infectious disease threats; to promote global health security as an international priority; and to spur progress toward full implementation of the IHR\". They require WHO member nations to report outbreaks of international concern to the WHO within 24 h of discovery [15]. For case-based surveillance, member states can report using the WHO outbreak toolkit [16] to develop an initial case report. For example, consistent with the IHR, during the initial months of the H1N1 pandemic in 2009, the WHO requested that countries report the initial cases and, thereafter, the number of confirmed cases and deaths throughout the H1N1 pandemic. The resulting database represented one of the most comprehensive and timely outbreak reporting databases available to the public on the Internet at the time [13]. By contrast, the overwhelming human resource demands to effectively undertake COVID-19 data collection and analysis over the initial months of the pandemic swiftly required a shift from case-based reporting to aggregate and accelerated forms of reporting [17]. A critical component of disease surveillance which is growing in importance due to the availability of big data is 'epidemic intelligence' [18; 19]. Epidemic intelligence incorporates two components: an indicator-based component and an event-based component [9; 12; 20; 21]. The goal of indicator-based surveillance is to find increased numbers or clusters at a specific time, period, and/or geolocation that may indicate a threat [22].