Abstract
Background: Surveillance systems with early warning alert and response (EWAR) objectives often integrate diverse data sources, including laboratory surveillance, community reports, and open-source information for robust public health intelligence. While such systems focus on case detection and reporting, contextual insights—such as community behaviours, health system capacity, and environmental conditions—are often overlooked. This study explores the role of contextual data obtained from the World Health Organization’s Epidemic Intelligence from Open Sources (EIOS) system.
Methods: This retrospective analysis assessed contextual insights in EIOS through four outbreak use cases: (1) influenza Type A in Brazil (2021), (2) COVID-19 Omicron variant in Kenya (2021), (3) measles in Afghanistan (2021–2022), and (4) diphtheria in Nigeria (2022–2023). Customized EIOS search boards filtered outbreak-related content based on keywords, geography, and timeframe. Items were triaged, flagged, and qualitatively coded for event and contextual data. Contextual themes were analysed for frequency and trends across each use case outbreak period.
Results: Across all outbreaks, contextual themes were identified, including healthcare infrastructure challenges, vaccine accessibility, mass gathering risks, and public sentiment toward mitigation measures. Public health infrastructure issues, such as hospital bed shortages and vaccine supply gaps, were particularly salient in influenza and COVID-19-related use cases. Vaccine hesitancy, misinformation, and environmental factors (e.g., flooding, earthquakes) were reported less frequently during the outbreak periods. Public health and social measures were also less commonly coded than medical countermeasures, namely, vaccination campaigns across all use cases.
Conclusions: Contextual insights enhance public health intelligence by providing situational awareness for outbreak response. Integrating systematic qualitative coding into surveillance can provide precise risk assessments and inform targeted interventions. These findings support the need for structured guidance and methods related to contextual data extraction surveillance systems and analysis of such complex information to strengthen global health security.