Community-based surveillance systems for emerging infectious diseases: A qualitative assessment of effectiveness in early detection and reporting

Community-based surveillance (CBS) engages lay community members and local health workers in identifying and reporting priority health events, thereby complementing formal facility-based systems and improving early outbreak detection. Evidence from Africa, Asia, and high-income countries shows that CBS improves timeliness and volume of signal reporting for priority infectious diseases and emerging threats, although performance is highly context-dependent. CBS has detected outbreaks several days to weeks earlier than routine systems and increased reporting of suspected measles, yellow fever, cholera, and other syndromic events. Drivers of effectiveness include community trust, simple case definitions, strong supervision, feedback, and digital tools (SMS, smartphone apps, mHealth platforms) that support near real-time data flows. Key challenges are incomplete coverage, reporting fatigue, weak integration with national systems, high false-positive rates, sustainability constraints, and ethical and governance concerns, particularly with digital and AI-augmented surveillance. Using a qualitative synthesis of published evidence up to 2023, this study assesses how community surveillance mechanisms contribute to early detection and reporting of infectious diseases, identifies success factors and barriers, and proposes recommendations for strengthening people-centred, ethically robust CBS for emerging infectious diseases.