Cyber-epidemiology and the surveillance exclusion triad: Structural invisibility in global mental health data systems

Abstract

Digital mental health surveillance promises to guide resource allocation and policy in low-and middle-income countries, yet its architectures can systematically exclude entire regions, rendering populations invisible. This Perspective introduces cyber-epidemiology as a critical framework for examining how digital surveillance systems shape population visibility and produce structural inequities. Using the WHO World Mental Health International College Student (WMH-ICS) Initiative as a sentinel case, we identify the absence of West African countries from published participation reports despite more than 100,000 students being surveyed globally. This exclusion is not merely a data gap but generates what we term the Surveillance Exclusion Triad: epidemiological distortion (biased global prevalence estimates), resource allocation distortion (funding flows that reinforce existing infrastructure) and policy translation distortion (interventions lacking contextual validity). These distortions create self-reinforcing cycles of invisibility, underfunding and neglect. We argue that cyber-epidemiology must treat absence as data, interrogate platform architectures for exclusionary design and triangulate multiple data sources to make invisible populations visible. The implications extend beyond surveillance into Population Health Intelligence; the systematic conversion of population-level data into actionable health system intelligence, where representational inequities propagate into prediction, prioritisation and policy decisions. Without deliberate attention to who is counted and who is excluded, digital transformation risks deepening the very health inequities it aims to address. We propose five principles and an operational framework to advance more equitable global mental health surveillance.