Digital Epidemiology in Low-Resource Settings: Bridging Promise and Reality

Abstract

Digital epidemiology, which applies digital data sources, technologies, and analytics to understand and monitor disease patterns, has become an essential component of modern public health surveillance. The integration of mobile technologies, social media, and artificial intelligence has enhanced the ability to detect, predict, and respond to outbreaks in real time. This narrative review synthesizes recent literature to examine the evolution, adoption, and implementation of digital epidemiology in low-resource settings (LRS), where limited infrastructure, laboratory capacity, and data availability often impede surveillance efforts. Examples such as mTrac, DHIS2, and AfyaData demonstrate how participatory surveillance, SMS-based reporting, and open-source platforms have improved data collection and response capacity. Nonetheless, persistent challenges—including data bias, funding sustainability, and governance limitations—continue to restrict progress. To address these gaps, the paper outlines strategic recommendations focused on strengthening digital literacy, ethical data governance, local ownership, and sustainable policy frameworks. The review concludes that advancing digital epidemiology in resource-limited contexts requires context-specific, equitable, and resilient approaches supported by multidisciplinary collaboration among health professionals, technologists, policymakers, and communities.