Exploring the use of big data in risk communication: a study of Uganda’s response to COVID-19

Abstract

This study explores the use of big data in public health risk communication, with a specific focus on COVID-19 in Uganda. Using the Ministry of Health (MoH), particularly the Health Information Management Division (HIMD), as a case study, the research investigates how data-driven insights informed the design and dissemination of communication messages during the pandemic’s resurgence phase. The resurgence of COVID-19 after initial containment raised questions about the design of earlier public health messages and the extent to which big data influenced their refinement. The study’s objectives were threefold: (1) to identify the big data tools and technologies used in risk communication by the Ministry of Health’s HIMD during the COVID-19 pandemic. (2) to analyse the extent to which big data is used by the Ministry of Health in designing messages against COVID-19; and (3) to establish the challenges of big data use as a tool in communicating health risk messages. Guided by the perception/outrage model of public health risk communication and framed within the Unified Theory of Acceptance and Use of Technology (UTAUT), the study employed a qualitative single-case research design. Data was collected through semi-structured, in-depth interviews with purposively selected participants from the HIMD and MoH communications teams and analysed using Yin’s (2015) thematic analysis framework. Findings reveal that the MoH utilised big data from diverse sources, including health surveillance systems, digital platforms, and community feedback mechanisms, to develop targeted, multi-platform messages aimed at improving public compliance with preventive guidelines. Despite the benefits, the study also highlights challenges such as limited data infrastructure, technical capacity gaps, and concerns around data integration and real-time use. These insights underscore the potential of big data as a critical enabler in designing effective, responsive, and adaptive public health risk communication strategies. The messages, which included text, videos, and fear-arousing content, were designed to motivate compliance with COVID-19 prevention measures. These messages, however, did not influence illiterate Ugandans and those living in far remote areas. The Ministry of Health did not design interpersonal communication messages to avoid contradicting some prevention guidelines, but these messages would have positively influenced the Ugandans identified above, especially after the lockdown. Indeed, the same messages had been applied to prevent COVID-19 resurgence. Big data was utilised to update the Ministry of Health's message designs based on the current state of COVID-19, identify COVID-19 hotspots for targeted messaging, and evaluate public feedback. However, the use of big data was limited by the shortcomings of MySQL, which was the only big data tool employed by the HIMD during the peak of COVID-19. Although other big data tools