Digital healthcare tools for the world’s poorest people and places: A new framework for syndromic surveillance forged in the fight against COVID-19 in Somalia. (Preprint)

UNSTRUCTURED Low- and middle-income (LMIC) countries often lack capacity for infectious disease surveillance. The COVID-19 pandemic has challenged many of them to implement non-pharmaceutical interventions while preparing to distribute vaccines. Because of the rapidly evolving situation in many LMICs, real-time data is crucial to understand the needs of the population, and to provide supportive evidence for healthcare interventions. In this paper, we highlight the role of international partners in a data collection effort (79,746 individuals surveyed over six weeks, beginning in April, 2020,) led by the municipal government in Mogadishu, Somalia, where health workers and officials are battling COVID-19 alongside numerous other communicable diseases, as well as violent extremism, and natural disasters. This effort united a collaboration between Canadian and Somalian digital health information engineers in developing a freely-accessible open source digital survey tool for syndromic surveillance. The project enabled extensive health data collection and sharing. It also spurred the rapid development of locally-customized digital solutions, and the training of local digital health engineers. The research insights gathered from this project have been instrumental in the implementation of non-pharmaceutical interventions and in health and sanitation resource allocation in Mogadishu, while providing an updated perspective of the socio-demographics of the population of a city that has not had a census since 1975. Nine collaborative principles are elucidated for developing digital survey tools for syndromic surveillance. The project offers a model for replication in other LMICs.