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Objective: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. Approach: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. Conclusion: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance. [ABSTRACT FROM AUTHOR]

• Trellis network data collection platform enables name disambiguation. • Trellis network data collection platform enables multiple language interviews. • Trellis report allows for real-time monitoring of data collection. • Trellis network data collection platform can be used offline in remote areas. Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis ' ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location. [ABSTRACT FROM AUTHOR]

We analyze the competition issues in mobile money in Kenya–where Safaricom is the market leader–and in Uganda: where the market is duopolistic. We evaluate the multi-sided platform characteristics that have underpinned the rapid growth of the lead mobile money platforms and that also imply that the market leaders have substantial market power and can act as gatekeepers. The conduct of the lead firms is assessed along with competition measures and regulatory interventions made in each country. The analysis highlights the importance of–and challenges for–competition enforcement in developing countries to ensure effective rivalry while realizing the gains from innovation in digital platforms. [ABSTRACT FROM AUTHOR]

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