Fourie et al

Global health seeks to understand and accommodate the complex systems of our planet-wide society as they relate to health(Salm et al., 2021). Traditionally, global health institutions (e.g. WHO) have, in a top-down manner, negotiated with the highest levels of government across our world’s nations toward aligned health policy (Salm et al., 2021). Unfortunately, top-down approaches have produced mixed results. While they succeed shaping in economic matters, they consistently fail to achieve social progress (Hoffman & Røttingen, 2015; Hoffman et al., 2015).

Recently, such institutions have taken steps towards a more holistic, multi-level approach under the banner of the One Health (OH) approach (Osterhaus et al., 2020). The OH approach strives to mobilize multiple sectors, disciplines, and communities at varying levels of society to work together to foster well-being and tackle threats to health and ecosystems (Adisasmito et al., 2022), with the inclusion of digital health(Benis et al., 2021; Ho, 2022).

However, there remain challenges regarding the implementation of OH. Key challenges include policy and funding, education and training, as well as multi-actor, multi-domain and multi-level collaborations (dos S. Ribeiro et al., 2019). This is despite the increasing accessibility to knowledge and digital research tools through the internet.

To tackle those key challenges in Global Health (and more specifically in Global Health and Machine Learning), we propose a bottom-up or grassroots community based means of participatory research to bring together researchers from varying parts of society. Participatory research, unlike conventional research, emphasizes the value of research partners in the knowledge-production pro-cess where the research process itself is defined collaboratively and iteratively (English et al., 2018).

In this work, we review some existing Grassroots Participatory Communities (GPCs) and propose a Grassroots Participatory Community Framework summarising the participatory approaches by Machine Learning communities. We intend this framework to enable any small group of individuals, with scarce resources, to build and sustain an online community within the space of 2 or so years. Under this framework, we provide a recommended roadmap to create a machine learning for global health community in Africa (ML4GHA) GPC, as a means to alleviate some of the problems highlighted in implementing OH.