A framework for grassroots research collaboration in machine learning and global health
Abstract
Traditional top-down approaches for global health have historically failed to
achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015). However, recently, a more holistic, multi-level approach, One Health (OH) (Osterhaus
et al., 2020), is being adopted. Several challenges have been identified for the
implementation of OH (dos S. Ribeiro et al., 2019), including policy and funding,
education and training, and multi-actor, multi-domain, and multi-level collaborations. This is despite the increasing accessibility to knowledge and digital research
tools through the internet. To address some of these challenges, we propose a general framework for grassroots community-based means of participatory research.
Additionally, we present a specific roadmap to create a Machine Learning for
Global Health community in Africa. The proposed framework aims to enable any
small group of individuals with scarce resources to build and sustain an online
community within approximately two years. We provide a discussion of the potential impact of the proposed framework on global health research collaborations.
Research Areas
