Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts.

ABSTRACT: Despite successes on the Sustainable Development Goals for access to improved water sources and sanitation, many low and middle-income countries (LMICs) continue to struggle with high rates of diarrheal disease. In Guatemala, 98% of water sources are estimated to have E. coli contamination. This project moves toward a novel low-cost approach to bridge the gap between the microbiologic identification of E. coli and the vast impact that this pathogen has on human health within marginalized communities using co-designed community-based tools, low-cost technology, and AI. An agile co-design process was followed with water quality stakeholders, community staff, and local graphic design artists to develop a community water quality education mobile app. A series of alpha- and beta-testers complet

SUBMITTER: Hall-Clifford R 

PROVIDER: S-EPMC10021207 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature