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Welcome to Global Health Engineering

While the field of Global Health seeks to address the drivers and outcomes of well-being for international populations from a medical or epidemiological perspective, Global Health Engineering addresses determinants of health as a function of engineered interventions and systems. Our work looks at ways of reducing the negative impacts of modern life, particularly in urban areas of over-exploited countries. Our research seeks to reduce the negative impacts of improperly managed human, organic, and anthropogenic waste while ensuring that solutions are affordable, effective, and acceptable to users.

Research

Image of a cube that shows nine thematic focus areas of Global Health Engineering Group. Economics, Engineering, Social Science, Anti-Colonial, Open Data, Failure, Human Waste, Organic Waste, Antropogenic Waste.

Thematically, we are interested in the drivers and barriers to safely manage human waste, organic waste, and anthropogenic waste.

Methodologically, we rely on the fundamental tools of engineering to develop and optimize technology, social science to understand the perceptions and behaviours of populations regarding interventions, and economics to determine financial feasibility and user acceptance.

Cross-cutting all of our work are three core principles: How and why failure has occurred in the past is the foundation of our decision to develop or not develop something new; by adopting open science principles, we are committed to ensuring that our research is reproducible, transparent and reusable for the greatest possible impact. Most importantly, we acknowledge the historical inequalities of international research and strive to understand, implement, and contribute to anti-colonial principles within Global Health Engineering in all of our work.  

Blog & News

Blog: Climate change alone does not cause mass migration

Blog: Empowering Students: The Role of Transparency at Global Health Engineering

Blog: Data for social good, yes and then?: A year at openwashdata as a data scientist

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