Researchers at the Institute for Global Nutrition are increasingly leveraging machine learning and advanced modeling to improve dietary assessment and understanding of micronutrient adequacy across populations. These approaches allow investigators to estimate micronutrient gaps, assess population-level adequacy, and evaluate the potential impact of interventions such as fortification, supplementation, and food system changes. Together, these innovations strengthen the policy relevance of global nutrition research, supporting more targeted and effective strategies to address micronutrient deficiencies and malnutrition worldwide.
DIGEST: Dietary Intake General Estimation and Statistical Toolkit
DIGEST is a free, open-source R toolkit under development at the UC Davis Institute for Global Nutrition, in collaboration with the International Food Policy Research Institute (IFPRI), that makes advanced analyses of usual dietary intake accessible to researchers, program implementers, and policy makers worldwide.
Plant Village Food AI Dietary Assessment
An innovative approach to dietary assessment by leveraging artificial intelligence (AI) to provide real-time dietary intake data.
Proxy Modeling Project to Estimate Vitamin and Mineral Deficiency Prevalence
Researchers aim to catalyze and facilitate the development of new predictive models of vitamin and mineral deficiency (VMD) prevalence as proxies for biomarker-based data on micronutrient deficiencies.
MINIMOD
The MINIMOD project aims to develop and use a suite of tools that allows decision-makers to design and manage more effective, cost-effective, and sustainable national and sub-national micronutrient intervention programs.