The Stanford University (SU) School of Medicine “Co-Creation” group will employ a mixed quantitative and qualitative methods approach to analyze/mine existing data sets, dialogue with implementers and evaluators, and share the knowledge and data gained from the Bill and Melinda Gates Foundation (BMGF)-funded Ananya program in Bihar, India. SU will conduct this analysis to disseminate learning from Ananya to inform the scale-up of national and global family health (reproductive, maternal, newborn and child health and nutrition, RMNCHN) interventions.
There is a rising epidemic of autism around the world that now affects an estimated 1 in 68 children in the United States, with similar prevalence rates found in many countries worldwide. Multiple barriers exist to identification and treatment of at-risk children. Our goal is to identify and diagnose every child with autism in Bangladesh before the age of 4 using mobile machine-learning technology that analyzes home videos and a short caregiver-directed questionnaire in minutes.
Dengue, Zika, chikungunya, and other Aedes aegypti-transmitted viruses are a major concern throughout the tropics and sub-tropics, and better mosquito control could dramatically reduce disease burden. Mosquito control is currently inefficient and poorly targeted in part because of a general lack of mosquito surveillance data in most places. Understanding the links between climate, mosquito abundance, and dengue infections would promote a more effective allocation of costly and sometimes environmentally damaging mosquito control resources, such as insecticides.
Working with the Infant & Toddlers Nutrition, Health and Development Program (IT-NHDP) and the Rural Education Action Program (REAP) in China.