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Featured Research from Stanford University

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Project Reducing infectious disease exposure among school-aged children in developing countries: The WaSH Up! Alliance, Stanford University (July 1, 2016 - Present)

Principal Investigator:

WaSH Up! is a partnership between Sesame Street, World Vision, and Stanford University to reduce child disease and death by ensuring children have access to safe water and sanitation by practice healthy behaviors relators to water, sanitation and hygiene (WaSH). Sesame Workshop has worked to created a new Muppet character named Raya, a girl ambassador who promotes safe WaSH practices. Sesame Workshop with World Vision announces a commitment to roll out WaSH Up! across 15 countries in the next six years.

Project Creating a Scalable Model to End Poverty: Delivery of an Integrated Childhood Development Strategy in Rural China

Principal Investigator:

Working with the Infant & Toddlers Nutrition, Health and Development Program (IT-NHDP) and the Rural Education Action Program (REAP) in China.

Location

Shaanxi, China

Collaborators

  • Scott Rozelle, Helen C. Farnsworth Professor in International Agricultural Policy and Senior Fellow at the Stanford Institute for Economic Policy Research, Stanford University

Project Predicting dengue transmission in a changing climate to improve mosquito control, Stanford University (Depts of Biology and Pediatrics) (7/1/2016 - 6/30/2018)

Principal Investigator:

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.

Project A Mobile Autism Risk Initiative (AMARI) to detect Autism Spectrum Disorder in all Bangladeshi children under the age of 4., Dhaka Shishu Hospital, Stanford University

Principal Investigator:

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.

Project Learning from Ananya - Scaling Up Improved Family Health, Stanford University (12/2016)

Principal Investigator:

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.

Project Grave Reform in Modern China

Principal Investigator:

The Deathscape China project is building and harnessing an interactive spatial and textual analysis platform to examine the phenomenon of grave relocation in modern China, a campaign that has led to the exhumation and reburial of 10 million corpses in the past decade alone, and has transformed China’s graveyards into sites of acute personal, social, political, and economic contestation.

Location

China

Project Characterizing the Effects of Antenatal Parasitic Infection on Fetal Immune System Development, Stanford University, Case Western Reserve University, Ministry of Health- Kenya (1/1/2016 - 1/1/2017)

Principal Investigator:

Extensive resources are being committed to improve global childhood vaccination coverage, but the response to standard vaccination is often diminished in children from developing nations. The ineffectiveness of vaccination programs in developing communities has been blamed on cold chain lapses and lack of supportable infrastructure, but chronic infections also play a significant role. Multiple maternal parasitic infections affect the unborn infant and are potentially important vaccine response modifiers, but have not been well studied.

Project Complex Data and Deep Learning for Disease Outbreak Prediction, Stanford (1/1/2017 - 1/1/2018)

Principal Investigator:

Disease outbreaks are not easily predicted because they occur only when multiple factors trigger the rapid spread of disease. Key factors can often be identified, e.g., excess rainfall leading to outbreaks of Rift Valley fever virus (RVFV)1,2, but the complex circumstances that lead to outbreaks remain elusive for several reasons. First, gathering varied datasets (climatic, genetics, demographic, historical, and behavioral) is time consuming and expensive.

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