Predictive Model

Ali Mokdad and a team at the University of Washington in the U.S. will integrate data on animal health and its determinants to better understand and monitor human health and opportunity in the developing world. Animal health, which itself is influenced by environmental factors, has a direct impact on human health. Although data on all these areas exist, there has been no attempt to amalgamate them to measure the overall impact on human health.

Matthew Bonds of Harvard University in the U.S. will quantify the economic burden of disease using a combined metric to incorporate disease impact on both human and animal health. Current measures of economic burden consider humans and animals independently, yet they are both influenced by disease and by the health of each other. They will develop an integrated model combining epidemiology and economic growth to uncover links between disease impact and income in both human and livestock systems.

Luiza Cintra Campos of the University College London in the United Kingdom proposes to develop a simulation tool that can be used in developing communities that have non-networked sanitation systems to effectively evaluate new sanitation technologies. By including parameters such as pit latrines served, distance to treatment, and potential for energy recovery, the simulation tool can aid communities in determining the best new systems for local needs.

Roger Miller and colleagues at the Logistics Management Institute in the U.S. propose to develop a software-based prototype modeling tool that allows vaccine program managers to lower total delivered costs by analyzing all of the clinical and logistical factors that determine the cost per viable vaccine dose administered. The prototype could be developed as a fully deployable software product with associated training, reporting, and analysis capability.

Kumanan Wilson of Ottawa Hospital Research Institute in Canada is developing an algorithm to estimate gestational age using specific metabolic analytes found in blood spots collected routinely from newborns in many countries. Knowing the precise age of infants is important for evaluating development particularly of brain function. It is currently measured by ultrasound, which requires expertise and expensive equipment, and is not available in many countries.

Philip Baker from the University of Aukland in New Zealand will determine whether the presence of specific metabolites in a mother's hair during pregnancy can be used as an early marker of defective neurodevelopment in the child. Early diagnosis of neurodevelopmental disorders such as autism could lead to better treatment. Because hair stably incorporates chemical compounds, they will use samples of maternal hair from a previous study to search for metabolic markers such as fatty acids and amino acids that may correlate with subsequent developmental defects in the infants.

Ana Namburete of the University of Oxford in the United Kingdom will develop a computational tool called Autodate that identifies physical features of the fetal brain from a routine ultrasound image to automatically estimate gestational age at any stage of pregnancy. Determining accurate gestational age is important for healthy pregnancy. However, ultrasound, which is the most accurate technique, can only estimate gestational age when used during the early stages of pregnancy by a trained sonographer, who are often absent in low-income settings.

Paul Albert of Eunice Kennedy Shriver National Institute of Child Health and Human Development in the U.S. will use a modeling approach to help clinicians with different resources better estimate gestational age, which is critical for monitoring maternal and child health. Gestational age is currently estimated using ultrasound, which is often unavailable in low-resource settings, or the date of the last menstrual period and external measurements of the uterus, which can be imprecise. The timing of these measurements during pregnancy also influences the accuracy of the estimate.

Don Sharkey of the University of Nottingham in the United Kingdom will develop a software-based analysis tool to automatically calculate gestational age from simple videos of newborn faces and feet. Knowing the gestational age, particularly for babies born preterm, is critical for ensuring their healthy development. However, current dating procedures are expensive and/or require trained personnel, and as such are often unavailable in low-middle income countries.

Laura Jelliffe-Pawlowski of the University of California, San Francisco in the U.S. is developing an algorithm to measure gestational age from metabolic markers taken during routine newborn screening. Measuring accurate gestational age is important for assessing infant health such as brain development, but it is challenging in developing countries without specialized equipment and expertise. In Phase I, they developed a statistical model using data on 51 metabolic markers from around 730,000 newborns in the U.S.