Predictive Model

Christopher Duggan of Children's Hospital Boston in the U.S. and his team will test whether known biomarkers of gut dysfunction can accurately predict impaired neurodevelopment and stunting, which reflects chronic malnutrition and is associated with increased morbidity and mortality in young children. The biomarkers will be validated in a well-characterized group of young Tanzanian children. The goal is to facilitate the identification of at risk children early in life, so that appropriate intervention strategies can be applied.

Charles King of Case Western Reserve University in the U.S. and his team will study how chronic parasitic infections in pregnant mothers affect infant immunity and childhood development. Using existing and prospective maternal-child cohorts in Kenya they will analyze the effect of parasitic infections, such as schistosomiasis and intestinal helminths, encountered in utero on subsequent infant vaccine responses, and on general growth and development later in childhood.

Mark Westra and their team at the Akvo Foundation in the Netherlands will build a central data integration platform for malaria that assembles existing data from a variety of sources and enables it to be easily visualized, analyzed, and shared by all types of users. They will build an initial system and test it with a first set of users composed of key groups of stakeholders and users in multiple countries. This will help them gain a detailed understanding of what different users need from such a platform to ensure it becomes a valuable resource for the community.

Isabel Cruz of the University of Illinois at Chicago in the U.S. will build an ontology-based data integration framework that can predict where malaria incidence is likely to increase or decrease in Zimbabwe, to better target elimination efforts. Eliminating malaria requires being able to monitor the changing patterns of infection risk across an entire region, which is affected by multiple factors including the location of health centers, temperature, rainfall, type of landscape, and population distribution.

Paula Cohen of Cornell University in the U.S. will develop a spermatogonial stem cell culture system to investigate whether the first stage of sperm formation - meiotic division of the spermatogonial cell - is a valuable target for the development of effective male contraceptives. Targeting this early stage rather than later stages has several advantages including that it is accessible to compounds in the circulation, and that the effect on fertility would be rapid and reversible. However, little is known about the molecular mechanisms regulating meiotic entry.

Ayumi Arai of the University of Tokyo in Japan will use anonymized mobile phone data to produce a dynamic census that reveals the movements of all individuals in a population over time broken down into age and gender to help reduce regional malaria transmission. Human mobility and distribution play key roles in malaria transmission but it is difficult to monitor the movements of everybody in a population.

Hayley Dickinson of Monash University in Australia will evaluate the spiny mouse, which is the only rodent that naturally menstruates, as a new animal model for developing and testing contraceptives. Menstruation is an essential feature of human reproduction, and is regulated by hormonal contraceptives. However, current contraceptives like the contraceptive pill can cause side effects such as anxiety and low libido. A small animal model that mimics human menstruation would be valuable for testing new contraceptives that have fewer side effects.

Helder Nakaya of the University of Sao Paulo in Brazil will identify hotspots of malaria transmission using the GPS data from mobile phones of infected individuals in order to find asymptomatic cases and help elimination efforts. Malaria is a major public health concern in many countries including Brazil. Eliminating the disease is difficult due in part to the existence of asymptomatic individuals who can still spread the disease but are difficult to detect. Relying on a patient remembering where they have been to identify asymptomatic individuals has not been adequate.

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.

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.