Awards
Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.
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Community-Centric Climate Early Warning and Response System (C3-EWS) for Enhancing Resilience to Climate-Related Health Hazards in Siaya County, Kenya
Daniel Kwaro of CREATES in Kenya will develop an early warning system for malaria outbreaks, floods, and heatwaves in Siaya County in Kenya, co-designing it with the local community. They will incorporate health and demographic surveillance system data, including a specific focus on maternal health indicators and birth outcomes, as well as data from automated weather stations, wearable devices, and mosquito traps. Through secondary data analyses, they will assess the probability and consequences of climate-related hazards, including identifying vulnerable communities, high-risk geographical areas, and occurrence patterns of climate-sensitive diseases. They will actively involve Siaya County residents, healthcare providers, and relevant local authorities in co-designing the early warning system paired with multiple mechanisms for communication to ensure the system is accessible and effective in responding to local needs.
Climate-Focused Analytics and Modeling for Mosquito-Borne Infections in Southern Africa (CAMMISA)
Sheetal Silal of the University of Cape Town in South Africa will establish a research consortium to analyze how climate change affects the transmission and control of mosquito-borne diseases, focusing on how to optimize interventions for malaria, chikungunya and dengue in Southern Africa. The consortium will integrate research projects led by local data scientists working closely with local decision-makers. Through mathematical and statistical modeling together with climate science, these projects will determine climate scenarios across time scales relevant for management of mosquito-borne diseases. These time scales will encompass short-term windows (6-12 months) as well as longer windows (5-10 years) relevant for policy planning and that incorporate the predicted impact and costs of new interventions. The consortium will also explore even longer windows (over 30 years) to provide predictions useful to initiate policy discussions and bring attention to the long-term implications of climate change on disease control strategies.
This grant is funded by The Wellcome Trust.
Leptospirosis in Changing Climates: Soil Health, Sociocultural Behaviors, and Public Health Policy
Roman Thibeaux of the Institut Pasteur de Nouvelle Calédonie in New Caledonia will examine how climate-driven soil changes and societal and behavioral factors can affect the incidence of leptospirosis to develop community-centered prevention strategies. The causal agent of the disease is the bacterium Leptospira, which can be found in water or soil contaminated with the urine of infected animals and thus can spread following heavy rainfall. Leptospirosis is endemic in the New Caledonia archipelago in the South Pacific, with potential climate-driven increases in incidence. Using soil microcosms in the laboratory, they will explore the effects of temperature, rainfall, and soil structure on Leptospira survival and dispersion. Through interviews and focus groups with New Caledonia community members together with ethnographic fieldwork, they will record local perceptions and knowledge relevant to leptospirosis and its transmission. In partnership with local community members and health authorities, they will then identify sustainable strategies to reduce leptospirosis incidence.
This grant is funded by the Pasteur Network.
Machine-Learning Ultrasound Tools to Monitor Women's Nutrition in Ethiopia
Bryan Ranger of Boston College in the U.S. will develop a cost-effective, portable, and automated ultrasound tool to monitor nutritional health of young pregnant women in Ethiopia. The tool will incorporate AI models that guide users in collecting high quality data, so the tool can be used by frontline and community healthcare workers without extensive ultrasound training, and the models will use this data to predict metrics of nutritional status. In a pilot study conducted at the Jimma Medical center, they will create a database of ultrasound scans, anthropometry, body composition measured by gold standard techniques, and the associated clinical data from a group of young pregnant women. Ultrasound measurements will incorporate data on user position to identify the most informative positions via machine learning. They will survey clinical users to guide the ultimate design of the ultrasound system.
Modeling for Decisions in a Dynamic Africa
Susan Rumisha of Ifakara Health Institute in Tanzania will support the establishment of data modeling hubs in the Democratic Republic of Congo, Nigeria, and Tanzania, linking them into a collaborative network to guide the control of mosquito-borne diseases in the face of climate change. The focus will be on the direct and indirect effects of environmental change on malaria, modeling the interplay of these effects with public health systems and mosquito vector and disease patterns. This will encompass modeling mosquito vector distribution, abundance, and seasonality using historical climate data together with new microclimate information. The models will be designed to support national programs in prioritizing vector surveillance activities, targeting interventions, and developing early warning systems for emerging health threats. The network will strengthen model-building expertise and could be adapted to address mosquito-borne arboviral diseases.
This grant is funded by The Wellcome Trust.
Modelling Aedes-borne Diseases for Improved Public Health Decision-Making in the Horn of Africa
Bernard Bett of the International Livestock Research Institute in Kenya will develop disease transmission models for two Aedes mosquito-borne arboviral diseases, dengue and chikungunya, and use the models to design decision support tools to guide surveillance and control of these diseases in Kenya, Somalia and Ethiopia. The models will be validated with longitudinal field data, including mosquito population density, infection patterns, blood meal sources, and the incidence of Aedes-borne diseases in humans. The models will be used to estimate important metrics for disease management, such as time-to-disease outbreak, cost effectiveness of control, and spatial distribution of risk. They will also help identify how the ecological tipping points for outbreaks of dengue and chikungunya compare to each other and how existing control measures for the two diseases could be integrated for better health outcomes. The project will link institutions including the Ethiopia Public Health Institute, Kenya’s Department of Disease Surveillance and Epidemic Response, Somalia’s Federal Ministry of Health, Jomo Kenyatta University of Agriculture and Technology, Abrar University, the Kenya Medical Research Institute, Ohio State University, Global One Health Initiative, and the International Livestock Research Institute.
This grant is funded by The Wellcome Trust.
Uncovering Targets of Protective Immunity for Next-Generation Malaria Vaccines
James Beeson of Burnet Institute in Australia, Melissa Kapulu of Health Research Operations Kenya Limited in Kenya, Isaac Ssewanyana of Infectious Diseases Research Collaboration in Uganda, Faith Osier of Imperial College London in the U.K. and Pras Jagannathan of Stanford University in the U.S., will analyze clinical samples using an antibody functional assay platform with malaria antigen arrays to identify antigens targeted by protective antibodies for next-generation malaria vaccines. They will identify antigen-specific functional antibodies that strongly correlate with protective immunity to malaria observed in clinical studies with two populations: Kenyan adults after controlled experimental challenge infection with Plasmodium falciparum and children followed longitudinally who were naturally exposed in Uganda and in Papua New Guinea. They will then use biostatistical modeling approaches to identify antigen and functional antibody types that most frequently occur in protective combinations, identifying additive and synergistic combinations of responses and responses most predictive of protective immunity across age groups and populations. This will enable prioritization of antigens and their combinations for malaria vaccine candidates.
Syndemic Disease Modeling to Optimize Health Service Integration in Africa
Mary Mwanyika-Sando of Africa Academy for Public Health in Tanzania will develop a mathematical model that accounts for multiple co-occurring diseases and their interactions as well as resource constraints to design integrated healthcare services for people living with HIV in Burkina Faso, Tanzania, and South Africa. The team will use high-quality longitudinal data from four health and demographic surveillance sites. They will characterize co-morbidities and the syndemic clustering of HIV with other diseases (synergistic epidemics), including hypertension, diabetes, and depression, that is due to interrelated biological, environmental, and behavioral factors. They will use the model to predict current and future chronic disease burdens of HIV and other diseases, and then determine optimal health service delivery. The results will be used to co-design intervention implementation strategies with local implementers and policy makers.
Anti-TB Drug Discovery: Design, Synthesis, Evaluation, and Mechanistic Studies
Rajshekhar Karpoormath of the University of KwaZulu-Natal in South Africa will test a set of potential anti-TB hit compounds against clinically relevant TB strains, using the results to generate optimized hit compounds for development of new anti-TB drugs. They will screen the potential hits against susceptible, monodrug-resistant, multidrug-resistant, and extensively drug-resistant TB strains as well as other Mycobacterium strains. The screening results will inform structure-based drug design to generate optimized hit compounds. Potential lead hits will be screened again, with the most promising evaluated against intracellular bacteria in macrophages, tested for in vitro cytotoxicity, and evaluated for mechanism of action in bioassays including carbon-isotope tracing metabolomics and an in vitro granuloma assay.
Remodeling Maternal Health Care: Evaluating the Impact of Midwife-Led Birthing Centers on Maternal and Neonatal Health Outcomes in Ethiopia
Solomon Hailemeskel of Debre Berhan University in Ethiopia will pilot test midwife-led birthing centers for pregnant women and newborns at low risk of complications to increase access to safe, high-quality childbirth experiences for Ethiopian women. They will implement a multicenter randomized controlled trial, recruiting a cohort of pregnant women from antenatal care clinics across diverse healthcare facilities to ensure a representative sample. After training midwives to provide continuity of care before, during, and after pregnancy, they will establish midwife-led birthing centers in dedicated spaces, either within or separate from a higher-level health facility. A subset of trial participants will be randomly assigned to the birthing centers. They will compare outcomes for the two groups, including data on maternal and neonatal health outcomes, as well as qualitative data from interviews of mothers, midwives, and healthcare providers.