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.
Influence of Adverse Climate Events on Birth Outcomes and Maternal and Infant Nutrition Using Data from the 100 Million Brazilian Cohort
Aline Rocha of Fiocruz in Brazil will link datasets through the Center for Data Integration and Knowledge in Health (CIDACS) to measure the impact of extreme climate events on maternal and infant nutritional outcomes across diverse ecological settings and population groups in Brazil. They will integrate longitudinal data from two datasets, the 100 Million Brazilian Cohort and the Climate and Health Data Platform, connecting them through the municipality where mothers reside. The cohort database links data from social protection programs to administrative and health databases to assess the social determinants of health. The data platform extracts and links climate and environmental data from the year 2000 onwards from existing open-source databases. The integration of these two datasets will guide evidence-based programs to enhance the resilience of health services and mitigate the effects of climate change on maternal and child health, particularly for those most vulnerable.
This grant is funded by Grand Challenges Brazil.
One Health Approach to Data Modeling of Aedes-Transmitted Arboviruses in Brazil
Livia Casseb of Evandro Chagas Institute in Brazil will develop models to understand and predict the impact of climate change on the Aedes mosquito-transmitted arboviral diseases dengue, chikungunya, and Zika in Brazil. The models will integrate a variety of existing data for the different geographic regions of Brazil, including historical data on climate, landscape characteristics, population density, mosquito distribution, and public health. They will also incorporate structured and unstructured data from community networks, teaching and research institutions, and state government entities. The models will reveal interdependent relationships and interactions, including spatial correlations between the arboviral diseases over time. They will develop distinct models for individual geographic regions to serve as early warning systems for arboviral disease outbreaks and to guide local interventions.
This grant is funded by Grand Challenges Brazil.
Community-Led Interventions, Crowdsourced Surveillance, and Governance of Public Spaces in Urban Slum Communities to Mitigate Climate Change
Hernan Argibay of Fiocruz in Brazil will support a participatory research approach for communities in urban slums in Salvador, Brazil to develop and monitor the impact of interventions to reduce the risk of vector-borne and zoonotic diseases. Guided by local needs, new community-led projects will focus on environmentally transmitted diseases (e.g., leptospirosis and enteric infections) and vector-borne diseases (e.g., leishmaniasis, rickettsiosis, and those caused by the arboviruses dengue, chikungunya, and Zika), all of whom could increase in incidence due to climate change. Intervention projects will include environmental clean-up to reduce disease transmission by mosquitos and rats, planting to improve drainage and provide additional food sources, and using an app to map potential risk factors and guide new projects. They will measure intervention impact, including community-led pathogen surveillance using vector traps, water sampling, and metagenomic sequencing.
This grant is funded by Grand Challenges Brazil.
Heat Islands and Thermal Comfort in the Favelas of Maré, Rio de Janeiro
Andréia Santo of the Associação Redes de Desenvolvimento da Maré in Brazil will collect temperature, humidity, and air quality data together with associated health data for residents in the Maré favelas in Rio de Janeiro to better understand the causes of respiratory diseases and reduce their burden. They will also train high school girls as citizen scientists to work alongside health professionals in collecting and analyzing data and developing practical technologies to mitigate the health effects of heat and poor air quality. This participatory science approach will serve as a sustainable mechanism to understand the impacts of climate change on the health of particularly vulnerable communities in Brazil and to guide the development of innovative solutions. In selected residences in Maré, they will pilot an intervention consisting of a bio-concrete wall coating to reduce indoor relative humidity as a cause of heat stress for occupants.
This grant is funded by Grand Challenges Brazil.
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.
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.
Revolutionizing Decentralized Diagnosis of Bacterial Sexually Transmitted Infections for Women Worldwide
Rapidemic in the Netherlands will collaborate with Mohammed Majam of Ezintsha in South Africa to develop a prototype for a molecular test for rapid multiplex diagnosis of chlamydia and gonorrhea, while determining the requirements for its deployment in South African primary care settings to serve hard-to-reach populations. The test system will be designed to diagnose symptomatic and asymptomatic patients accurately and inexpensively using a rapid and disposable test. To guide prototyping, they will research user preferences and assess the usability of the developed device. They will also conduct research to ensure that the development meets regulatory requirements for the South African market and addresses the needs of pharmacies and primary healthcare settings in South Africa.
Integrating ChatGPT-4 with a Wearable Vital Signs Monitor to Improve User Proficiency and Clinical Decision Making for Neonatal Care in Kenya
Sona Shah of Neopenda, PBC in Kenya will integrate ChatGPT-4 as a virtual assistant for the wearable, vital sign monitor neoGuard, supporting healthcare providers in effectively monitoring and managing neonatal health. They will train ChatGPT-4 to help providers identify and address challenges with the neoGuard monitor, such as poor sensor placement on the patient, and to give providers appropriate recommendations based on vital sign data together with the clinical information they gathered. This real-time clinical decision support would be particularly beneficial in remote and understaffed healthcare facilities. For model training, they will use a dataset of newborns admitted to a hospital in Kenya, including vital signs, clinical histories, and treatment outcomes, as well as insights from unstructured clinical notes extracted using natural language processing. They will evaluate use of neoGuard with ChatGPT-4 for reliability, accuracy, and user-friendliness, and compare neonatal patient outcomes before and after ChatGPT-4 integration with the monitor.
Molecular Epidemiology of HPV Infections in Kenyan Women with Cervical Cytological Abnormalities
Moses Obimbo Madadi of the University of Nairobi in Kenya and Aida Sivro of the University of Manitoba in Canada will determine the molecular epidemiology of human papillomavirus (HPV) in cervical cancer cases in Kenya to enable monitoring of changes in the prevalence of HPV types targeted by current vaccines and detect possible replacement with other types. They will perform a cross-sectional study on Kenyan women being followed-up for cervical cell abnormalities at hospitals in Nairobi and in rural Kenya. Outcome measures will include prevalence of HPV genotypes by age, geographic location, and HIV status. HPV genotypes will be stratified by cervical diagnosis to determine the top genotypes associated with cervical cancer. This research will provide robust and standardized statistics on the burden and genetics of oncogenic HPV infection in Kenyan women.
Strengthening Childcare Models that Advance Women’s Economic Empowerment in Machakos County in Kenya
Mary Mbithi of the University of Nairobi in Kenya will test a childcare model in Kenya's Machakos County to increase women's economic participation, reduce and redistribute the burden of unpaid care, and shift gender norms related to childcare. This test will build on results from one tested by the University of Nairobi Women's Economic Empowerment Hub in a different Kenyan county and will inform the model's deployment more broadly across the country. In collaboration with the county government and the local community, they will establish childcare facilities in three sub-counties, including recruiting staff and participating children aged 0-4 years. They will measure impact on child growth, development, and school readiness, as well as measuring impact on women's economic empowerment, including video interviews as qualitative assessment of women's experiences. The county government will take over the running of the childcare centers at the end of the project.
Market and Usability Feasibility for Fetal Lite in Kenya
Wambui Nyabero of Medevice Kenya in Kenya and Vibhav Joshi of InnAccel Technologies Pvt Ltd in India will pilot test Fetal Lite, a fetal monitor for early detection of fetal distress to reduce intrapartum mortality. The monitor is designed for ease of use and patient comfort. It measures fetal and maternal heart rate and uterine activity, has automated data analysis with audio and visual alerts, and has a built-in electronic partogram and AI-based pregnancy risk scoring. It is cloud-enabled with a central web dashboard for report sharing and trend monitoring. They will deploy devices in medical facilities associated with the University of Nairobi, measuring the quality of the auto-generated analysis compared to blinded expert annotation and the ease of use by nursing staff. They will also capture the associated birth outcomes, the guidance provided through remote monitoring, and the number of detected fetal distress cases and referrals.
Meeting Them Where They Are At: Using Large Language Models to Lower Barriers to Measuring the Impact of Gender-Based Violence on Mental Health and HIV Outcomes in Girls and Women in Kenya
Mike Baiocchi of Stanford University in the U.S. will use LLMs to analyze conversational interviews with adolescent girls and young women in Kenya to identify causal mechanisms impairing their health within the potential interplay between living with HIV, mental health conditions, and gender-based violence. Working with the Kenyan Medical Research Institute, they will re-identify and enroll a previously studied cohort living with HIV. The new longitudinal dimension of the study will contribute to untangling causes and effects in parallel to the new LLM-based analysis. They will use LLMs to create statistical measures of participants' descriptions of their experiences to help identify the underlying causes, for instance detecting differences between the responses of those experiencing violence or depression compared to those who have not. Such improved understanding will help to design and target appropriate health interventions.
Acceptability and Marketing of Innovative, Quick-Drying, Reusable, Menstrual SunPad in Kenya
Elizabeth Nyothach of Kenya Medical Research Institute in Kenya will explore introduction of SunPad, a prototype reusable menstrual pad, determining its acceptability, marketability, and regulatory requirements in Kenya. The SunPad product is made of fabric with a built-in cleaning and disinfecting process that is activated by sunlight. They will conduct focus group discussions with women in Kenya to understand their needs in terms of washing, drying, and reusing the pad, and to gauge their willingness to pay for the product. They will also research the potential for local manufacturing of the pad and determine the regulatory requirements and associated documentation.