Awards

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.