Awards

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.

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.

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.

Halfan Ngowo of Ifakara Health Institute with Sarah Osima of the Tanzania Meteorological Authority, both in Tanzania, and Mercy Opiyo of the Centro de Investigação em Saúde de Manhiça in Mozambique will perform data-driven modeling to better understand the impact of climate change and extreme weather events on mosquito-borne malaria transmission in African countries. They will compare data from countries more prone to such events (Mozambique) to those less prone (Tanzania and South Africa). They will use retrospective and newly generated data to model the increased risk of malaria transmission, encompassing human and mosquito behavior and disease dynamics. The models along with enhanced malaria risk assessment tools will be developed in user-friendly formats for use by local, regional, and continental health authorities. Through the project platform, they will train African data scientists and modelers and expand partnerships contributing to resilient malaria control strategies in the face of changing climate patterns.

Nicki Tiffin and Tsaone Tamuhla of the University of the Western Cape in South Africa will model how infectious and non-communicable diseases interact to affect maternal, neonatal, and child health outcomes, using gestational diabetes as a case study. These interactions include those between multiple chronic conditions and multiple medications in individuals and those due to variable access to health care. They will develop risk factor models for gestational diabetes, harnessing Large Language Models for data harmonization and standardization. The models will be applied to mother and child clinical datasets held by collaborators across the Global South through a federated data analysis approach (joint analysis without sharing the data itself). This collaboration will generate new models and evidence for gestational diabetes outcomes. It will also establish guidelines more broadly for health data modeling to inform policy, while helping build a collaborative Global South data modeling community.

Alykhan Vira of Quantium Health in South Africa will establish a project across partner organizations in South Africa, Tanzania, and Nigeria to identify trends in cervical cancer disease burden and model the impact of interventions to inform the design of national HPV immunization programs and health policies. Partners include the National Cancer Registry in South Africa, the University of Dar es Salaam in Tanzania, and Pathfinder International in Nigeria. They will consolidate existing national data on the burden of disease and HPV vaccination, screening, and treatment coverage. They will then use national policy guidelines to develop a modeled view of HPV vaccination, screening, and treatment, overlaying uptake with predicted demand and service delivery capacity. This will be used to model the impact of new programs for cervical cancer prevention. They will also develop an open-source interactive platform to communicate data trends and model outputs, supported by Large Language Model-based tools.

Nicole Wolter of Wits Health Consortium (Pty) Limited in South Africa will establish an African modeling partnership between South Africa, Malawi, and Kenya to ensure that serosurveillance data for vaccine-preventable diseases are effectively incorporated into epidemiological models to guide immunization programs and policies. Serosurveillance provides the most direct measure of a population's immunity. They will establish a collaborative network across the three countries to use serological data to develop two types of models: epidemiological models to inform vaccine program priorities, predict disease burden, and identify immunity gaps and vulnerable groups at risk of outbreaks, and cost-effectiveness models to assess potential changes to these programs. They will train students in the use of AI to enhance data analysis, improve model outputs, and facilitate effective communication. They will regularly engage policymakers and other stakeholders, sharing results to enable comparisons between different countries and making resources available on open-access platforms.

Teferi Gedif of Addis Ababa University in Ethiopia will use health and economic data modeling to improve the efficiency and effectiveness of primary healthcare in Ethiopia through enhancements in the supply chain, health information system, and workforce distribution. Current challenges include inaccurate forecasting leading to shortages and wastage of healthcare commodities, lack of interoperability between patient and healthcare commodity information systems, poor implementation and integration of community-based and social health insurance schemes, and fragmented workflow with poorly matched workforce in terms of number of workers and their training. The models will identify key bottlenecks and inefficiencies. In collaboration with the Ethiopian Ministry of Health, these results will be used to design evidence-based guidelines and protocols for improved primary healthcare service delivery. They will pilot test the proposed strategies in two regions of the country.