Awards

Ani Varghese of ZeroEarth Private Limited in India will pilot test a climate-smart dairy farming platform supporting rural women farmers in the Indian state of Tamil Nadu. Through partnerships with financial institutions, they will set up mechanisms facilitating farmers' access to credit to sustainably increase their farming income. They will launch a pilot business center, which will provide training in entrepreneurship and climate-smart farming practices; centralized access to veterinary services; and coordination between dairy, calf-rearing, and fodder farmers. Farming practices supported by the center will include those to improve the health of soil for growing fodder; to optimize feed to minimize greenhouse gas emissions and enhance milk quality; and to efficiently manage manure, with the launch of a biogas plant to generate electricity.

Satadal Saha of the Foundation for Innovations in Health in India will develop an AI-based platform to support public health interventions for women living in the Sundarban Biosphere Reserve, focusing on anemia, urogenital tract infections, and anxiety and depression. The Reserve is a river delta region highly susceptible to climate change-driven severe weather. The project will build on the team's existing digital platform for health data that supports community health workers deliver primary care to island communities. They will collect environmental data, including data for weather and air and water quality, and expand the platform with software enabling regular monitoring and integrated analysis of health and environmental data. This includes incorporating an AI-based predictive model to guide the proactive design and implementation of public health interventions for vulnerable women in this region.

Raghuram Dharmaraju of the I-Hub for Robotics and Autonomous Systems Innovation Foundation in India will improve heatwave forecasting using AI approaches and develop an early warning system for the Indian state of Karnataka to enhance preparedness for heat-induced health risks. The improvements in forecasting will encompass increases in accuracy, lead time, and spatial resolution. The early warning system will use web-based dashboards, mobile apps, and social media platforms to communicate heatwave alerts in local languages. It will include messages tailored to particularly vulnerable groups as well as alerts to healthcare providers to actively monitor these groups. It will also send notifications to relevant government agencies about the potential severity of health impacts. This system will guide public health interventions while helping establish data collection mechanisms for ongoing improvement of the system.

Tavpritesh Sethi of Indraprastha Institute of Information Technology Delhi in India will develop an AI-based platform to support responses to vector-borne diseases in the face of climate change in the Indian state of Uttar Pradesh. They will establish a comprehensive database that integrates climate data with data from existing programs for the control of vector-borne diseases (malaria, dengue, chikungunya, Zika, and Japanese encephalitis). Data will be at the block level of local government in Uttar Pradesh and will include real-time data. For analysis, they will develop an AI-based platform, named Sanketak, that includes modules to capture data, provide automated alerts, visualize changes in disease incidence, and identify early warning signs that predict disease hotspots. They will pilot test the platform, evaluating its potential to preempt, detect, and manage vector-borne disease outbreaks in a timely and effective manner.

Tavpritesh Sethi of Indraprastha Institute of Information Technology Delhi in India will develop an AI-based platform for AMR surveillance and management across a broad network of public and private hospitals in India. The platform will extract weekly data on AMR from the All India Institute of Medical Sciences, New Delhi (AIIMS Delhi) hospital and the Max Healthcare hospital network, including patterns of antibiotic prescriptions across the network. It will use a federated data analysis approach (joint analysis without sharing the data itself), and they will develop and integrate AI-based models to identify and predict trends in AMR. They will also create applications driven by these models to widely and effectively communicate the analyses to healthcare professionals. This will support antibiotic stewardship and data-driven AMR management at both the local and regional levels.

Sai Raj Reddy of Daia Tech Private Limited in India will develop a program to increase access to health education and related resources for adolescent girls in rural areas of the Indian state of Karnataka. The program will be developed in partnership with the Karnataka Health Promotion Trust, building on their ongoing work with local schools, healthcare providers, community leaders, and government agencies. After engaging community members to understand the local context, they will develop resources for adolescent girls including life skills courses and health education workshops and pilot test the program in selected villages. They will integrate AI tools across the program to broaden participation and to broaden the range of health outcomes improved for adolescent girls.

Chiranjay Mukhopadhyay of the Manipal Institute of Virology in India will perform a two-year longitudinal study in the Western Ghats region of India, focusing on sentinel surveillance of tick-borne pathogens and their transmission dynamics. This mountain range region is known for its high biological diversity, and they will sample across 12 sites representing diverse ecological habitats where people and wild and domestic animals interact most frequently. They will collect host-seeking ixodid ticks, screen them for eight tick-borne pathogen groups, and perform whole-genome sequencing for the pathogens identified. Corresponding weather data will be collected from the Indian Meteorological Department. They will combine this longitudinal data to develop statistical models that predict the spatial and temporal transmission of tick-borne pathogens and the corresponding disease risk, which will guide public health interventions.

Bruno Mmbando of Kampala International University in Tanzania will model the combined impact of malaria chemoprevention strategies and vaccines on the burden of childhood malaria in Africa. A modeling focus will be on determining the level of vaccine uptake at which chemoprevention strategies cease to be cost-effective in settings with moderate to high malaria transmission. Data modeling will include the short- and long-term effects of parasite antimalarial drug resistance on the combined impact of chemoprevention and vaccines. The multidisciplinary team will include data modelers, health economists, clinical trialists, and epidemiologists. They will work closely with malaria decision-making organizations, leading to tools and processes that better support the use of malaria data modeling to inform public health interventions.

This grant is funded by The Wellcome Trust.

Karthik Sasihithlu of the Indian Institute of Technology Bombay in India will map urban heat islands across India and test radiative cooling paint on building rooftops to reduce temperatures, heat-related illnesses, and energy consumption for cooling. Urban centers in India face extreme summer temperatures, worsened by the heat island effect in developed areas relative to rural areas and by heatwaves increasing in frequency because of climate change. Radiant cooling paint, because it does not require structurally modifying buildings, could be an affordable and sustainable intervention to reduce the negative health and economic impacts of heat. Heat islands will be mapped using satellite imagery, and radiative cooling paint will be tested in the severe heat islands identified.

Arup Ghosh of CSIR-Central Salt and Marine Chemicals Research Institute in India will design a feed additive for Indian cattle that combines algae and bacteria to reduce enteric methane emission, improve livestock health, and enhance agricultural productivity. From Indian sources, they will select seaweed, marine microalgae, and bacteria, screening them for their ability to inhibit methanogenesis in vitro and in the rumen of cattle. They will also assess their effect on the rumen microbiota and on cattle physiology and productivity. Candidate products made from these additives will be evaluated for their economic viability, including their potential for large-scale cost-effective production, storage, and distribution, as well as for their benefits to farmers relative to existing solutions.

Belehalli Pavan of Strideaide Private Limited in India will develop an AI-based platform to increase timely access to diagnosis and treatment for diabetic foot ailments. Early detection of peripheral neuropathy, peripheral arterial disease, and diabetic foot ulcers would enable interventions that reduce the need for limb amputation. The platform will build on their existing network of podiatry clinics located in a variety of public spaces. These clinics are staffed with a paramedic providing treatment guided by diagnostic tools including a foot mat with a plantar pressure sensor to help predict and assess foot-bottom ulceration. Existing and new digital data from these clinics will be used to build a diabetic foot registry, to improve automated assessment through AI-based analysis, and to train an LLM with a chatbot interface for clinical decision support across the podiatry clinics.

Amrita Mahale of ARMMAN in India will incorporate an LLM-based chatbot into the Kilkari mobile health service to answer questions about pregnancy and infant care. Kilkari currently provides weekly pre-recorded messages on preventive care, and it is implemented in partnership with India's Ministry of Health and Family Welfare. They will assess different available LLMs and train the best candidate with the Kilkari database of vetted content. They will pilot test the model with Kilkari users through WhatsApp, engaging users in Delhi and in the state of Jharkhand to encompass diverse participants spanning urban, rural, and tribal populations. The test will include regular surveys to measure the service's accuracy, relevance, and usability.

Mohanasankar Sivaprakasam of the Healthcare Technology Innovation Centre at the Indian Institute of Technology Madras in India will develop an AI-based platform to support diagnosis and report generation for endoscopic gastrointestinal exams. They will use curated datasets of annotated endoscopic images to develop an AI-based model for diagnosing gastrointestinal abnormalities, such as polyps and ulcers. The data will also be used to train a Large Language Model (LLM) to generate diagnostic reports of endoscopic exams including representative images and text descriptions. This will improve report quality and consistency for better diagnosis and more accurate, detailed patient records, with the report automation reducing the time and personnel required. Together, these platform components will improve patient care in gastroenterology, including more efficient care across more patients.

Ntombifikile Mtshali of Shout-It-Now with Elona Toska of the University of Cape Town, both in South Africa, will pilot test use of ChatGPT to provide information on sexual and reproductive health that helps young women and adolescent girls in South Africa make informed decisions and effectively access health services. They will test integration of the chatbot into Shout-It-Now's existing platforms: a tablet-based platform in mobile clinics staffed by young women and a mobile phone app. They will train ChatGPT to provide information on sensitive topics, including gender-based violence, HIV infection risk, and pregnancy, using existing materials and guided by a workshop with mobile clinic staff. Users' perception of the chatbot and the chatbot's effectiveness in increasing the use of health services will be assessed using on-line questionnaires and phone surveys across five demographically different districts.

Vijay Sai Trap of OnionDev Technologies Pvt. Ltd. in India will develop an AI-based platform to provide accurate and private automated answers to questions on sexual health and mental well-being for youth and women in rural India. They will generate a dataset of community-generated questions on these topics during a mental health awareness campaign in the Indian states of Uttar Pradesh, Madhya Pradesh, and Tamil Nadu. With the support of subject matter experts, they will add answers to these questions from the relevant literature. They will then create a master dataset of questions and answers, including translations using an existing LLM trained on local Indian languages. This dataset will be used to compare different AI-based models to identify the one best able to effectively answer questions on these sensitive topics.

Himanshu Sinha of the Indian Institute of Technology Madras in India will develop an LLM-based chatbot to provide personalized reliable guidance on antenatal care in multiple Indian languages, particularly for mothers without regular access to health care. The chatbot will use an open-source LLM and will be incorporated into a mobile phone application. The project will begin by surveying pregnant women seeking care in a variety of health care settings and across all three trimesters, asking them what features they would want in a pregnancy app. The LLM will be trained with information from textbooks, clinical manuals, government health resources, and guidelines from professional organizations. Initially the chatbot will function in Hindi and Tamil, with additional Indian languages to follow. The app will track pregnancy milestones and deliver relevant evidence-based advice.

Ananda Kumar Mishra of Cowbit Technologies Pvt Ltd in India will pilot test the Cowbit, a wearable device for real-time monitoring of dairy cow health. They will test the device across farms in different states in India, along with a mobile app in local languages for user-friendly readout of the data. The device's sensors will measure elements of cow health particularly relevant for guiding farm management. This includes measurements such as udder temperature for early diagnosis of mastitis and prompt veterinary treatment. It also includes behavioral measurements to identify cows without typical signs of estrus (silent heat) who are ready for insemination to maximize breeding efficiency. They will collaborate with farmers and other stakeholders to ensure the device is relevant for increasing farm productivity and has potential for broad uptake.

Kavitha Sairam of FIB-SOL Life Technologies Private Limited in India will develop mulch sheets that are biodegradable and can be tilled into the soil or composted. Mulch sheets can improve crop yields while reducing the need for irrigation and addition of agricultural chemicals. Traditional polyethylene mulch sheets, however, need to be removed each crop cycle, which is labor intensive, and they are a source of plastic pollution. To develop a biodegradable mulch product, they will explore various materials and fabrication technologies. They will characterize the physical and chemical properties of the candidate product and pilot test its performance and rate of degradation in the field with two different crops as compared to a commercial polyethylene sheet.

Anandkumar Naorem of the ICAR-Central Arid Zone Research Institute, Jodhpur in India will perform polyhouse farming experiments in the Indian state of Rajasthan to identify soil conditions that reduce emission of the greenhouse gas nitrous oxide. Polyhouses are greenhouse-style but enclosed in polyethylene rather than glass. They will grow tomato plants as a vegetable test crop, adjusting soil properties by varying factors including the type of mulch, irrigation frequency, the type of nitrogen compound added, and whether biochar is added. They will assess soil health through analysis of its physical, chemical, and biological properties, focusing on how they relate to nitrous oxide emission. The results will guide improvements in polyhouse farming particularly relevant as a sustainable agricultural strategy for arid regions.

Sujatha Sunil of the International Center of Genetic Engineering and Biotechnology in India will determine the temperature preference of Indian Aedes aegypti mosquitoes to build a mathematical model that predicts their prevalence across the country as well as hotspots for the arboviral diseases they transmit. To understand temperature preference, they will study laboratory-adapted Aedes aegypti, assessing their physiology and development in the laboratory across the mosquito life-cycle at a range of relevant temperatures. They will also sample mosquitoes across sites in Delhi, recording their physical and physiological features and their carriage of arboviruses, together with weather data from nearby weather stations and arboviral disease incidence data from nearby hospitals. The laboratory and field data will be integrated to build predictive models to identify areas in India most at risk of arboviral epidemics.