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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Orapan Sripichai of the National Institute of Health of Thailand in Thailand will engage a national network of laboratories for the genomic surveillance of Salmonella, involving sequencing clinical isolates to characterize strains, virulence factors and mechanisms of antimicrobial resistance. Salmonella infection is prevalent in Thailand and can be life-threatening. The emergence of multidrug-resistant Salmonella strains in Southeast Asia is an additional major concern. They will collect approximately 1,500 clinical isolates from 77 provincial hospitals across Thailand over one year, and train local laboratory scientists and bioinformaticians to produce and analyze genomics data. The data will be uploaded to a standard repository in the National Center for Biotechnology Information (NCBI) and will help to guide prevention and control measures.
Rifky Waluyajati Rachman of the West Java Provincial Health Laboratory in Indonesia will employ targeted next-generation sequencing (NGS) to support genomic surveillance of drug-resistant tuberculosis (TB) in Indonesia. Indonesia has the second highest number of TB cases globally and a growing burden of largely undetected multidrug-resistant TB, yet no drug resistance surveillance in place. They will perform targeted NGS on over 5,000 positive sputum samples to more accurately estimate drug-resistant TB prevalence. They will also conduct whole genome sequencing at the community level to understand transmission patterns and help guide public health interventions. To build capacity, they will provide tailored training on the experimental, bioinformatic, public health, and epidemiological aspects of infectious disease surveillance. They will also establish a public center of expertise for pathogen surveillance in West Java, which has a population of 48 million.
Jacqueline Weyer of the National Institute for Communicable Diseases in South Africa and Jinal Bhiman of Wits Health Consortium (Pty) Ltd also in South Africa will leverage a rapid monoclonal antibody (mAb) isolation and screening pipeline to develop diagnostics that differentiate between pathogens to support epidemic responses. Africa’s burden of many zoonoses and vector-borne diseases (VBD), such as Lassa fever and yellow fever, remains largely unknown, mainly due to diagnostic costs and limited access to reagents. They will leverage an existing screening pipeline, with infrastructure established by the Global Immunology and Immune Sequencing for Epidemic Response - South Africa (GIISER-SA) project, using a mouse model as a more readily available source of pathogen-specific B cells to identify mAbs that detect three ebolavirus species. These mAbs will be tested for sensitivity and specificity using patient samples and can be used to develop immunoassays, including rapid lateral flow assays, which are important for rapid, field-based diagnosis.
Anne Lee of Brigham and Women's Hospital in the U.S. and Yasir Shafiq of Aga Khan University in Pakistan will develop geospatial models to predict risks of undernutrition among adolescent girls and pregnant and lactating women in settings affected by conflict, climate and COVID-19 to help target interventions. Globally, around 30–40 million pregnant women and 50 million adolescent girls are underweight. Risks of undernutrition have recently been amplified by numerous armed conflicts, climatic shocks such as flooding and the COVID-19 pandemic. However, real-time data shortages prevent interventions, such as balanced energy-protein supplements, from reaching the highest-risk groups. Using Bayesian Hierarchical Spatial modeling, they will develop geospatial models for countries vulnerable to conflict and climate change, such as Ethiopia and Yemen. By incorporating socio-demographic and economic indicators, and climate-related and conflict-related shocks from national databases, they can estimate risks based on exposure and predict outcomes, such as undernutrition and anemia.
Margaret Kasaro and Soumya Benhabbour of the University of North Carolina at Chapel Hill in the U.S. will evaluate 3D-printed intravaginal ring (IVR) prototypes in Zambia to identify the design most acceptable to women for long-term use against unplanned pregnancy and HIV infection. In Zambia, HIV prevalence remains particularly high among women, and 41% of pregnancies are unplanned. IVRs are an effective, well-tolerated, and women-controlled contraceptive and HIV-preventative; however, their performance has suffered in large-scale clinical trials because of poor adherence. They have exploited a state-of-the-art 3D-printing process to rapidly engineer IVRs in a cost-effective, single-step process enabling the controlled release of multiple drugs for HIV prevention and contraception. They will recruit around 16 women, aged 18–45 from Kampala Health Centre, and use focus groups to evaluate their views on the proposed 90-day timeframe of use for four different IVR prototypes to guide the final design.
Aida Sadikh Badiane of the Universite Cheikh Anta Diop de Dakar in Senegal will use a metabolomics platform to identify cervicovaginal metabolites and inflammatory mediators associated with high-risk human papillomavirus (HPV) infection, which cause the majority of cervical cancer cases, in Senegalese women. Cervical cancer is the leading cause of cancer deaths in women in sub-Saharan Africa. Metabolic and immune markers could enable more effective diagnoses for these diseases than the current methods used in low-resource settings. They will perform a prospective, cross-sectional study on a cohort of 385 women using an untargeted metabolomics platform to identify molecules within the cervicovaginal microenvironment that are predictive of infection and cancer risk. They will also use Luminex assays to evaluate inflammatory molecules and other markers associated with infection, and sequence the L1-HPV gene in the samples to better track the genotypes in Senegal.
Pragya Yadav of the Indian Council of Medical Research - National Institute of Virology in India will strengthen genomic and epidemiological surveillance in different locations across India to enhance preparedness against high-risk viral diseases. With India's extreme geo-climatic diversity, it is under constant threat of emerging and reemerging viral infections. They will enhance surveillance of endemic diseases in India, including Zika and Dengue, by establishing a network of seven laboratories and training staff in molecular diagnostic techniques, including sequencing, data analysis, and biosafety. They will also select surveillance sites for collecting samples and expand next-generation sequencing capacity to identify variants.
Simon Kariuki of the Kenya Medical Research Institute in Kenya will use an antibody platform to characterize children's immune responses to the new malaria vaccine to determine the impact of any accompanying infections. The WHO recently approved a new malaria vaccine that will mainly be deployed in sub-Saharan Africa. During its development, HIV-infected children were found to mount weaker immune responses. Helminth infections, which are prevalent in sub-Saharan Africa, are also suspected to negatively impact vaccine efficacy. To test this, they will use an antibody-dynamics platform to assess the impact of helminths and other current or prior parasitic, bacterial, and viral infections on humoral and cellular immune responses following the 4th dose of the new malaria vaccine in two- to three-year-old children at six hospitals in western Kenya. This will help design more effective deployment strategies such as deworming before vaccination.
Senjuti Saha of the Child Health Research Foundation in Bangladesh will use a single-cell analytics platform to track the immune responses of babies before and after receiving a pneumococcal conjugate vaccine to determine the impact of various factors, including nutritional status and seasonality, on vaccine efficacy. Vaccines have successfully reduced childhood morbidity and mortality; however, their efficacy can be influenced by host factors and extrinsic factors through unknown cellular mechanisms. They will recruit 50 newborns in a rural district north of Dhaka and collect blood and nasopharyngeal swabs before, during and after a routine vaccination series. They will extract peripheral blood mononuclear cells and use them to perform single-cell RNA sequencing to identify cell subtypes and link differential vaccine responses to factors including gestational age, nutritional status and sex.
Yasir Shafiq of Aga Khan University in Pakistan and Anne Lee of Brigham and Women's Hospital in the U.S. will develop geospatial models to predict risks of undernutrition among adolescent girls and pregnant and lactating women in settings affected by conflict, climate and COVID-19 to help target interventions. Globally, around 30–40 million pregnant women and 50 million adolescent girls are underweight. Risks of undernutrition have recently been amplified by numerous armed conflicts, climatic shocks such as flooding and the COVID-19 pandemic. However, real-time data shortages prevent interventions, such as balanced energy-protein supplements, from reaching the highest-risk groups. Using Bayesian Hierarchical Spatial modeling, they will develop geospatial models for countries vulnerable to conflict and climate change, such as Ethiopia and Yemen. By incorporating socio-demographic and economic indicators, and climate-related and conflict-related shocks from national databases, they can estimate risks based on exposure and predict outcomes, such as undernutrition and anemia.
Raghavan Varadarajan in collaboration with Sudha Kumari, both of the Indian Institute of Science in India and Nico Callewaert of the VIB-UGent Center for Medical Biotechnology in Belgium will modify the microorganism, Pichia pastoris, used to produce lower-cost vaccines in low-resource settings, to generate more effective vaccines. Many vaccines are composed of pathogen-derived proteins that require production inside other cells. Although P. pastoris can produce these antigens at a lower cost than mammalian or insect cells, the viral proteins it produced for the SARS-CoV-2 vaccine were hyperglycosylated and poorly immunogenic, unlike those produced in mammalian cells. They will express different antigen forms in mammalian cells, and in different Pichia hosts, to determine whether altering glycosylation and protein size affects immunogenicity. They will also glycoengineer Pichia hosts to determine whether they can produce more effective vaccines. Ultimately, this approach could improve vaccine production for COVID-19 and other viruses.
Jinal Bhiman of Wits Health Consortium (Pty) Ltd in South Africa and Jacqueline Weyer of the National Institute for Communicable Diseases also in South Africa will leverage a rapid monoclonal antibody (mAb) isolation and screening pipeline to develop diagnostics that differentiate between pathogens to support epidemic responses. Africa's burden of many zoonoses and vector-borne diseases (VBD), such as Lassa fever and yellow fever, remains largely unknown, mainly due to diagnostic costs and limited access to reagents. They will leverage an existing screening pipeline, with infrastructure established by the Global Immunology and Immune Sequencing for Epidemic Response - South Africa (GIISER-SA) project, using a mouse model as a more readily available source of pathogen-specific B cells to identify mAbs that detect three ebolavirus species. These mAbs will be tested for sensitivity and specificity using patient samples and can be used to develop immunoassays, including rapid lateral flow assays, which are important for rapid, field-based diagnosis.
Simon Kariuki of the Liverpool School of Tropical Medicine, Kenya in Kenya and Holden Maecker of Stanford University in the U.S. will determine whether probiotics and synbiotics can boost infant immune responses to vaccines. Diarrhea is the second leading cause of death in young children, with rotavirus a leading culprit. Oral rotavirus vaccines are routinely administered in low- and middle-income countries (LMIC) but are only 50% effective compared to 85–98% effectivity in high-income countries. One major cause could be environmental enteric dysfunction (EED), which is pervasive in children in LMIC. Their clinical trial of 600 newborns from western Kenya indicated that administering weekly probiotics and synbiotics (Lactobacilli and Bifidobacteria) up to age six months improved gut health and prevented EED-associated inflammation. They will use stored plasma samples and vaccination records to determine the impact of EED and systemic inflammation, as well as pro- and synbiotic effects on rotavirus vaccine efficacy.
Abdoulaye Djimde of the University of Sciences, Techniques, and Technologies of Bamako in Mali will use a metabolomics platform to identify biomarkers to detect dormant Plasmodia hypnozoites in a previously malaria-infected individual as a diagnostic method and to screen for new therapeutics. Malaria remains one of the deadliest parasitic diseases in the world, with 95% of deaths occurring in sub-Saharan Africa. Most research focuses on the most prevalent causative parasite, Plasmodium falciparum, but other strains, including P. vivax and P. ovale, are likely to become more dominant. These strains uniquely produce hypnozoites, which can lay dormant for years in the liver where they are undetectable and resistant to treatment. They will generate hypnozoite-containing liver cells in vitro and subject them to metabolomics analysis to identify hypnozoite-associated biomarkers. Candidate biomarkers will then be validated in serum samples from thirty infected individuals.
Georgia Tomaras and Nathanial Chapman of Duke University and Girija Goyal and Don Ingber of the Wyss Institute at Harvard University, both in the U.S., will test whether Organ-on-a-Chip technology can inform how antibodies protect humans from pathogen infections to design more effective vaccines. Identifying protective vaccine features and validating them in human clinical trials is time-consuming and costly. An alternative is to use primary human organ chips that reproduce human physiology in vitro. They will stimulate peripheral blood mononuclear cells on the human lymph-node-on-a-chip with existing COVID vaccines and extensively characterize the resultant antibodies, including evaluating epitope specificity, and isotype and glycan profiling. They will also assess the capacity of these antibodies to prevent or reduce SARS-CoV-2 infection using the lung-on-a-chip technology. This approach can ultimately be applied to other pathogens, such as those causing malaria.
Audrey Dubot-Pérès of the Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit (LOMWRU) in Lao PDR will establish a pilot respiratory syncytial virus (RSV) genomic surveillance system to determine disease burden and monitor strain circulation in Lao PDR. RSV is the leading cause of viral pneumonia in young children in low-income countries. Accurate data on disease burden, transmission and viral evolution are critical to successfully introduce emerging vaccines and therapies. Leveraging their experience as a national center for SARS-CoV-2 genomic surveillance, they will develop an RSV genomic sequencing protocol using samples collected from children at two central and four provincial hospitals. They will also investigate whether RSV RNA can be purified directly from rapid diagnostic tests to improve surveillance in remote areas. The data will be displayed on a national health dashboard. If successful, their approach could be expanded into a national surveillance system.
This grant is one of three grants that are funded and administered by the Programme for Research in Epidemic Preparedness and Response (PREPARE) in Singapore.
Chhorvann Chhea of the National Institute of Public Health in Cambodia will expand Cambodia’s Severe Acute Respiratory Infections (SARI) surveillance network by integrating metagenomic next-generation sequencing to better diagnose and monitor severe respiratory infections. Pneumonia is the leading cause of death globally in children under five years old, with the majority of severe cases classified as viral. To successfully develop treatments and vaccines, a comprehensive understanding of viral genetic diversity is required; however, this remains largely uncatalogued for common respiratory viruses, such as respiratory syncytial virus (RSV). They will collect oropharyngeal swabs, blood culture isolates, or lower respiratory tract samples from adults and children with SARI at nine sites. They will extract RNA and leverage pandemic sequencing infrastructure for sequencing, taxonomic identification and phylogenetic analyses to guide molecular epidemiology and outbreak investigations. The data will be integrated with a country-wide genomic surveillance strategy, currently under development.
This grant is one of three grants that are funded and administered by the Programme for Research in Epidemic Preparedness and Response (PREPARE) in Singapore.
Mai Le of the National Institute of Hygiene and Epidemiology in Vietnam will expand Vietnam’s systematic surveillance and sequencing capacities to detect potential pandemic pathogens, including influenza and coronaviruses, and incorporate agnostic sequencing of conventionally undiagnosed pathogens. They will build on the existing infrastructure of the influenza-like illnesses sentinel surveillance network, which collects samples from four outpatient clinics, to include testing for both influenza A and B and SARS-CoV-2 viruses, with the possibility to expand. They will also revive the hospital-based Severe Acute Respiratory Infections (SARI) surveillance network, which works with three hospital emergency departments and ICUs, to focus on 12 pathogens and incorporate an agnostic sequencing component. Their activities will include training health workers in sample collection and scientists in directed and agnostic sequencing of respiratory pathogens and bioinformatics analysis. The data produced will be shared in real-time on an online dashboard.
This grant is one of three grants that are funded and administered by the Programme for Research in Epidemic Preparedness and Response (PREPARE) in Singapore.
Olayinka Omigbodun of the University of Ibadan in Nigeria will build a critical mass of female researchers and policymakers to adapt and apply diverse mathematical models to better understand the epidemiology of depression in young women in sub-Saharan Africa and identify more effective preventative measures and treatments. Adolescent girls and young women in sub-Saharan Africa are three times more likely than their male counterparts to have a depressive disorder. Mathematical modeling provides a powerful means of predicting the dynamics of depression. However, there is a paucity of models that inform mental health strategies in this region. They will leverage existing research networks across the region to train new female modelers and, together with them, critique existing mathematical models of mental health and depression. This will enable the development of more suitable models, populated with local data, to identify predictors of depression in this group.
Cameron Myhrvold of Princeton University and Mireille Kamariza of the University of California, Los Angeles, both in the U.S., will develop an assay to rapidly detect multiple drug resistance mutations in Plasmodium falciparum and Mycobacterium tuberculosis for malaria and tuberculosis (TB) surveillance, respectively. Malaria and TB are two of the world's deadliest infectious diseases. Rapid and accurate drug resistance testing can save lives but current assays are slow or difficult to scale. Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN) is a CRISPR-based diagnostic test that detects nucleic acid biomarkers, such as those in pathogens, with high specificity and throughput. They have developed microfluidic CARMEN (mCARMEN), which produces results in under five hours, and will use an algorithm to design assays that detect the top ten drug-resistant P. falciparum mutations from blood samples, and M. tuberculosis mutations from saliva samples that confer resistance to two first-line TB drugs.
Alex Ario of Makerere University in Uganda, together with the Uganda National Institute of Public Health, the Ministry of Health of Uganda, and sister organizations in the East Africa region will expand their modeling capacities and establish collaborative research groups to apply modeling and data analytics to study health issues disproportionately affecting women. They will set up a multi-country steering committee to identify teams of modelers in Uganda, Kenya, and Rwanda. This committee will also select the most pressing women’s health issues and assign them to the modeling teams for investigation. They will also train modelers, particularly women, through on-the-job teaching and mentorship. The main findings from the collaborative studies will be disseminated to decision-makers and they will also advocate to influence policy.
Project intends to capture the images of blood samples for diagnosis of Lymphatic Filariasis and applying customized machine learning algorithms for quantification and classification of Lymphatic Filariasis.
Esnat Chirwa of the South African Medical Research Council in South Africa will strengthen modeling and data science capacities by incorporating training and networking approaches, particularly for female researchers in Malawi and South Africa. The rising disease burden in sub-Saharan Africa has resulted in the generation of many large, complex datasets; although these provide rich research resources, local analytical capabilities are limited. They will increase the number of female modelers and statisticians by providing financial support to seven female Biostatistics and Statistics master’s students, who will be mentored by their team, and a series of free, short in-person and online advanced statistics courses to over 90 more female researchers. They will also build networks between female researchers to facilitate collaborations on defined topics, including identifying the mechanisms driving women’s health outcomes in Southern Africa and the long-term impact of rape on mental health.
Project intends to discover high affinity and specific ligands called suprabodies as the biomarkers of Lymphatic Filariasis.
Maurício Barreto and colleagues of Fiocruz in Brazil, together with Alexa Heeks and colleagues of the Health Foundation of South Africa in South Africa, will employ real-world data from two large countries of the Global South to develop a common data model of infectious diseases affecting pregnant women to identify causes and aid intervention development. Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS), together with the Western Cape Provincial Health Data Centre (WCPHDC), have built data systems to utilize routinely collected health data for exploring disease impacts. They will leverage these data systems to explore the impact of gestational syphilis in Bahia, Brazil, and tuberculosis in the Western Cape province of South Africa, and the coverage and effects of screening interventions. Teams will include data curators, analysts and scientists, who will perform data discovery and processing, alongside epidemiologists, clinicians and public health specialists, who will perform epidemiological analyses and community engagements.
Alexa Heeks and colleagues of the Health Foundation of South Africa in South Africa, together with Maurício Barreto and colleagues of Fiocruz in Brazil, will employ real-world data from two large countries of the Global South to develop a common data model of infectious diseases affecting pregnant women to identify causes and aid intervention development. Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS), together with the Western Cape Provincial Health Data Centre (WCPHDC), have built data systems to utilize routinely collected health data for exploring disease impacts. They will leverage these data systems to explore the impact of gestational syphilis in Bahia, Brazil, and tuberculosis in the Western Cape province of South Africa, and the coverage and effects of screening interventions. Teams will include data curators, analysts and scientists, who will perform data discovery and processing, alongside epidemiologists, clinicians and public health specialists, who will perform epidemiological analyses and community engagements.
Seth Adu-Afarwuah of the University of Ghana in Ghana and Julie Croff of Oklahoma State University Center for Health Sciences in the U.S. will assess the effects of nutritional supplementation on adolescent brain development in low-resource settings to support interventions. Nutritional behavior majorly impacts the rapid stage of adolescent neurodevelopment, which in turn impacts future generations through effects on maternal and paternal nutritional status, cognition and parenting. However, little is known about typical adolescent neurodevelopment in low- and middle-income countries, where 90% of the world’s adolescents live. They will recruit 40–60 post-pubertal adolescents in Accra, Ghana, measure their corticolimbic system development over nine months, and assess their problem-solving, planning and cognitive functioning. In another cohort of 40–60 post-pubertal adolescents, they will measure adherence to an eight-month twice-daily micronutrient supplementation program and associated nutritional outcomes.
Julie Croff of Oklahoma State University Center for Health Sciences in the U.S. and Seth Adu-Afarwuah of the University of Ghana in Ghana will assess the effects of nutritional supplementation on adolescent brain development in low-resource settings to support interventions. Nutritional behavior majorly impacts the rapid stage of adolescent neurodevelopment, which in turn impacts future generations through effects on maternal and paternal nutritional status, cognition and parenting. However, little is known about typical adolescent neurodevelopment in low- and middle-income countries, where 90% of the world’s adolescents live. They will recruit 40–60 post-pubertal adolescents in Accra, Ghana, measure their corticolimbic system development over nine months, and assess their problem-solving, planning and cognitive functioning. In another cohort of 40–60 post-pubertal adolescents, they will measure adherence to an eight-month twice-daily micronutrient supplementation program and associated nutritional outcomes.
Brigida Rusconi of Washington University in the U.S. will determine whether female infants develop long-lived antibodies against gut bacteria that subsequently both protect against bacterial infections and promote healthy gut immune and microbiota development in their offspring. Enteric bacterial infections are leading causes of infant morbidity in low- and middle-income countries. Using their mouse model, they found that mothers lacking IgG antibodies, which normally develop before weaning, are unable to provide passive protection against enteric infections to their pups. They will adapt their microbial flow cytometry to test whether maternal serum IgGs react more strongly to infant gut bacteria, suggesting establishment in infancy, and whether they provide passive immunity during pregnancy. They will also analyze plasma from two-year-old infants to identify those with weak IgG reactivity and potential causes. Finally, using a malnutrition cohort in Pakistan, they will train local bioinformaticians and assess whether malnutrition inhibits anti-gut commensal IgG responses.
Sandra Agudelo-Londoño of the Pontificia Universidad Javeriana in Bogota, in collaboration with various partners across Colombia including Yadira Eugenia Borrero Ramirez at the University of Antioquia in Medellín, will apply gender-transformative and feminist-based approaches to data analysis to identify the structural barriers affecting women's health in Colombia. Women's health is a complex issue with biological, historical, sociocultural, economic, and political aspects. The Global South has few female data modelers and no training or mentoring networks for women. They have therefore assembled an interdisciplinary group of female scholars and will deploy five virtual training courses on an open and free educational platform, focusing on gender, feminism, and health data analysis, alongside political advocacy, and data-driven decisions. They will also create a health data feminist network and use an existing gender-specific health and social dataset to conduct a comprehensive analysis focused on health issues disproportionally affecting women.
Laeticia Toe of the Institut de Recherche en Sciences de la Sante in Burkina Faso will use a metabolomics profiling platform to identify new biomarkers that can be used to diagnose environmental enteric dysfunction (EED) in women of reproductive age. EED affects nutrient absorption and immune function and may cause adverse birth outcomes in pregnant women. It is widespread in deprived areas in low- and middle-income settings but is often undiagnosed because the gold-standard diagnostic method requires an invasive procedure by trained personnel. They will determine the prevalence of EED by performing ELISA on existing plasma, serum and stool samples from 80 women of reproductive age living in rural Burkina Faso. They will then apply untargeted metabolomics on the samples to identify biomarkers that can be integrated with inflammatory markers and sequencing data and cross-validated for large-scale diagnoses of EED in women from low-resource settings.
Daniel Kiboi of the Jomo Kenyatta University of Agriculture and Technology in Kenya will assess whether a novel mutation in the human malaria parasite, Plasmodium falciparum, can be used as a marker to identify drug-resistant malaria and protect key antimalarial drugs. Emerging P. falciparum variants resistant to the three frontline drugs kill millions of people annually but are hard to detect. A better understanding of how these variants resist the actions of existing drugs can help to develop more effective drugs. They previously used a mouse malaria model to produce Plasmodium parasites resistant to all three main drugs and identified the candidate mutated protein likely causing this resistance. They will use in silico bioinformatics analysis, CRISPR/Cas9 approaches, and in vitro drug susceptibility assays to evaluate and validate this mutant protein and determine its role in drug resistance in the human malaria parasite.
Yingda Xie of Rutgers New Jersey Medical School in the U.S. and Joaniter Nankabirwa of Makerere University in Uganda will use CRISPR-based technology to monitor respiratory, food-borne and antimicrobial-resistant pathogens in Ugandan wastewater. A recent Ebola outbreak in Uganda highlights the need for routine multi-pathogen surveillance. However, the vast quantities and diversities of microbes in wastewater make it hard to identify those that might cause deadly outbreaks. They will combine CRISPR-based diagnostics with the recently developed multiplex assay, Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (CARMEN), which enables highly sensitive and specific detection of over 150 nucleic acid sequences from dozens of samples in parallel. They will assess the performance of a field-deployable CRISPR assay to monitor specific pathogens in hospital sewage lines of Mulago Hospital. They will also leverage CARMEN to broadly survey for high-priority outbreak pathogens, including Ebola and yellow fever, in Kampala’s regional wastewater sources.
Amira Kebir of the Pasteur Institute of Tunis in Tunisia will create an African-based and -led learning and research network that links Francophone and Anglophone African research institutions to strengthen the capacity and ecosystem for modeling and analyzing women's health in Africa. They will train eight Ph.D. and Postdoctoral researchers in an intra-African collaboration to use modeling approaches on available datasets that can inform public health decisions. They will also establish a summer school and workshops for training up to twenty students. These trainees will be incorporated into modeling groups by partners in northern, western, central, and eastern Africa that will apply mathematical modeling and gender-based data analysis to investigate four infectious disease areas that highly impact women, namely human papillomavirus, hepatitis B virus, COVID-19, and antimicrobial resistance. They will also build a software platform to standardize data collection and manage project information and data security.
Berge Tsanou of the University of Dschang in Cameroon will support trainee mathematical modelers in epidemiology, particularly women, to strengthen capacity and to investigate health problems related to human papillomavirus (HPV) and cervical cancer (CC) in four Central-East African countries. Both HPV and CC are affected by HIV, all of which disproportionately affect women, particularly in sub-Saharan Africa. However, the nature of this interplay is largely unknown. They will synergize efforts across sub-Saharan Africa and use modeling approaches to study the co-evolution, prevention, and diagnosis of these diseases to enable earlier-stage treatments. They will support 30 master’s students, 14 PhD students, and three Postdoc fellows, at least 70% of whom will be female, and hold workshops to engage stakeholders and support evidence-based policymaking. They will also develop a dashboard and interactive software for ongoing disease surveillance in the region.
Praveen Devarsetty of the George Institute for Global Health in India will integrate an LLM into their SMARThealth Pregnancy application to enable two-way communication support for frontline health workers to improve healthcare services for pregnant and postpartum women in India. Reducing maternal and newborn mortality and morbidity is a global priority, particularly in low- and middle-income countries where information about medical conditions and pregnancy symptoms is difficult to access in simple terms and local languages. Together with experts, they will create an "encyclopedia" of pregnancy advice based on Indian and WHO guidelines, integrate ChatGPT-4 into their SMARThealth Pregnancy application, and evaluate the application for providing high-quality and contextually relevant healthcare information and services following prompts from healthcare workers.
Imad Elhajj of the Humanitarian Engineering Initiative of the American University of Beirut in Lebanon will use Large Language Models (LLMs) to develop an interactive community health promotion platform with a chatbot that provides accurate health messages and real-time responses to queries on platforms like WhatsApp to vulnerable populations in Lebanon and Jordan. They will process texts from trusted websites, documents, and other text repositories, such as UNICEF and the WHO, into smaller text segments. These segments will then be converted into fixed-length vectors that capture their semantic meaning and contextual relationships. To generate answers, the GPT-3.5/4 model will retrieve the relevant vectors based on the user's query and use them together with the context taken from the conversation history. They will first evaluate the platform internally to ensure the relevancy, coherence and accuracy of the generated messages, and then conduct a pilot study with a small representative group from the target communities.
Maryam Mustafa of the Lahore University of Management Sciences in Pakistan will build a voice-enabled, mobile phone-based, conversational AI assistant, Awaaz-e-Sehat, for maternal healthcare workers in Pakistan to create and manage detailed electronic medical records. Pakistan has among the poorest pregnancy outcomes worldwide. The lack of documented medical records of pregnant women seeking care makes it challenging for doctors to provide accurate diagnoses and contextualized care based on socio-economic and lifestyle factors, which also play a vital role in maternal health outcomes. They will develop a proof-of-concept system comprising an intuitive user interface speech recognition module and a text recognition module to record audio responses in different languages following specific prompts. The system will then convert responses into text and populate a template electronic medical record in Urdu. Awaaz-e-Sehat will be evaluated by maternal healthcare workers at Shalamar Hospital for its ability to collect records from 500 patients.
Nirmal Ravi of EHA Clinics Ltd. in Nigeria will develop and test scalable and cost-effective ways to use large language models (LLMs) such as ChatGPT-4 to provide “second opinions” for community health workers (CHEWs) in low- and middle-income countries (LMICs). These second opinions would mirror what a reviewing physician might advise the provider in question after seeing or hearing their initial report. If LLMs can enhance the capabilities of CHEWs in this way, it could improve patient outcomes, free high-skill providers for other tasks, and mitigate the serious shortage of qualified health personnel in many LMICs. The specific outcomes of this project will be: a proof of concept that LLMs can be integrated within LMIC healthcare systems to improve quality of care; a proof of concept of a system architecture for LLMs that can be scaled up and deployed progressively in LMIC healthcare systems; and an initial understanding of the capacity of current LLMs to interact with CHEWs in LMIC settings.
Henrique Araujo Lima of the Universidade Federal de Minas Gerais in Brazil will develop a tool to systematically assess the accuracy and clarity of responses generated by Large Language Models (LLMs) to common questions on maternal health to increase their value in settings with limited healthcare access. To improve LLMs, it is essential to ensure the information they provide is both reliable and understandable, and for purposes such as health, LLMs will only be successful if both healthcare providers and users are confident about their benefits. They will collect the most common types of questions about maternal health in English, Portuguese, and Urdu, and submit them to the LLM. The quality of the answers will then be evaluated by medical experts from the U.S., Brazil and Pakistan, and the readability of the answers will be evaluated by individuals and a software model.
Shashi Jain of the Indian Institute of Science in India in collaboration with Uma Urs from Oxford Brookes University in the United Kingdom along with colleagues from Akaike and Kotak Mahindra Bank also in India, will build a GPT-enabled AI bot called SATHI, which stands for Scheme, Access, Training, Help, and Inclusion, to deliver information on the latest government financial schemes that support sectors, like micro-enterprises and farms, to potential customers and providers in rural and suburban India. Together with several partners, they will capture data and provide context to SATHI to enable it to answer queries related to financial schemes. They will also use a translation module so it can understand voice queries and respond with an audio answer in the local language. They will perform a field test at a bank branch to compare the use of SATHI alone with a human financial expert and with semi-experts supported by SATHI. They will collect data on customer satisfaction and their follow-up actions using standard field research methodology, including oral interviews and survey questionnaires.
Theofrida Maginga of the Sokoine University of Agriculture in Tanzania will develop a ChatGPT-powered Swahili chatbot for smallholder farmers with limited literacy and scarce resources in Tanzania to detect crop diseases quickly and easily. Maize is one of the most important crops in Tanzania and generates up to 50% of rural cash income. Several diseases that afflict maize are hard to detect visually, leading to substantial losses in crop productivity and income. They will integrate AI with Internet of Things (IoT) technologies that use non-invasive sensors to monitor the non-visual early indicators of diseases, including volatile organic compounds, ultrasound movements, and soil nutrient uptake. They will also develop and integrate a Swahili chatbot to interact with farmers in their local language in a culturally-sensitive manner and perform model validation and field testing.
Sophie Pascoe of Wits Health Consortium (Pty) Ltd. in South Africa, with support from the organizations, AUDERE in the U.S. and the Centre for HIV and AIDS Prevention Studies (CHAPS) in South Africa, will develop a Large Language Model (LLM)-based application, Your Choice, that interacts with individuals in a human-like way to respectfully obtain their sexual history and improve the accuracy of HIV risk assessments to control the epidemic in South Africa. Gathering accurate sexual history is essential for assessing HIV risk and prescribing preventative drugs but is challenging due to concerns about stigma and discrimination. Your Choice, which stands for Your Own Unique Risk Calculation for HIV-related Outcomes and Infections using a Chat Engine, leverages an LLM to ensure privacy and confidentiality, improve the accuracy of risk assessments, and increase awareness of preventative treatments. This solution would provide 24/7 access to an unbiased and non-judgmental counselor for marginalized and vulnerable populations specifically, greatly reducing the barriers and concerns around seeking advice. They will co-design the app with at-risk populations and evaluate a prototype using 550 public sector healthcare providers and clients.
Tamlyn Roman of Quantium Health in South Africa will use generative AI and Large Language Models (LLMs) to develop an automated analyst that integrates disparate health datasets and automates data analytics to support evidence-based decision-making in public health. Although there is a relative abundance of health-related data in South Africa, it is difficult to use effectively because the datasets are not standardized and analytics capacity to support policy- and decision-making is limited. They will source datasets for the analyst and assess the LLM's ability to automate checks and link multiple datasets. Improving interoperability between datasets will enable unique correlations to be identified between separate social indicators, which are currently recorded in distinct databases. They will also develop a user-friendly platform for output generation and visualization.
João Paulo Souza of the Fundação de Apoio ao Ensino, Pesquisa e Assistência in Brazil will determine whether Large Language Models (LLMs) can be utilized as accurate information sources to guide healthcare provider decision-making. Frontline health workers must make real-life care decisions by distinguishing between relevant and irrelevant information and contextualizing it to their setting. This is particularly challenging in remote areas with limited healthcare specialists. To support them, an information program, the Formative Second Opinion (FSO), was developed to produce curated evidence summaries based on a large repertoire of real-life clinical queries. An updated version of this program is now being developed to combine a mobile messaging platform with LLMs for regions with limited internet connectivity and computer access. Using a mixed-methods study approach, they will evaluate the accuracy of the evidence summaries generated by ChatGPT-4 and those created by humans to 450 randomly selected clinical questions.
Livia Oliveira-Ciabati of the Sociedade Beneficente Israelita Brasileira Albert Einstein Hospital in Brazil will leverage AI to produce guidance and monitoring tools for less-experienced or overworked health professionals providing prenatal care via telemedicine to people from minority groups and people with greater social vulnerabilities. During the COVID-19 pandemic, Brazil's maternal mortality rate jumped to 110 deaths per 100,000 live births, which is far from their target of reaching 30 deaths per 100,000 live births by 2030. To increase the quality of prenatal care, they will integrate LLMs with an API for converting and preparing voice data and develop a simple interface to present suggestions and results to the healthcare professional. The training database will be developed using protocols defined and validated by the scientific community, including from the Global South. They will test their model with health professionals by comparing the professional decisions with the AI's suggestions which they will then follow up with a randomized clinical trial.
Alain Ndayishimiye of the Center for AI Policy and Innovation Ltd. in Rwanda will integrate a translation model with GPT-4 to produce a health service support tool in the national language, bypassing the need to build language-specific LLMs from scratch. LLMs have broad and powerful applications for improving public services such as education and healthcare by bridging information gaps across different cohorts. However, to create an impact in Rwanda, LLMs must be able to converse in Kinyarwanda, and current approaches to train LLMs in relatively minor languages are too expensive for low-resource nations. They will develop a support tool for community health workers focused on malnutrition leveraging GPT-4 as a knowledge base and an Mbaza English-Kinyarwanda translation model. The integrated support tool will be evaluated by assessing the quality of its translations.
Nana Kofi Quakyi of the Aurum Institute in Ghana will develop an AI-powered decision support tool for antibiotic prescribers to improve appropriate antimicrobial usage and combat antimicrobial resistance (AMR) in Ghana. AMR is a major public health concern, with the highest mortality rates occurring in Africa. To address this, Ghana's National Action Plan for Antimicrobial Use and Resistance (2018) identified the need for support tools that provide personalized, adaptable, and context-sensitive recommendations. They will develop an interactive, AI-powered clinical decision support tool that allows prescribers to enter prompts, respond to system queries, and receive personalized, real-time antibiotic prescribing recommendations, such as drug, dosage, and duration of administration. The model will be trained with a comprehensive dataset consisting of clinical guidelines, research data, expert opinions, and surveillance evidence that has been reviewed for its representativeness. The proposal includes field testing, iterative refinement, and engagement with stakeholders to ensure effectiveness and scalability.
Nirat Bhatnagar of the Belongg Community Ventures Private Ltd. in India, in collaboration with colleagues at ARTPARK also in India, will develop a Large Language Model (LLM)-based tool to enable development practitioners, funders, and researchers to adopt more equitable approaches, particularly addressing the intersections of marginalization. They will assemble a comprehensive and trusted corpus of development research papers, reports, and media articles and use it to build a user-friendly website and a backend ChatGPT 4.0 API-based LLM model. Users will be able to upload their draft research or program proposals and receive tailored recommendations on how to increase inclusivity across dimensions such as gender, disability, caste, religion, ethnicity, and sexual orientation. The tool will also connect users with researchers and experts with experience living with marginalized identities.
Yogesh Hooda of the Child Health Research Foundation in Bangladesh will use AI-based tools to teach low- and middle-income scientists to perform modeling and prediction studies in public health, which are dominated by researchers in the Global North. The codes generated during modeling studies are not often shared amongst researchers, making the methods difficult to learn. They found that ChatGPT could produce a code using a published model in just three weeks with only a beginner-level programmer and a biostatistician. Using epidemiological and demographic data and medical records collected from a catchment area, they will adapt published code with the help of ChatGPT to predict the impact of introducing specific vaccines in Bangladesh. They will also develop a curriculum, covering the basics of ChatGPT, data preprocessing and modeling techniques, for a course that they will pilot with public health professionals and students. All materials will be openly available in Bangla and English.
Faisal Sultan and Sara Khalid of Shaukat Khanum Memorial Cancer Hospital and Research Centre in Pakistan will leverage the power of open-source AI Large Language Models (LLMs) to extract insights more quickly and easily from large volumes of clinical data to support medical decision-making and minimize health disparities in South Asia. Healthcare systems in South Asia have limited resources and the critical information required for decision-making is often buried in patient notes (such as family history, drug adverse events, and social, behavioral, and environmental determinants). Health disparities are also prevalent, particularly for women and children. They will leverage existing LLMs specifically designed for health data and use the SKMCH&RC database, which contains both free text and structured data for 250,000 patients, to ensure that key subjective information, such as family history, and electronic health records are included. They will validate their model using available data on COVID-19 infections in Pakistan and evaluate its performance in terms of accuracy and speed.
Khoa Doan of VinUniversity in Vietnam together with Helen Meng, Viet Anh Nguyen, and colleagues from the Centre for Perceptual and Interactive Intelligence and The Chinese University of Hong Kong, both in Hong Kong; in collaboration with the Hanoi Obstetrics & Gynecology Hospital in Vietnam, will build a conversational AI chatbot to scale up gynecological healthcare support for women and LGBT+ communities in Vietnam. Access to gynecological healthcare in Vietnam is limited, particularly in remote regions and for minority groups due to high costs, low investment, social stigmas, and misinformation. They will build a tool to provide informational and psychological support, adapted to Vietnamese linguistic and cultural contexts and able to operate in low-resource settings. The chatbot will consist of a scientific database, a GPT-like conversational system, a voice generation engine, and a sentiment analytics module to evaluate the psychological traits of the user. It will be capable of empathetic dialogues that encourage users to share their symptoms. Data from patient-clinician dialogs will be used as a reference for the design of an initial patient-AI prototype.
Nneka Mobisson of mDoc Healthcare in Nigeria will integrate ChatGPT-4 into their chatbot, Kem, which provides virtual self-care coaching for low-income women of reproductive age in Nigeria, to improve its accuracy and capacity to respond to queries with evidence-based information. The burden of maternal deaths in Nigeria remains inequitably high with many risks encountered even before conception, highlighting the importance of supporting self-care. They will assess the accuracy and empathy of Kem+ChatGPT's responses to diverse inquiries from women, its improved ability to triage based on reproductive stages and risk factors, and the effectiveness of human health coaches leveraging ChatGPT-4 as a resource for answering more complex questions. This will involve field testing with a cohort of 300 women of reproductive age. They will also hold a series of iterative user-testing workshops across the states with at least 160 members from communities to observe usability and provide feedback for product refinement.
Martin Mwangi of Intellisoft Consulting Ltd. in Kenya will build an application-supported LLM to improve knowledge, attitudes, and practices surrounding the risk factors for non-communicable diseases (NCD) for young people in Kenya. NCDs constitute the leading cause of mortality globally, accounting for three-quarters of deaths worldwide. Many Kenyans lack information on NCDs and their major risk factors, which include unhealthy diet, physical inactivity, and harmful alcohol use. They will form an interdisciplinary Community Advisory Board, including government officials, researchers, and young people, to guide the design, analysis, and dissemination of the app. They will recruit Kenyans aged 18–34 from community-based sites, such as universities and malls, to evaluate the application's ability to improve knowledge, attitudes, and practices surrounding NCD risk factors.
Daphne Ngunjiri of Access Afya in Kenya will integrate ChatGPT into a virtual clinic application, mDaktari, to support clinicians and better respond to patient inquiries. Poor quality healthcare results in 5.7 million deaths in low- and middle-income countries, emphasizing the need to increase healthcare quality as well as accessibility. Their mDaktari platform combines a digital and physical healthcare network, telemedicine, and localized patient health data to support patients and clinicians in low-income communities from diagnosis to treatment. They propose to scale their approach using Large Language Models (LLMs) and anonymous patient data from multiple sources. This will increase the scope, speed, and quality of responses to patients' queries in their preferred language, and support clinicians to provide accurate diagnoses and treatments. They will work with end users during the design and pilot phases and verify the accuracy of the AI with human medical professionals.
Chinazo Anebelundu of DSN Ai Innovations Limited in Nigeria will develop a Science, Technology, Engineering, and Mathematics (STEM)-focused multimedia learning platform by leveraging GPT and DeepBrain text-to-video AI tailored to rural students to increase their engagement. Nigeria has an estimated 18.5 million students out of school, a population that can potentially be reached through this more engaging and personalized modality. This platform will integrate local contexts and nuances to enhance student comprehension of STEM subjects. It will also tailor the learning to the preferences of each student and employ visual activities such as interactive STEM laboratory simulations. For each subject, they will collect a diverse array of educational resources and construct the curriculum. This curriculum will then be transformed by Large Language Models (LLMs) into a script and then into video lessons that can be personalized to a specific student. The platform will be evaluated for the degree of student engagement and accuracy of content, amongst other measures, using their existing network of students and teachers.
Brenda Hendry of Boresha Live in Tanzania will integrate ChatGPT-4 into community radio to broadcast inclusive health messages across Tanzania to combat malaria. Tanzania is among the top ten countries with the highest malaria cases and deaths. Their control efforts are severely hampered by limited access to accurate health information among certain populations. Radio is very popular and reaches across rural and remote areas making it a powerful communication medium. To leverage this, they will train ChatGPT-4 with malaria health information and local contexts and behaviors collected from community leaders, healthcare professionals, and malaria control programs. ChatGPT-4 will then be able to produce accurate malaria-related information that respects cultural norms, language preferences, and local challenges. These messages will be broadcast by community radio stations that reach over 36 million people, and they will evaluate the impact on increasing knowledge and reducing malaria cases.
Amelia Taylor of Malawi University of Business and Applied Sciences in Malawi will employ Large Language Models (LLMs), including ChatGPT and MedPalm, to develop a tool to streamline the collection, analysis, and use of COVID-19 data. Collecting accurate and comprehensive data during a pandemic is critical for response efforts but the process is labor-intensive. During COVID-19 surveillance, there were also limited training materials available to explain specialized concepts for data collection to the multidisciplinary teams. To address this, they will leverage their experience in the operational aspects of COVID-19 data management in Malawi to develop an automated feedback tool that records high-quality, complete data while ensuring compatibility across diverse sources and destinations. Furthermore, together with frontline health workers and epidemiologists, they will create a knowledge map of symptoms and clinical terminologies to support clinicians, lab technicians and surveillance officers engaged in data collection.
Darlington Akogo of MinoHealth AI Labs in Ghana will leverage a multimodal Large Language Model (LLM) to generate accurate and comprehensive medical reports based on the analysis of medical images to reduce the need for manual reports and enhance diagnostic capabilities for radiologists and clinicians. African healthcare systems have excessively high patient-to-doctor ratios and prevalent diseases and severely inadequate numbers of radiologists. They will fine-tune a multimodal LLM applied to radiology and medical imaging using a supervised approach with a labeled dataset of medical images and corresponding reports collected from facilities across Ghana and Africa. The platform will enable interactive conversations with clinicians seeking answers to specific queries or clarifications regarding medical images. They will use metrics and humans to evaluate the model and assess its ability to generate accurate and comprehensive medical reports. They will also conduct field testing with clinicians and individuals from diverse demographics.
Essa Mohamedali and Kalebu Gwalugano of the Tanzania AI Community in Tanzania will use ChatGPT-4 to develop a chatbot and support tool to help healthcare workers adhere to the Integrated Management of Child Illness (IMCI) guidelines and access updates and alternative treatment options by linking them to the latest research via their mobile phones. Access to formal training on the IMCI guidelines is limited for healthcare workers, particularly in the private sector, and its duration makes it prohibitively expensive for companies. They will convert the existing guidelines and algorithms into a chatbot version and use the GPT-4 framework to connect to the latest research. They will engage healthcare workers during the development stage and then implement and field test the support tool at three private health facilities in rural, urban, and peri-urban areas of Tanzania, to assess its usability.
Amrita Mahale of ARMMAN in India, in collaboration with colleagues at ARTPARK also in India, will integrate an LLM-powered co-pilot into an existing learning and support application to improve the training of auxiliary nurses and midwives in India so they can better manage high-risk pregnancies. One woman dies in childbirth every twenty minutes in India. Many maternal and infant deaths could be prevented by improving access to critical care information and ensuring that health workers can detect risk factors and treat complications early on. They will fine-tune ChatGPT, or an open-source equivalent, with their existing content in English and Telugu, using machine translation models to provide personalized answers depending on the individual's training level. The design will be informed by user research studies and the application will be trialed with over 100 community health workers.
Minh Do of Fulbright University Vietnam in Vietnam will create a chatbot "NướcGPT" (Nước means water in Vietnamese) that combines cutting-edge AI tools with a user-friendly interface in the local language to support the management of salinity intrusion in the Mekong Delta. The Mekong Delta, home to 21.5 million Vietnamese, is suffering from increased saltwater intrusion caused by multiple factors including climate change. They will fine-tune GPT3.5 and GPT4 using data in English and Vietnamese collected from diverse sources including literature reviews, field studies of water-related problems, and technological solutions. This will bridge the gap between complex scientific knowledge and practical decision-making, empowering users to make informed choices. They will also build a website to host their chatbot and field test it with selected stakeholders, including government officials and farmers to evaluate its usability, accuracy, and ability to support decision-making processes.
Floris Sonnemans of Degas Ghana Limited in Ghana will apply AI technology to support African smallholder farmers to implement more climate-adaptive and regenerative agricultural (RA) techniques, such as crop diversification, and scale climate action across the continent. Africa only contributes 3.8% of global greenhouse gas emissions but experiences the harshest impacts, particularly on food production. However, protective RA techniques are relatively new and challenging to adopt, and there are not enough field agents to support farmers and respond to queries. To address this, they will integrate a Large Language Model (LLM)-based system trained on RA manuals into their existing agent-facing application. This system will provide an easy interface for farmers to access information and guidance to effectively implement RA practices, such as biochar application, minimal tillage, and permanent organic soil coverage. The application will be field tested amongst field agents and farmers.
Robert Korom of Penda Health Limited in Kenya will integrate ChatGPT-4 into their established patient communication system to increase consultation efficiency and the speed of delivering accurate health information in Kenya. Their existing chat-based digital health solution relies on a dedicated team of clinicians and call center agents to serve low-income Kenyans; however, increasing needs are leading to longer response times. They propose to blend the empathetic and intuitive nature of human interaction with the instantaneous, data-driven capabilities of AI to improve throughput, response times, and patient experience. This will create a hybrid model where clinical call center agents work hand-in-hand with AI. They will carry out a proof-of-concept involving a limited field test to monitor patient satisfaction, efficiency, and relevance of responses.
Olubayo Adekanmbi of Data Science Nigeria in Nigeria will develop a multilingual, voice-based chatbot to demystify complex financial concepts and provide customized financial support to informal traders, women business owners, and smallholder farmers in Nigeria. These groups are often disadvantaged due to their low income and literacy and are historically underserved by conventional financial systems. They will create a chatbot capable of recording transactions from verbal inputs, such as "I bought four oranges at N50 naira," and answering financial questions. Customized financial guidance will be communicated back to the end user via voice note using text-to-speech. They will engage users in the design process and build large, locally-orientated financial datasets. They will then merge speech-to-text technology and GPT learning capabilities with an AI-driven financial management tool. The chatbot will be tested using their existing network of informal traders, and the feedback used for refinement and improvement.
Joseph Mulabbi of Comzine Tech And Investments Limited - Dromedic Health Care in Uganda will use ChatGPT-4 to optimize the surveillance of zoonotic diseases and predict future pandemics. Zoonoses are infectious human diseases that originate from animals and represent over 75% of all emerging diseases. Predicting the emergence of a zoonotic disease currently requires manual monitoring of the dynamic interactions between humans and livestock, which is time-consuming, resource-intensive, and prone to delays. Together with relevant stakeholders, they will build DROMEDIC-AI, a ChatGPT-4 AI platform trained with large volumes of text from diverse sources, such as news articles, social media, and clinical notes, where farmers can upload photos of sick animals and receive advice. The platform will also generate risk assessments and maps of hotspots and inform health officials to help them better monitor potential outbreaks. They will collect user interaction data and feedback on its performance.
Mamadou Alpha Diallo of Cheikh Anta Diop University in Senegal will apply Large Language Models (LLMs) to improve decision-making, policy development, resource allocation and communication to help combat infectious diseases in Africa. They will use ChatGPT-4 to analyze and interpret epidemiological data, clinical records, and research literature to help predict outbreaks, identify priority areas for interventions, and evaluate the potential impacts of specific policies. The information produced will include tailored messages, educational materials, and real-time updates on disease trends and prevention strategies for healthcare workers, policymakers, and affected communities. This tool is expected to achieve the following impact in LMICs: enable faster, more accurate, and more inclusive decision making; strengthen the healthcare system at all levels from the healthcare worker to the policymaker; result in the reduction of disease burden; and reduce healthcare disparities through enhanced equity and access to information, resources, and interventions.
Daudi Jjingo of the Infectious Diseases Institute in Uganda will leverage generative AI to develop an interactive conversation-based platform to communicate the national guidelines for pandemic preparedness in a native African language to health workers to improve pandemic management. The national guidelines, currently available as a lengthy PDF, will be translated into a local Bantu language, Luganda, to improve accessibility to non-English speaking users, and converted into a data format for Large Language Models (LLMs) such as GPT-4. The data will include locally-relevant, medically-curated, and pre-approved information for pandemic preparedness, including prevention, detection, and treatment strategies. They will also build a user-friendly, dynamic interface for health workers to interact with the AI model as needed and use the information to guide interventions. Their platform will be field-tested by a group of 60 health workers at 20 clinical sites.
Joyce Nakatumba-Nabende of Makerere University in Uganda will leverage ChatGPT to provide tailored support to smallholder farmers in sub-Saharan Africa in their local languages. These smallholder farmers contribute up to 69% of household incomes, but they are vulnerable to the devastating effects of crop diseases and pests and lack the timely support required to combat such challenges. Digital technologies have been developed to help but they cover a limited number of crop types and languages. They will curate a dataset comprising 1,000 farmer-specific agricultural questions on pests and diseases, markets, and seed advisory services, in English and Luganda. They will then perform prompt engineering of ChatGPT to investigate its potential to provide targeted, accurate, and unbiased agricultural advice on a wide range of crops. The responses will be further fine-tuned and compared with responses from agricultural experts.
Cally Ardington of the University of Cape Town in South Africa will develop an AI-powered voice-recognition model that performs Early Grade Reading Assessments (EGRA) in low- and middle-income countries (LMICs). Seventy percent of children in LMICs do not learn to read in any language, which severely affects their overall education and future prospects. Reading assessments, such as EGRA, test children on letter-sound knowledge, word reading, reading connected text, and answering questions on that text. They are critical for supporting reading programs but are currently expensive and time-consuming because they are administered one-on-one. They will perform a pilot study to determine whether a new open-source voice recognition program developed by Facebook (wav2vec), which is especially useful for languages with little training data, can automatically evaluate speech production and assess children's early reading abilities in African languages. They will validate EGRA-AI by adding it to an existing 120-school field trial using standard EGRA.
Hugo Morales of Munai Health in Brazil will integrate OpenAI's ChatGPT-4 and other Large Language Models (LLMs) with Munai's Clinical Intelligence platform to help frontline healthcare providers adhere to guidelines for antimicrobial therapy and reduce antimicrobial resistance. Antibiotic-resistant bacteria cause over 20% of infections in Brazil. However, the antimicrobial stewardship programs designed to address this consist of complex protocols and there is little training for health workers in low-resource settings. The Munai platform incorporates machine learning into an application and web interface that is currently connected to 29 hospitals and hosts data from over 12 million medical encounters. They will incorporate conversational AI tools into the platform to enhance accessibility and usability. They will then convert institutional antimicrobial therapy protocols into a machine-readable format and incorporate patient data to allow for more personalized responses. The LLM will be evaluated by a prospective study engaging 20 physicians in a two-part simulated clinical trial.
Enrica Duncan of Mapa Do Acolhimento in Brazil will use AI to improve the influx of volunteer psychologists and lawyers to their support network, which provides mental health and legal support to women at risk of gender-based violence. In 2022, one woman died every six hours from gender-based violence in Brazil. They have built a network of 10,000 volunteers who have supported over 5,000 women. To expand this network, they have developed core training content using an inter-sectional feminist framework and will produce an equitable AI model to evaluate volunteers' backgrounds to better understand their skills and experiences. They will also incorporate localized knowledge to better serve all regions of Brazil. Machine learning will also be used to determine the optimal format to deliver training for each individual based on their engagement and responsiveness. These efforts aim to enhance the volunteer experience, improve knowledge absorption, and reduce turnover.
Tonee Ndungu of Kytabu Company Ltd. in Kenya will develop a comprehensive AI-powered mobile application, SOMANASI (derived from the Swahili words meaning "learn together") to provide personalized education to every student in Kenya. Kenya suffers from widespread educational inequities with many students failing to receive individualized attention. The application will harness ChatGPT-4 and act as an intelligent virtual tutor that delivers tailored content, adaptive learning experiences, and interactive guidance. They will collaborate with experts to design high-quality materials aligned with the Kenyan curriculum and cultural context. They will also engage students, teachers, and educational stakeholders in the design process, and mitigate bias by considering the full diversity of the student population. They will pilot test SOMANASI across a diverse student population in ten schools to evaluate its ability to enhance learning outcomes.
Moinul Haque Chowdhury of CMED Health Limited in Bangladesh will integrate a multilingual AI engine into their existing digital healthcare platform, SuSastho, to produce a chatbot that provides secure access to sexual, reproductive, and mental health care for adolescents. Bangladesh has the highest adolescent pregnancy rates globally, and 16-18% of its adolescents suffer from mental disorders; however, little to no sexual, reproductive, or mental health care is available. They will use an open-source language model that operates in multiple languages, including Bangla. Together with experts, they will compile common queries and sought-after information regarding education, early marriages, contraceptive use, adolescent pregnancies, and sexual and mental health, and collect training data for the AI model. The chatbot will also be designed to assess health risks and make referrals. They will conduct beta testing, clinical validation, user acceptability testing, and cultural validation through consultative workshops.
Suzanne Staples of the THINK Tuberculosis and HIV Investigative Network (RF) NPC in South Africa and Kristina Wallengren of THINK International in Denmark will produce a toolkit that leverages ChatGPT for the analysis and interpretation of health program data in low- and middle-income countries (LMICs). Due to resource constraints, data analysis takes a back seat to diagnostics and treatments and is a scarce skill in LMICs, particularly in the public health sector. In addition, health data management is hindered by manual and fragmented electronic datasets. They will work with end-users, including program managers and decision-makers, to generate a toolkit that utilizes ChatGPT in data analysis to drive evidence-based decision-making, and aid in the early detection of disease outbreaks, initially focusing on the TB program. They will assemble the most frequent queries by various stakeholders to identify real priorities for program management and evaluate the ability of ChatGPT to analyze and interpret routine TB program data.
Christophe Bocquet of Dalberg Global Development Advisors (K) Ltd. in Kenya will develop VIDA PLUS, a chatbot accessible via WhatsApp that delivers public health information by live interaction to health officials, particularly in rural areas, to support their decision-making. Accessing relevant public health information is often challenging for health workers in rural areas who have limited access to technology and data literacy. Initially in Guinea, they will integrate GPT-3.5-Turbo into the national health management information system (HMIS), which comprises data on health outcomes, health facilities and utilization, and disease surveillance. This will enable health officials to ask questions on topics such as maternal health, infections, vaccinations, and hospitalization, and receive tailored answers via WhatsApp. Health officials will be involved in the design, deployment, and testing stages, and they will also plan the scale-up, including a cost and impact analysis.
Michael Leventhal of the Association RobotsMali in Mali will determine whether ChatGPT-4 can support curriculum development and teacher training to improve literacy in Mali, which has 65% illiteracy. The West African language Bambara is the most widely spoken language of Mali, but there is almost no literature in Bambara and few Malians can read their mother tongue. Education is provided almost entirely in French, a language most Malians do not understand, and in a cultural context foreign to Malian children. They will use ChatGPT-4 to generate graded, culture-specific written stories for children in Bambara along with linked pedagogical material for teachers to improve lesson quality. They will evaluate the material with students and teachers using available quantitative tools and assess its ability to improve educational outcomes.
Neal Lesh of Dimagi South Africa (Pty) Ltd. in South Africa will create an LLM-powered coach tailored to frontline workers that offers training, performance feedback, and encouragement to support their health and improve their productivity. Frontline programs serve billions of people; however, they rely on a hard-working, often overburdened workforce that receives limited support, particularly in low- and middle-income countries. They will work with 10–20 community health volunteers in Malawi to co-design three variations of the LLM-powered coach using their rapid LLM-building platform. They will assemble content on early childhood development and the Kangaroo Mother Care method. They will then design professional development curriculums to strengthen existing skills; teach new skills, such as financial management; and build resilience skills to encourage self-care and well-being. They will test the coaching bots on 100 frontline workers to evaluate safety, accuracy, usability, and added value.
Bishesh Khanal of the Nepal Applied Mathematics and Informatics Institute for Research in Nepal will assess LLMs for their ability to provide accurate information on sexual, reproductive, and maternal health (SRMH) topics in Nepali to the general public and female community health volunteers. In Nepal, limited access to SRMH resources due to language barriers and social stigmas has led to increased numbers of unsafe pregnancies and sexually transmitted diseases. While LLMs could be helpful, they have many limitations, particularly in low-resource, non-Western settings. These include inaccurate responses, poor performance in non-English languages, responses generated largely from Western-cultural contexts, and large computational resource requirements. Together with a local multidisciplinary team, involving AI scientists, domain experts, and community engagement experts, they will integrate four chatbots into a simple mobile-friendly web-interface, and evaluate their performance to anonymous chat queries from 5,000 individuals.
Leonora Tima of Kwanele - Bringing Women Justice in South Africa will develop a mobile application and chatbot to provide understandable legal information on gender-based violence (GBV) to vulnerable groups, including high school learners, young women, survivors of GBV, members of the LGBTQIA+ community and sex workers. South Africa faces disproportionately high rates of GBV but lacks access to justice and understandable legal information for survivors. They will integrate GPT4 and OpenAI's Large Language Model (LLM) with front-end applications, such as WhatsApp and Facebook, to guide users through the complex judicial system using everyday language. They will run community training and onboarding events to demonstrate the technology and introduce people to the application and they will run focus groups, workshops and interviews to support the design of the tool and build the datasets.
Scott Mahoney of The Health Foundation of South Africa will create an application that combines human expertise with AI technology to produce clinical recommendations from published medical evidence to be used as a decision-support tool for healthcare professionals in low- and middle-income countries. Currently, producing guidelines and support tools relies on manual reading and synthesis by individual clinicians or editorial teams, which is time consuming and can lead to biased coverage. The application will use ChatGPT-4 and be able to analyze text-based medical evidence in various formats, extract relevant clinical recommendations, and formulate clinician-validated clinical decision support algorithms for frontline healthcare workers in near-real time. This will improve the speed, accuracy, and inclusivity of decision-making. They will validate the application by comparing its performance with recommendations provided by their clinical editorial team.
Suhani Jalota of the Myna Mahila Foundation in India will build a chatbot, Myna Bolo, by incorporating Large Language Models (LLMs) into their health application to provide tailored sexual and reproductive health services through smartphones, via text or audio, in local languages to women in India. In India, 71% of girls report not knowing about menstruation before their first period. This is because of limited access to unbiased information due to stigma, discrimination, and lack of resources. Information needs to be non-judgmental, confidential, accurate, and tailored to those living in urban slums. They will incorporate LLMs by integrating Google Bard into their application. Women can then ask questions and receive tailored responses that are considerate of their backgrounds and limited smartphone access, and respectful of their privacy. They will select accurate source material, relevant to local women in different languages, and incorporate fact-checking capabilities and maps for providing referrals and treatments.
Henrique Dias of the Instituto de Inteligencia Artificial na Saude in Brazil will determine whether AI can produce an accurate hospital discharge summary to ensure that essential information is passed to the next healthcare provider and patient care is maintained. Discharge summaries are often incomplete, unclear, or delayed in terms of their delivery due to the document construction process. They will test two AI models to produce discharge summaries - one trained by health professionals completing electronic medical records, and the other trained with 46 GB of data in Portuguese, corresponding to 38 million clinical notes from 70 hospitals. They will perform a retrospective, non-inferiority, single-blind study to compare the quality and speed of the discharge summaries produced by both AI models with those produced by medical professionals.
Carl Lachat of Ghent University in Belgium and Firehiwot Workneh of Addis Continental Institute of Public Health in Ethiopia will assess nutrient gaps in adolescent girls and the feasibility of providing supplements to break intergenerational cycles of poor growth and development in Burkina Faso and Ethiopia. Annually, around 21 million adolescent girls in low- and middle-income countries become mothers, with their infants at increased risk of impaired development. This may be caused by nutrient competition as both pregnancy and adolescence are nutrient-demanding phases. Supplementation with balanced energy-protein (BEP) during pregnancy increases birth weight with sustained benefits during infancy, but how this intervention could be tailored to adolescent girls is unclear. They will use a probability of adequacy approach to evaluate the diets and nutrition of 200 adolescent girls. They will also assess adolescent girls’ acceptability of paying for and taking BEP supplements in rural settings using group discussions and questionnaires.
Project intends to identify highly specific, and tandem repeats in the genomes of W. bancrofti, B. malayii, B. timori, and other human filarial agents, to develop a field-deployable, diagnostically robust molecular Point-of-Care test for Lymphatic Filariasis detection.
Herman Pontzer of Duke University in the U.S. and Patricia Ukegbu of the Michael Okpara University of Agriculture in Nigeria will track the daily energy expenditures and requirements, and nutritional statuses of adolescent girls in rural Nigeria to help support their growth and development. Low- and middle-income countries suffer greatly from undernutrition, poor dietary practices and food insecurities, but are also experiencing increased obesity and unhealthy weight gain. Adolescents are particularly vulnerable to poor nutritional health but often neglected in nutritional program planning due to a lack of accurate data. To address this, they will recruit fifty female adolescents aged 13–18 from selected urban and rural schools in Abia State and measure their daily energy expenditure (kcal/d) and body composition (fat%) using the gold-standard doubly-labeled water method. This will be combined with dietary, food security and physical activity assessments to develop an accurate evaluation of nutritional health.
Kathryn Holt of the London School of Hygiene and Tropical Medicine in the United Kingdom and Senjuti Saha of the Child Health Research Foundation (CHRF) in Bangladesh, along with FTIR experts Luísa Peixe and Angela Novais from the University of Porto, will establish Fourier-Transform infrared (FTIR) spectroscopy in a pediatric microbiological diagnostics laboratory in Bangladesh to support clinical and infection control decisions. FTIR is a relatively low-cost, reagent-free technique that can discern different pathogen strains when combined with attenuated total reflection (ATR). They will set up a Spectrum Two FTIR-ATR instrument, on loan from PerkinElmer at the CHRF, train personnel, and use it to acquire spectra from approximately 1,500 isolates from their biobank to identify three clinically important pathogens: Klebsiella, Acinetobacter, and Salmonella. They will assess reproducibility across different users and laboratories on a validation set of 100 sequenced isolates, and finally test whether FTIR can identify pathogens directly in blood to produce more rapid results.
Rose Hayeshi of North-West University in South Africa will test whether humanized mouse models harboring selected gene variants specific to indigenous African populations can be used to identify novel therapeutics that will be effective in this population before advancing into clinical trials. Most medicines are developed and tested in European and Asian populations, which can lead to approved drugs that cause adverse reactions or are ineffective in African populations. Cytochrome P450 (CYP) enzyme allelic variants are common in African populations and may affect drug responses. She will use humanized mouse models expressing CYP2B6 and the CYP2B6*6 allelic variant, which is common in African populations, to test whether they can recapitulate specific drug responses observed in vitro and in humans using physiologically-based pharmacokinetic (PBPK) modeling.
Vinicius de Araujo Oliveira of Fiocruz in Brazil will develop a framework for the re-use of large clinical and administrative datasets to enable comparative analysis of COVID-19 vaccine safety and effectiveness in Brazil and in Pakistan, with colleagues at Shaukat Khanum Memorial Cancer Hospital and Research Centre there, to improve pandemic responses and promote data-driven evidence generation in the Global South. Monitoring vaccinations across different settings is crucial for containing pandemics. However, comparative analysis of large health datasets in different scenarios is challenging due to concerns around safety and reproducibility and the loss of the context in which the data was collected, which can affect research results. They will adapt data science standards and tools to different local health system scenarios and run individual and joint vaccine effectiveness analyses for the two countries to assess compatibility and reproducibility of the findings. They will also build a public data visualization dashboard for health managers and policymakers to monitor the pandemic, particularly in vulnerable populations.
Vincent Cubaka of Partners In Health in the U.S. will build robust data governance structures to enable the utilization of electronic medical records from multiple countries for research purposes to improve health. So-called FAIR (Findable, Accessible, Interoperable, Reusable) data principles enhance the value of personal medical records for research, and CARE principles were developed to protect the owners of these data. However, the rigidity of these principles can create conflicts, which can make it difficult, for example, to open access to datasets across different countries. To address this, for their current project studying the impact of COVID-19 on chronic care patients across four low- and middle-income countries, they will develop data governance structures and set-up a multi-country community oversight committee to enable full access by researchers to appropriately de-identified individual-level data on a suitable platform.
Wyckliff Omondi from the Ministry of Health in Kenya will integrate a neglected tropical disease (NTD) surveillance program into the national blood donation program as a more cost-effective mechanism to monitor lymphatic filariasis and other endemic NTDs in Kenya. Kenya is on course to eliminate lymphatic filariasis using mass drug administration programs. Certifying regions as disease-free requires careful post-treatment assessments. To support this, they will work with the Kenya National Blood Transfusion Service, which collects, tests, and distributes blood across the country, to collect small, normally discarded blood samples from regional centers and test them for lymphatic filariasis, dengue and chikungunya in their central laboratory. As well as supporting eradication efforts, this routine testing will provide an early warning system by identifying distribution patterns and prevalence of dengue and chikungunya, which have histories of outbreaks in coastal regions.
Ida Viktoria Kolte of Fiocruz in Brazil will employ metagenomic next generation sequencing (mNGS) for the analysis of sputum and blood samples from Indigenous patients to identify the causes of severe lung infection in the rural Amambai district. Brazil's one million Indigenous people suffer a disproportionate burden of infectious and respiratory diseases. Lung infections are challenging to diagnose because they can be caused by viral, bacterial and fungal pathogens and are often associated with co-infections. They will collect samples from 170 patients aged over 18 years presenting with symptoms of severe lung infection from five locations and subject them to next generation sequencing to identify the microorganisms present. They will also use implementation research to identify any cultural barriers that have restricted current diagnostic and therapeutic practices to help more effectively implement the new metagenomic next generation sequencing technology into clinical practice.
Ifeoluwa Olokode of Helium Health in Nigeria will develop a digital antenatal risk stratification tool to determine the risk of maternal mortality for pregnant women in Nigeria and link them to appropriate care services to reduce maternal death rates. Nigeria has one of the highest burdens of maternal mortality, with the biggest driver being a delay in the decision to seek health care. They will develop the stratification tool to incorporate patient demographic, behavioral, and obstetric clinical information into existing models to predict antenatal risk levels and communicate them to patients to aid earlier decision making. The tool will be adapted to different demographics, such as women with no education, and integrated into an existing digital patient healthcare platform. It will also connect women with financial support services. They will implement their tool across Lagos, Kano, and Akwa Ibom states over twelve months to demonstrate proof of concept.
Jennifer Fitzpatrick of Zambart in Zambia will design and implement a one-step multiplex whole genome sequencing platform for the diagnosis of female genital schistosomiasis (FGS), sexually transmitted infections (STIs) and vaginal microbiome analysis in Zambia. FGS is caused by Schistosoma haematobium and affects around 56 million women in sub-Saharan Africa. Current diagnostic capabilities for STIs and FGS are inadequate and many patients are either incorrectly treated, overtreated or receive no treatment at all. They will use 525 self-taken vaginal swabs to develop the sequencing assay and follow-up with self-taken cervicovaginal swabs and S. haematobium eggs taken from up to 2,000 sexually active girls and women aged 15 to 50 for further development and implementation of the platform to enable the rapid identification of known and new pathogens. They will also characterize the cervicovaginal flora to gain insights into its role in sexual and reproductive health.
Elizabeth Batty of the University of Oxford in the United Kingdom will use metagenomic next generation sequencing to identify pathogens in patient samples that are negative by all other diagnostics, to better understand the causes of febrile illness in South and Southeast Asia. Although studies have identified a broad spectrum of pathogens underlying non-malarial febrile illness, the cause of fever remains unknown in more than half of patients. Febrile illness causes substantial morbidity and mortality, and correct diagnoses are needed to ensure that patients receive the appropriate treatments. They will collect samples in multiple healthcare centers in Bangladesh, Lao PDR and Thailand, and use multiplex PCR and serological tests that detect the most common causes of acute fever. Up to 300 samples that test negative using these approaches will be sent to the central Mahidol-Oxford Tropical Medicine Research Unit laboratories in Bangkok for metagenomic sequencing and bioinformatic analysis.
Marion Jourdan of Danone Nutricia Research in the Netherlands together with Michael Zimmermann of ETH Zürich in Switzerland will test an approach to enhance iron absorption from food in children in Kenya by providing them with live food-grade bacteria to release phytate-bound iron from popular foods such as cereal flour. Phytates bind strongly to iron and inhibit its absorption. Their previous work identified different bacterial strains containing phytases that could grow in milk, degrade phytates, and release nutritionally-relevant levels of free iron in vitro. They will test different strain combinations for their phytate-degrading activity under different conditions, such as in specific foods and in an environment mimicking the upper GI tract, and select the best one for producing a fermented food product. This will then be tested to assess its effect on iron absorption in a cohort of 22 iron-deficient Kenyan school-aged children.
Aida Badiane of the Universite Cheikh Anta Diop de Dakar in Senegal will use shotgun metagenomic next generation sequencing (mNGS) to identify the pathogens causing nosocomial infections in Senegal to improve diagnosis and treatment. Nosocomial infections (i.e., hospital-acquired) cause substantial mortality in Senegal but remain poorly understood. To create a more complete profile of the causative pathogens, they will apply shotgun mNGS to different types of clinical samples from 61 patients at LeDantec hospital to identify and quantify the pathogens. They will also identify the most suitable sample types for diagnosing the most common pathogens. The sequencing data will be analyzed and shared with clinicians, stakeholders and the global research community, and will help in the development of suitable diagnostic assays. This project will help implement sequencing technologies into the national healthcare system.
Innocent Semali of Hubert Kairuki Memorial University in Tanzania will design a more effective strategy for eliminating trachoma in the nomadic Maasai communities in Tanzania. Trachoma is a bacterial disease and a leading cause of blindness. Globally, there are around 84 million sufferers, mostly in sub-Saharan Africa. In Tanzania, the standard control strategy, which involves mass drug administration of azithromycin, eliminated trachoma from most districts. However, the strategy has largely failed in nomadic populations for unclear reasons. To identify those reasons, they will work towards building a partnership with a Maasai community and relevant stakeholders and use interviews and surveys to document their perceptions and behaviors around the standard trachoma interventions. This information will be used to understand the failure of the previous interventions and, together with the communities and stakeholders, to develop new strategies for testing in two villages using a mixed methods approach.
Haroon Hafeez of Shaukat Khanum Memorial Cancer Hospital and Research Centre in Pakistan will develop a framework for the re-use of large clinical and administrative datasets to enable comparative analysis of COVID-19 vaccine safety and effectiveness in Pakistan and in Brazil, with colleagues at Fiocruz there, to improve pandemic responses and promote data-driven evidence generation in the Global South. Monitoring vaccinations across different settings is crucial for containing pandemics. However, comparative analysis of large health datasets in different scenarios is challenging due to concerns around safety and reproducibility and the loss of the context in which the data was collected, which can affect research results. They will adapt data science standards and tools to different local health system scenarios and run individual and joint vaccine effectiveness analyses for the two countries to assess compatibility and reproducibility of the findings. They will also build a public data visualization dashboard for health managers and policymakers to monitor the pandemic, particularly in vulnerable populations.
Kanny Diallo of the Centre Suisse de Recherches Scientifiques en Côte d'Ivoire will use metagenomic sequencing to investigate the etiological diversity of meningitis in Mali, Guinea, and Côte d’Ivoire, three countries in the so-called African meningitis belt, to improve diagnosis and public health responses. The African meningitis belt stretches from Senegal to Ethiopia and has the highest burden of meningitis worldwide. Meningitis can be caused by many different types of pathogens (bacteria, virus, fungi, and parasites), which vary between countries. Although 35 meningitis-causing pathogens are detectable by current PCR-based techniques, over 80% of cases remain undiagnosed suggesting that other pathogens are involved. They will perform a prospective study by collecting 65 cerebrospinal fluid samples from children under 5 years old with suspected meningitis and apply an unbiased metagenomic approach to identify both known and unknown pathogens. Their results will also help inform the design of new vaccines.