Awards

Daniel Kwaro of CREATES in Kenya will develop an early warning system for malaria outbreaks, floods, and heatwaves in Siaya County in Kenya, co-designing it with the local community. They will incorporate health and demographic surveillance system data, including a specific focus on maternal health indicators and birth outcomes, as well as data from automated weather stations, wearable devices, and mosquito traps. Through secondary data analyses, they will assess the probability and consequences of climate-related hazards, including identifying vulnerable communities, high-risk geographical areas, and occurrence patterns of climate-sensitive diseases. They will actively involve Siaya County residents, healthcare providers, and relevant local authorities in co-designing the early warning system paired with multiple mechanisms for communication to ensure the system is accessible and effective in responding to local needs.

Sona Shah of Neopenda, PBC in Kenya will integrate ChatGPT-4 as a virtual assistant for the wearable, vital sign monitor neoGuard, supporting healthcare providers in effectively monitoring and managing neonatal health. They will train ChatGPT-4 to help providers identify and address challenges with the neoGuard monitor, such as poor sensor placement on the patient, and to give providers appropriate recommendations based on vital sign data together with the clinical information they gathered. This real-time clinical decision support would be particularly beneficial in remote and understaffed healthcare facilities. For model training, they will use a dataset of newborns admitted to a hospital in Kenya, including vital signs, clinical histories, and treatment outcomes, as well as insights from unstructured clinical notes extracted using natural language processing. They will evaluate use of neoGuard with ChatGPT-4 for reliability, accuracy, and user-friendliness, and compare neonatal patient outcomes before and after ChatGPT-4 integration with the monitor.

Emery Metelo of the Institut National pour la Recherche Biomedicale in the Democratic Republic of the Congo will test implementation by women and youth community members of a mosquito vector control program to reduce the burden of disease caused by Aedes-borne arboviruses. The program will be guided by local health authorities and the network of community health workers. It will be implemented over 15 months in two areas of the city of Kinshasa, and it will consist of community education and training of participants, followed by mosquito trapping and clearing of potential habitats for mosquito larvae. They will assess the program's effectiveness by comparing data before and after the intervention, including an arbovirus serosurvey covering dengue, chikungunya, Zika, and yellow fever, and an entomological survey of mosquitos and their larvae. They will also assess changes in relevant behaviors, knowledge, and perceptions due to community participation in the program.

Rory Assandey of La Ruche Health in Côte d'Ivoire will expand an AI-based platform to provide youth with information on mental health and wellbeing, while increasing awareness about and access to relevant services. The platform will build on their voice-compatible chatbot KIKO, which is currently available through WhatsApp and used by marginalized youth for automated anonymous access to health guidance and to make appointments with clinicians. They will further develop their tool to make it usable through additional apps such as the Ministry of Health's DHIS2 and interoperable with additional sources of public health data. They will also improve its capabilities for data analysis and report generation to inform public health decision making. To better understand user needs, they will organize discussion groups with university students and youth in remote villages as well as meetings bringing together youth, mental health clinicians, and health ministry representatives.

Cleo Conacher of Stellenbosch University in South Africa will develop a surveillance and risk prediction system for the presence of Candida pathogens in river water sources used by informal settlements in South Africa. This system will account for climate change-influenced environmental factors that can affect the prevalence, distribution, and behavior of these fungal pathogens. They will survey community members for data on river water exposure. They will also design a sampling device to collect data on river water properties as well as microbiological samples for data on Candida pathogen prevalence and gene expression. They will combine this data to enhance an existing framework for microbial risk assessment and to develop an AI-based model that predicts fungal infection risk from environmental trends. They will communicate the risk estimates and overall conclusions to community members through meetings and a project website, with materials translated into relevant local languages.

Ousmane Sy of the Universite Cheikh Anta Diop de Dakar in Senegal will develop mathematical and AI-based models to predict the impact of climate change on malaria morbidity, mortality, and transmission by the Anopheles gambiae species complex in Senegal. They will use epidemiological, environmental, and entomological data from the last 20 years in northern and central Senegal to predict the future of the disease in these areas. The models will incorporate the effects of interventions for malaria control being used in Senegal, including long-lasting insecticidal nets, indoor residual spraying with insecticides, and seasonal chemoprevention. The project's interdisciplinary approach will support the National Malaria Control Program, while building and strengthening collaborations for malaria modeling.

Blaise Bougnom of the Centre for Research in Infectious Diseases in Cameroon will develop models to predict the impact of climate change on the spread of clinically relevant, drug-resistant bacteria in maternity wards in Cameroon and Burkina Faso to enhance public health preparedness. The models will integrate climate data with data from hospitals across different climate zones in the two countries, enabled by a partnership with the University of Ouagadougou in Burkina Faso. They will collect and sequence bacterial samples from these hospitals, while also performing environmental monitoring in them. They will then collect historical and projected climate data for the relevant areas of the countries, using it to develop models that predict transmission of drug-resistant bacterial strains. Using the models, they will design targeted interventions, such as enhanced hygiene protocols, pilot testing them in hospitals to improve maternal and neonatal health outcomes.

Diana Karanja of COHESU in Kenya will develop a community health information system to reduce the burden of schistosomiasis in Kenya. They will implement the project in persistent hotspot areas of schistosomiasis near Lake Victoria. They will perform surveys to assess women's decision-making power in the household and how this correlates with schistosomiasis health outcomes for them and their families. The results will be used to inform communication strategies that improve these health outcomes. They will also train community healthcare providers to collect information on use of local water sources as a risk factor for water-borne transmission of the schistosomiasis parasite. This information will be integrated into existing predictive models of risk that will be used to develop an app. The app will provide simple messages and alerts, including information tailored based on the household surveys as well as seasonal updates on schistosomiasis risk.

Devotha Nyambo of the Nelson Mandela African Institute of Science and Technology in Tanzania will develop AI-based predictive models as an early warning system for vector-borne diseases, integrating community-based information gathering and vector control and focusing on malaria and dengue in areas of Tanzania particularly vulnerable to climate change. They will develop the models using topographic data from satellite images, historical climate and weather data, and vector-borne disease data sets. They will train women and youth to identify and eliminate mosquito breeding sites and larvae, and they will create user-friendly interfaces for this observational data to be reported and integrated into the predictive models. The early warning system, enhanced by direct engagement and education of community members, will guide effective strategies to mitigate the impact of vector-borne diseases.

Francis Kombe of EthiXPERT NPC in South Africa will develop an AI-based platform to support African research ethics committees and clinical trial decision-making. It will build on their cloud-based, online review system RHInnO Ethics. This system is currently used to manage the entire ethics review cycle, including protocol submission and review, approval, and follow-up, with the goal of shortening the review timeline, enhancing review quality, and speeding the discovery of life-saving public health interventions. They will consult with relevant stakeholders to identify elements of ethics review that could benefit from AI. They will then identify the required structured and unstructured data, use this data to train a model based on GPT-4, and integrate the model into their existing review system. They will evaluate the new platform, comparing it with and without the AI element and assessing results from current users, including decision quality and timeliness.

Paul Macharia of the University of Nairobi in Kenya will develop an LLM-based platform to give healthcare providers real-time access to comprehensive, up-to-date, adolescent HIV information for enhanced decision-making and better patient health outcomes. To guide the project, they will establish a community advisory board, including HIV-positive adolescents, healthcare providers, and community leaders. They will interview providers to identify their current sources of this information and their unmet needs. They will then create a dataset relevant for adolescent HIV care, including medical literature, clinical guidelines, and research findings; use it to train an LLM; and develop a natural language interface for healthcare providers to interact with the LLM. They will pilot test the platform in different healthcare settings, collecting data on its impact on provider knowledge and practice.

David Kamau of Mary Help of the Sick Mission Hospital in Kenya will integrate ChatGPT and a medical Large Language Model with the existing health management information systems in Kiambu County, Kenya, to provide clinical decision support for noncommunicable diseases. This integration will support healthcare providers in diagnosing diseases accurately and quickly, reducing misdiagnoses and improving patient outcomes, and it will support optimizing treatment plans, reducing unnecessary procedures and healthcare costs. They will evaluate the performance of the integrated AI tools, assess the usability of the system through surveys, and capture demographic data for patients receiving AI-assisted care. They will also provide training for healthcare professionals on effectively using AI tools to enhance patient care.

Steven Wanyee of IntelliSOFT Consulting Limited in Kenya will develop an AI-based framework for analysis of cervical cancer registry data to identify epidemiological trends and improve surveillance and prevention efforts. The analysis will incorporate variables such as demographic factors, geographic locations, screening history, HPV vaccination rates, and treatment outcomes. They will use natural language processing to extract and analyze unstructured data. Machine learning algorithms will be used to identify patterns and trends in cervical cancer incidence rates, stage at diagnosis, treatment outcomes, and survival rates. They will develop predictive models to forecast cervical cancer burdens, estimate the potential impact of interventions for prevention, and guide resource allocation and targeted prevention strategies. They will also create user-friendly interfaces and visualizations to enable policymakers, public health professionals, and researchers to easily interpret the modeled data and use it effectively.

David Kamugundu of eFiche Limited in Rwanda will develop an AI-based platform to support nurses in Rwanda in accurate and efficient diagnosis and patient treatment. The platform will be integrated into the already-operational, web-based electronic medical records system eFiche. It will serve as a virtual assistant for nurses, including providing diagnostic support, suggesting treatment plans based on the latest medical guidelines and assessing adherence, and identifying potential adverse events. They will train a Large Language Model with historical health data to help predict diagnostic outcomes, laboratory orders, and subsequent procedures and prescriptions, and they will develop a chatbot to provide information and recommendations to nurses in a conversational manner. They will evaluate the platform based on the accuracy of the model, satisfaction of users, and the impact on healthcare delivery, including diagnostic accuracy, treatment efficacy, and adherence to health guidelines.

Obed Brew of Kwame Nkrumah University of Science and Technology in Ghana will explore applying a combination of AI-based analytical approaches to clinical data for early detection of preeclampsia in pregnancy to reduce maternal and neonatal morbidity and mortality. They will collect a diverse set of data from pregnant women, including physiological data from wearable devices, electronic health records, clinical notes, and fetal nucleic acids from non-invasive prenatal testing. They will apply a variety of AI tools to detect patterns across the data, such as associations between fetal gene signatures and maternal physiological markers. While evaluating the performance of the AI models in detecting preeclampsia, they will develop training programs for healthcare professionals in the use of AI tools in clinical settings.

Mihaja Raberahona of Equipe de Recherche Clinique en Maladies Infectieuses in Madagascar will develop an AI-based triage tool to identify patients likely to have pulmonary TB based on combining physiological data from wearable devices, cough acoustics, and anthropometric data. They will create an AI model that can find patterns in the combined data to help staff with minimal medical training quickly prioritize patients needing confirmatory testing. From a cohort of patients with symptoms suggestive of TB from primary healthcare centers in Madagascar, they will collect physiological and anthropometric data and perform a digital chest X-ray, cough sound analysis, and a GeneXpert molecular assay for the presence of Mycobacterium tuberculosis. They will evaluate performance of the AI-based triage tool primarily as compared to the microbiological assay, but also to other current screening methods, including chest X-rays analyzed by clinicians with or without AI support.

Jesse Gitaka of Mount Kenya University in Kenya will develop the GPT-4 AI model to support healthcare providers with up-to-date medical information for improved clinical decision-making and neonatal care. They will identify knowledge gaps among providers, using this information to guide training for a cohort on using GPT-4 for clinical support. They will then evaluate their use of GPT-4 for its impact on clinical decisions and neonatal outcomes. A subset of the healthcare provider cohort will be engaged to identify the barriers, risks, and opportunities associated with use of the AI tool. Results from both these evaluations will be used to develop a scalable framework for the deployment of AI in similar healthcare contexts to reduce neonatal morbidity and mortality, especially where there is a shortage of medical personnel.

Fred Kaggwa of Mbarara University of Science and Technology in Uganda will develop an LLM for answering questions related to prenatal and postpartum care in Uganda in three languages: Swahili, Runyankore-Rukiga, and Luganda. This will also serve to create a scalable, open-source pipeline for developing AI models incorporating underrepresented languages more broadly. They will identify the most suitable LLM and train it with existing databases and with new, high-quality medical text, including that extracted from textbooks and patient education materials as well as new question-and-answer pairs written by local clinicians. They will iteratively evaluate and improve the LLM, working directly with community health workers and expectant mothers to ensure the model’s responses are safe, accurate, relevant, and accessible.

Mawuli Dzodzomenyo of the University of Ghana School of Public Health in Ghana will use a human-centered design approach to develop an AI-based early warning system to support communities in Ghana prepare for climate change-related risks to maternal and newborn health. They will develop a machine learning model that integrates climate and public health data to identify patterns relevant for maternal and newborn health. They will then use a human-centered design approach to engage community healthcare workers and women's groups in workshops, choosing communities in two distinct ecological zones in Ghana to encompass a range of climate change issues. The initial modeling results will be presented at the workshops, with participants providing feedback to shape the model into a prototype early warning system most suitable to their needs and concerns.

Dennis Maorwe of DPE Company Limited in Kenya will develop a Large Language Model (LLM) to support the planning and execution of behavior change interventions to improve health outcomes for Kenyans living with SCD. Guided by insights from SCD patients and medical specialists, they will train an LLM with SCD management practices and additional open data relevant to designing interventions for social behavior change for SCD. The LLM will be used to generate tailored health messages to support SCD patients through the different disease management stages and to empower them to take an active role in their health. This will help reduce the stigma around the disease and improve their quality of life.

Brian Turyabagye of MamaOpe Medicals in Uganda will develop an AI-based platform integrated with the MamaOpe screening tool to enhance the diagnosis and management of respiratory illnesses. MamaOpe is a pneumonia screening tool based on vital signs, including respiratory rate and lung sounds, with results presented via a mobile app into which healthcare workers can enter patient symptoms and store patient histories. They will train a Large Language Model with the MamaOpe database to help guide differential diagnosis by highlighting critical indicators and suggesting supplementary tests for more accurate diagnosis. In settings with limited direct access to specialists, the platform can facilitate remote consultations. They will perform an efficacy study to evaluate how well the platform correctly predicts clinical outcomes.

Rose Nakasi of Makerere University in Uganda will develop an AI-based platform to support diagnosis and management of cervical cancer in Uganda. They will collaborate with the Uganda Cancer Institute to develop a set of AI tools for automated diagnosis of cervical cancer based on microscopy of patient samples and for automated generation of the associated pathology reports. This will include categorizing pathologies, enabling identification of trends over time. The AI tools will be integrated into a web-based platform along with the capacity for Visual Question Answering to support interpretation based on medical images and diagnosis in remote areas of the country with limited access to pathologists. They will evaluate the accuracy of the cervical cancer diagnoses and the quality of reports generated through the platform as compared to those generated by expert pathologists.

Kevin Korir of Visortech Solutions in Kenya in partnership with Yemaya Health Advisory will develop an LLM to map the process of health policy creation and approval, serving as a tool for quicker translation of new evidence into policies. They will pilot test the tool by using it to develop a policy framework for strengthening national HIV prevention management systems. They will develop the LLM as support for policy simulation and scenario planning to inform the policy approval process, for gap analysis to optimize current policies, and for identification of conflicts across related policies and their implementation. The LLM will facilitate generating written documents useful for policymakers, including briefs for new policies and summaries of proposed changes to existing ones.

Prince Adjei of Kwame Nkrumah University of Science and Technology in Ghana will develop an AI-based tool to support clinicians and patients in managing complications and comorbidities in pregnancies, focusing on pre-gestational and gestational diabetes. They will train a Large Language Model with relevant data and health guidelines from Ghana, including a glossary of medical and nutritional terms in the local language Twi. They will also design a user-friendly interface for interactions in English and Twi, with the patient interface incorporated into WhatsApp. The resulting tool, RAID-MaP-Gh, will provide practical guidance to clinicians, ranging from specialists to midwives and traditional birth attendants, and guidance to patients that takes into consideration the socioeconomic and cultural contexts most relevant to Ghana. The tool will help close health information gaps associated with gestational diabetes, reducing the number of undiagnosed cases and improving maternal and infant health outcomes.

Polly Okello of Medbook Kenya Limited in Kenya will develop a set of LLMs to support frontline healthcare workers in rural areas and among marginalized populations in Kenya. The LLMs will be based on the clinical LLM Med42 and trained with information representative of the Kenyan healthcare system, including clinical data and guidelines. The LLMs will be used to provide information to clinicians about medical conditions, treatments, and medications; summaries of patient records and other medical documents; personalized treatment plans for patients; and educational materials for patients and healthcare workers. They will deploy the LLMs and evaluate their impact through surveys with healthcare workers and patients and through case studies. They will assess the knowledge and skills of frontline healthcare workers, quality of patient care, efficiency of healthcare delivery, and patient satisfaction, with the goal of making healthcare more efficient, effective, and accessible in Kenya.

Mukhlid Yousif of Wits Health Consortium (Pty) Limited in South Africa will sequence SARS-CoV-2 in sewage samples collected periodically from 40 wastewater treatment facilities across South Africa for the early detection of potentially dangerous variants to inform public health policies. Genome sequencing using sewage samples can monitor the molecular epidemiology and diversity of circulating SARS-CoV-2 variants, and also identify new variants even before they can be detected in the clinic. They will collect a total of 528 wastewater samples over a twelve-month period and process them for sequencing to identify novel mutations or mutations that are unique to variants-of-concern, especially those not yet reported in Africa. They will also compare these data with sequences of SARS-CoV-2 variants from local COVID-19 patients to support interpretation of wastewater sequencing results. Results will be immediately published online and communicated to provincial and national COVID incident management teams.

Francine Ntoumi of the Fondation Congolaise pour la Recherche Medicale in the Republic of Congo will set-up a national SARS-CoV-2 genomic surveillance system by increasing sequencing capacity to monitor viral variants-of-concern and determine the impact of vaccines on disease transmission to inform public health decisions. They will perform a cohort study by collecting oropharyngeal samples from patients at COVID-19 testing centers in the two largest cities, which account for 80% of the country’s new infections, and sequence around 60 SARS-CoV-2-positive samples per month to determine the prevalence of variants. These will be combined with existing COVID-19 epidemiological and clinical data to determine the virulence, transmissibility, and symptoms associated with new and existing viral variants-of-concern. They will also analyze blood samples from vaccinated and unvaccinated COVID-19 patients to evaluate their immune responses and combine these with socio-demographic and clinical data to determine vaccine effectiveness.

Richard Njouom from the Centre Pasteur du Cameroun in Cameroon will establish a genomic surveillance network across the country to routinely track circulating SARS CoV-2 strains and identify novel variants for informing health authorities. They will use an existing national network of six COVID-19 molecular testing laboratories for collecting samples. Around 1,200 samples will be screened using a commercial SARS-CoV-2 mutation panel over the course of 12 months to identify existing viral variants-of-concern and variants-of-interest. They will also set up a sequencing platform to sequence the spike protein of the virus to identify new variants, as well as for generating 240 whole SARS CoV-2 genome sequences to monitor viral evolution and identify markers of disease severity or increased transmissibility. Policy briefs will be used to inform the health authorities of circulating variants.

Fitsum Girma Tadesse of the Armauer Hansen Research Institute in Ethiopia will establish the Ethiopian Malaria Genomic Epidemiology Network (EMAGEN) by bringing together key public health, biomedical, and biotechnology institutions in Ethiopia to build malaria molecular surveillance capacity and renew elimination efforts. Eliminating malaria requires an urgent shift to more quantitative methods that can more accurately and rapidly track disease transmission and drug resistance, and better target interventions. They will develop a framework for building capacity and integrating it into the national malaria control and elimination strategy, produce next generation sequencing and bioinformatics protocols that will be used to monitor anti-malaria drug resistance, and train personnel in genomics and bioinformatics. They will also develop a simple, interactive web interface to effectively communicate results to diverse stakeholders.

Sikhulile Moyo of the Botswana Harvard AIDS Institute will expand the country’s genomic surveillance capacity to identify circulating SARS-CoV-2 variants and track their transmission routes and dynamics to inform the public pandemic response. Botswana has one of the highest global burdens of HIV and the associated immunosuppression may prolong SARS CoV-2 replication thereby increasing the probability of viral mutation and emergence of new variants. However, there is insufficient sequencing capacity to track these variants. They will increase national capacity by improving infrastructure, optimizing workflows, and providing training, as well as establishing a sampling framework and surveillance strategy. This will enable the temporal and spatial monitoring of circulating SARS-CoV-2 viral lineages across Botswana. They will also identify any associations between circulating viral variants and HIV infection, and study the risk of infection to specific SARS-CoV-2 variants among vaccinated people.

Atsbeha Gebreegziabxier Weldemariam of the Ethiopian Public Health Institute (EPHI) together with colleagues at the Armauer Hansen Research Institute, both in Ethiopia, will boost the country’s sequencing capacity to establish routine SARS-CoV-2 genomic surveillance and monitor the emergence and impact of new variants to better inform public policy. Over 1,000 SARS-CoV-2 positive samples will be collected across 14 hospitals and laboratories over a period of ten months and subjected to next generation sequencing and bioinformatics analyses to identify any new variants. They will then test the efficacy of existing diagnostic assays for detecting these different variants. New genomic data will be promptly uploaded to public repositories, and a web-based platform will be developed to rapidly communicate research findings to relevant stakeholders, including the Ethiopian Ministry of Health, so that results can be readily translated into public health policy.

Iruka Okeke of the University of Ibadan in Nigeria will use academic sequencing resources to expand the genomic surveillance framework of Nigeria’s Centre for Disease Control for the rapid detection of newly-evolved or imported viral variants to inform national vaccination strategies. Nigeria’s SARS-CoV-2 sequencing needs currently surpass its capacity. To address this, they will repurpose existing academic sequencing and bioinformatics resources and expertise for SARS-CoV-2 genomic surveillance, and share the data with the national and global communities in near real-time. They will also pilot an approach to more efficiently monitor the existence and spread of viral variants and viral breakthrough strains by sequencing SARS-CoV-2 positive samples from around 180 health workers, who have greater exposure and better access to vaccines.

Janine Aucamp of North-West University in South Africa will produce a novel drug screening platform for malaria by building a physiologically-relevant in vitro tissue model of the sinusoidal space of the human liver, which supports the development of liver-stage malaria parasites (sporozoites). Artemisinin-based combination therapies are first-line treatments for malaria but their efficacy suffers from the development of resistance, thus alternative approaches are needed. One approach is to block parasite development in the liver, which can prevent the establishment and symptomatic onset of malaria. They will build three different three-dimensional micro-bioreactor liver models and evaluate how well they can be infected by Plasmodium falciparum sporozoites compared to two-dimensional cultures. They will then test the value of the most promising model for identifying anti-malarial drugs first using two approved drugs and subsequently by screening novel drugs.

Grace Mugumbate of Chinhoyi University of Technology in Zimbabwe will develop new anti-malarial drugs by using a chemogenomics approach for ligand-based and structure-based virtual screening to identify compounds that selectively bind to heat shock proteins of the malaria parasite, Plasmodium falciparum. P. falciparum heat shock proteins are essential for parasite growth and survival, and represent a valuable new target for developing safe and effective anti-malarials. They will use existing chemical and genomic data to produce three-dimensional structures of several heat shock proteins for performing the virtual screens. Machine learning approaches will be used to identify binding ligands and inhibitors that will be validated using enzymatic assays in vitro. Promising hits will then be subjected to structure-based optimization to identify active compounds as leads for further development.

Erick Strauss of Stellenbosch University in South Africa will develop a small molecule inhibitor of an enzyme that helps pathogenic bacteria evade the host immune system and potentially become resistant to antibiotics as a novel treatment for methicillin-resistant S. aureus (MRSA), which is a major public health concern. They discovered a bacterial enzyme, MerA, that neutralizes an anti-microbial compound secreted by immune cells. This prolongs the survival of the bacteria in the host, giving them time to develop mutations that could render them less susceptible to antibiotics. They have identified two different chemical scaffolds that occupy the active site of MerA and will employ a new inhibitor discovery strategy that combines parallel synthesis with an X-ray structure-based binding screen to identify promising MerA inhibitor leads. These leads will be evaluated by in vitro and ex vivo assays for further development.

Lyn-Marie Birkholtz of the University of Pretoria in South Africa will identify gametocytocidal
compounds that specifically prevent human-to-mosquito transmission of gametocytes and block gamete and oocyst formation in mosquitoes as a complementary strategy to help eliminate malaria. Traditionally, anti-malarial compounds have been developed to target asexual blood-stage parasites. However, also blocking parasite transmission is critical for eradication. They developed a platform that can screen multiple sexual stages of the parasite and recently used it to identify ten hit compounds from the Medicines for Malaria Venture (MMV) Pandemic Response Box (PRB), most of which have not previously been tested against malaria-causing parasites. They will validate those hits, optimize them, and analyze their structure-activity-relationship (SAR) and their potential mode of action to identify at least one chemotype as an early lead candidate for further development.

Laurent Dembele of the Université des Sciences, des Techniques et des Technologies de Bamako in Mali will use their cell-based ex vivo phenotypic drug assay to identify approved anti-malarial drugs that are effective also against the neglected malaria-causing pathogen Plasmodium malariae, which has become widespread in sub-Saharan Africa. To eliminate malaria, treatments should be effective for all circulating malaria pathogens. However, current artemisinin-based combination therapies (ACTs) are largely designed to target the historically more prevalent P. falciparum species. They will recruit around 400 patients with uncomplicated malaria in Faladje to determine the P. malariae malaria burden. They will also evaluate the ability of a panel of anti-malarial compounds to destroy cultured P. malariae together with P. falciparum to help guide treatment strategies.

Fortunate Mokoena of North West University in South Africa will couple molecular docking approaches with in vitro and in vivo validation to identify novel inhibitors of Trypanosoma brucei and Plasmodium falciparum, the causative agents of the lethal diseases, African trypanosomiasis and malaria, respectively. Current drugs targeting these pathogens have limited efficacy due to the development of resistance and can cause severe side effects. They will identify a new group of drugs that specifically target parasitic molecular chaperone proteins, specifically heat shock protein 90 (Hsp90), which is an ATPase that helps correctly fold newly synthesized proteins. They will computationally model the structures of Hsp90 from both T. brucei and P. falciparum and prepare a three-dimensional database of inhibitors for virtual screening. The top 30 candidate inhibitors that selectively bind parasitic Hsp90 will be subjected to geometry optimization and induced fit molecular docking, followed by evaluation of their parasite killing activity in vivo.

Elizabeth Kigondu of the Kenya Medical Research Institute will identify natural products that block the resistance mechanism developed by tuberculosis-causing bacteria against existing anti-mycobacterial drugs to help more effectively treat tuberculosis. Tuberculosis (TB) is a highly prevalent and severe disease that has been exacerbated by the emergence of multi-drug resistant TB for which only limited treatments are available. Efflux pumps play a critical role in mycobacterial resistance to two drugs, spectinomycin and rifampicin. They will identify natural products and their derivatives that block these efflux pumps by first searching databases for analogs to published efflux pump inhibitors, and then performing virtual docking experiments to identify those that bind. These will then be tested in drug combinations with spectinomycin and rifampicin for synergistic cytotoxicity and anti-mycobacterial activity.

Gabriel Mashabela of the South African Medical Research Council will develop novel tuberculosis drugs derived from South African medicinal plants by utilizing CRISPR genome editing technology to produce Mycobacterium deficient in essential metabolic enzymes that can be used to screen natural products. Although the majority of approved drugs are of natural origin, most drug-screening approaches use synthetic libraries, which lack diversity. However, natural products contain very low concentrations of bioactive compounds making them difficult to use in traditional drug screens. To address this, they will use CRISPR to reduce the levels of a selection of essential metabolic enzymes, without removing them completely, so that lower levels of bioactive compounds are needed. They will prepare extracts from 100 plants with anti-mycobacterial activity, and perform whole cell screening to identify those with killing activity against the different Mycobacterium mutants. These can then be further optimized for drug development.

Carol Hiela of the University of Cape Town in South Africa will determine whether a new wireless vital signs monitor (ANNETM) is able to collect reliable and accurate measurements of vitals, in a way that is convenient, comfortable and efficient for neonates admitted in ICU, comparable to the hard-wired, standard-of-care monitoring systems.

Yogan Pillai of Clinton Health Access in South Africa will build on work done to address the gaps in knowledge that many pregnant women have in both understanding their pregnancy and the development of the fetus as well as how to navigate the health system.

Assumpta Nantume of Neopenda in Uganda examines the clinical impact and cost-effectiveness of a wearable vital signs monitoring system, neoGuard, for critically ill newborns in Kenya where the newborn mortality rate is one of the highest in the world.

Kenneth Ssembambulidde of Makerere University College of Health Sciences in Uganda will seek to test the hypothesis of whether abnormal (high or low) maternal and newborn cholesterol is associated with a risk for neonatal sepsis.

Christine Musyimi of Africa Mental Health Foundation in Kenya will aim at reducing Intimate Partner Violence (IPV) among pregnant women through anger management strategies, delivered via mobile phones to potential IPV perpetrators (PIP) in rural Kenya, where resources of mental health specialists are limited.

Evrard Nahimana of Partners in Health in Rwanda will test the delivery of a peer-support model using Expert Mothers for the provision of nurturing care to small and sick newborns in 2 district hospitals in Rwanda through a quasi-experimental pre-post design. Efforts to reduce neonatal mortality have included the expansion of care for small and sick newborns, which has the potential to save 1.9 million newborn lives. However, less attention has been paid to increases in developmental disability that occur as children survive the neonatal period with significant morbidity.

Jesse Gitaka of Mount Kenya university in Kenya will employ a strategy that will transfer cutting edge CRISPR-based isothermal nucleic acid analysis technique for diagnosis of CuSTIs (curable sexually transmitted infections) onto fibre mats with smartphone readout enabling on-site pathogen analysis in resource-limited settings enabling prompt treatment alleviating prematurity, stillbirths and neonatal deaths.

Sally Griffin of ICRH Mozambique in Mozambique aims to increase the involvement of men in maternal healthcare, by testing a gender- and culture-sensitive intervention to strengthen male involvement in antenatal care in Marracuene district in southern Mozambique.

Ali Sie of the Centre de Recherche en Sante de Nouna in Burkina Faso aims to demonstrate the feasibility of generating (baseline) data on the frequency of congenital anomalies in rural Africa, including on the variability of head circumference measurements, and for the development of first-line laboratory testing for infectious agents related to congenital anomalies.

Annettee Nakimuli of Makerere University College of Health Sciences in Uganda aims at using new genomic approaches to identify pathogens that cause fever (sepsis) among pregnant women. Sepsis is one of the major causes of maternal and neonatal morbidity and mortality globally, responsible for over 15% of maternal and neonatal deaths.

Angela Dramowski of Stellenbosch University in South Africa will determine the most effective antibiotics to use and when to use them for treating bloodstream infections in neonates to reduce mortality rates. In Sub-Saharan Africa, a quarter of a million deaths in children under five are caused by bacterial infections during the first 28 days of life. The causal bacteria and their susceptibility to antibiotics change over time and across different regions, thus standard treatment guidelines are likely outdated. They will assemble infection datasets collected between 2016 and 2018 from eight Sub-Saharan African neonatal units that include the bacterial types and antibiotic susceptibility, the treatment given, and the outcome. They will then develop a probabilistic decision-tree model to estimate the impact of using alternative antibiotics on neonatal mortality compared to the standard treatments, as well as to determine the best timing for recommending different treatment strategies based on age at infection onset.

Geoffrey Arunga of BroadReach in Kenya will develop a digital assessment tool to identify women with the highest risk of maternal morbidity and adverse pregnancy outcomes, and their causes, and to inform clinicians and health policies to improve maternal health and survival. They will apply advanced statistical analyses and machine learning techniques to clinical, social, and economic data from an existing longitudinal study of pregnant women in West Africa to identify the data that can best predict risk. This will be used to derive a minimum set of questions that can be incorporated into a digital tool for health workers to assess a woman’s risk at any given timepoint during pregnancy. The tool will be pilot tested for feasibility and predictive performance in rural and urban-based health facilities in Ghana.

Joseph Akuze Waiswa of Makerere University College of Health Sciences, School of Public Health in Uganda will leverage their dataset on pregnancy outcomes collected by household survey in four African sites to improve the quality of data on stillbirth and neonatal death rates to optimize interventions and investments. Around half the global burden of stillbirths and neonatal deaths occurs in Africa. However, the total numbers are derived from household surveys, and the quality of the data is relatively poor, with missing events and misclassifications of the cause of death. To address this, they recently performed a large-scale multi-site study in Africa to collect relevant household survey data on over 68,000 pregnancies using an Android-based platform. They will further analyze this dataset to identify factors that influence the quality of the data collected, such as the type and structure of the questions, to develop more effective survey questions and inform training for interviewers.

Naeemah Abrahams of the South African Medical Research Council will study the impact of gender-based violence on the health of mothers and children in South Africa to better inform prevention and health strategies. Gender-based violence affects one in three women globally, and rates are high in Africa, particularly for women of reproductive age. However, the effects on their health remain largely unknown, in part because of the lack of follow-up studies. They will analyze data from an ongoing longitudinal study, covering over 36 months, of over 1,600 women in South Africa, and they will perform statistical analyses to identify associations between the types of violence that they suffered, such as from intimate partners or during pregnancy, and the effects on HIV acquisition, pregnancy outcomes and their mental health. They will also identify factors that can influence these outcomes, such as physical injuries or lack of social support.

Adeladza Kofi Amegah of the University of Cape Coast in Ghana will investigate how diet, the environment and low birth weight lead to child undernutrition in socially-disadvantaged communities in West Africa. Studies have shown strong associations between socio-economic status and child undernutrition, but they have not identified the actual causes, which is critical for developing effective interventions. They will evaluate potentially causative dietary factors such as meal diversity and frequency, and environmental factors including water, sanitation, and hygiene practices, along with birth weight. They will analyze four demographic and health survey (DHS) datasets from the last twenty years that include birth weight, infant height and weight, wealth status, and education attainment. They will use various modeling approaches and statistical analyses to identify the associations between the different parameters, and a sequential causal mediation analysis to establish the role of specific factors, and combinations of those factors, on undernutrition.

Said Mohammed Ali from the Ministry of Health in Tanzania will use machine learning to develop a model for predicting gestational age based on routinely collected data and use it to better define when a birth should be classified as premature in Africa and thereby at higher risk of neonatal death. Premature births in Africa are currently defined as those occurring before 37 weeks. However, data suggests that births occurring just after this cut-off are also at higher risk of neonatal death, suggesting that the definition needs reevaluating. They will do this by developing an artificial neural network-based model using existing data, such as the date of the last menstrual period and pregnancy history, from 5,000 pregnancies with an ultrasound-verified gestational age to identify accurate predictors of gestational age. This will then be used to estimate gestational age on an additional 20,000 pregnancies, and further to identify the age cut-off that best predicts neonatal mortality, using logistic regression models.

Christopher Seebregts of Jembi Health Systems NPC in South Africa will use a data science approach to improve maternal, newborn, and child health by developing algorithms that integrate diverse personal and clinical data taken from disparate sources to make them more informative. To test their approach, they will apply it to existing data taken in the Tshwane health district in South Africa for a program looking to prevent mother-to-child transmission of HIV. These data include quantitative diagnostic readouts on HIV status for mother and child, therapy initiation reports, household and socio-economic data, and district health information, all produced by different groups using different formats. They will use data science methodology and algorithms to integrate all the data to produce longitudinal and cohort data and apply machine learning techniques to identify predictors of failure to prevent mother-to-child HIV transmission, and of mother or infant mortality.

Sikolia Wanyonyi of Aga Khan University in Kenya will analyze datasets on maternal mortality during hospital deliveries to determine the causes and to develop prediction models to help identify effective interventions in specific settings. Maternal mortality rates in Kenya are only slowly reducing, despite the increase in hospital deliveries, which may be due to a combination of different factors such as the quality of care and clinical characteristics. They have assembled available data from the Kenyan Ministry of Health for different counties over the last five years and will apply a hierarchical Bayesian model to identify the trends and their causes. They will also fit so-called generalizable estimating equations to the data to determine whether the risk of maternal mortality can be predicted from combinations of specific types of data, such as socio-demographic, which could be used to identify high-risk patients for more timely treatments.

Anthony Ngugi of Aga Khan University in Kenya will use a modeling approach to determine the optimal allocation of limited child nutrition budgets that will most effectively reduce mortality and morbidities, like stunting and anemia, caused by malnutrition. They will use the Optima Nutrition modeling tool, which combines cost functions with an epidemiological model, to make predictions about the cost-efficacy of different funding allocations, for example on food fortification or education. They will focus on the 24 counties in Kenya with the highest burdens of malnutrition and assemble local academics and collaborators at the National Ministry of Health to help collect and harmonize data for the modelling analysis, including existing nutrition-related datasets and health budgets. They will run and validate the model, and then test different optimization algorithms to identify the most effective funding allocations.

Lucas Malla of the Kemri-Wellcome Trust Research Program in Kenya will apply a novel statistical method to determine how the timing and rates of gestational weight gain during pregnancy affect maternal and child health in Africa to identify risk factors and those at highest risk. Excessive or insufficient gestational weight gain can predict adverse maternal and child health outcomes such as gestational diabetes, preterm birth, and infant mortality. They will use six existing longitudinal datasets with over 40,000 data-points on gestational weight gain and apply a univariate and multivariate SITAR (super-imposition by translation and rotation) growth curve analysis approach to model the gestational weight trajectories. They will then use regression techniques to identify specific parameters (size, timing, and velocity of the weight gain) that can predict specific health outcomes.

To apply Information and Communication Technology (ICT) to develop and test a pollution monitoring and management system which will connect community- based Pollution Control Officers and local government to effectively drive change in municipal responses to incidents of water pollution. The project will focus on rapid identification of sources of faecal and solid waste pollution sources that affect water quality and subsequently human health. It will involve tracking pollution due to sewage leaks, pump malfunctions or other environmental problems as well as solid waste challenges linked to water quality and sanitation.

To utilise a mobile enabled Geographic Information System (GIS) technology and an embedded point-based reward system/application to track faecal sludge management (FSM) patterns among waste entrepreneurs and subsequently inform issuing of incentives and subsidies to them. The incentives and subsidies will be issued based on the volume of faecal sludge emptied. The reward system will comprise integration of a point-based incentive system and a micro-loan as well as a non- cash incentive that facilitates day to day business running requirements needed for safe pit emptying.

To develop a software platform (accessible through text messaging and a mobile application) for women in informal settlements to report and receive real time information in cases where accessing public latrines and water sources may be a risk to their safety. The information will then be linked to service providers and community leaders to increase efficiency of maintenance of facilities and ensure they are safe for women and children to use.

To use digital water Automatic Teller Machine (ATM) dispensers and electronically rechargeable water ATM cards to improve access to sustainable and affordable clean water in poor peri-urban cities in DRC. This innovation will help reduce queuing times and gender-based violence against women and children who are normally obliged to walk at specific times in the early mornings or late evenings to access water points. The innovation will also improve the financial management of revenue from water for water providers to support sustainable operation and maintenance of water systems and expenditure by customers, through facilitating the payment of water upfront, allowing measurement of water sold and providing a digital audit trail to mitigate fraud.

Development of HydroIQ, a Global Positioning System (GPS) and internet-enabled device plugged into existing water supply systems and along water distribution networks to automatically monitor water use, water quality and water leakages using sensors and send data to an online platform in real-time, thereby turning traditional water systems into smart water grids to improve water use and billing efficiency, sanitation and hygiene in urban areas.

To establish a One Stop Digital Sanitation Solution Centre, a dashboard/digital platform that harmonizes the Ministry of Health and Urban governments’ WASH indicators and links government, service providers, entrepreneurs and end users to support and improve multi-sectoral decision making, planning, and provision of safely managed sanitation services to peri-urban settlements of Kampala city in Uganda.

To develop an ICT (Information & Communication Technology) platform and integrated mobile technologies to implement efficient digital customer support strategy and streamline waste collection, for Fresh Life's fast-growing network of toilets. End users will be able to utilize the platform to report maintenance and give feedback on sanitation products or services, among other applications. The platform will allow Fresh Life Initiative to improve their delivery of services through data collection for monitoring quality and standards of services by tracking efficiency and maintenance of waste collection and other sanitation products or services that Fresh Life Initiative provides to informal settlements in Nairobi, Kenya.

To capture data on AMR indicators- antibiotic prescriptions, drug sensitivities and resistance patterns from the private sector (human and animal clinics and laboratories) to generate evidence that will contribute to improving antibiotic prescription practices, epidemiological surveillance and effective control of AMR in Uganda. 

Timely, holistic and accurate information on antibiotic resistance is important for guiding public health actions and treatment decisions. Ng'eno's research explores application of ecological niche models in predicting spatial distribution of antibiotic resistance carriage risk, using antibiotic-use and environmental data.

The project is using One Health approach in investigating the emergence and spread of Methicillin Associated Staphylococcus aureus (MRSA) and other antibiotic resistant bacteria at the human-animal interface in Kajiado and Kiambu Counties in Kenya. The study is a continuation of Kagira's work on surveillance of antimicrobial resistance (AMR) in livestock in Kenya. Preliminary work by Kagira and a team of researchers have shown high prevalence of AMR in bacteria isolated from ruminants having mastitis. Consumption of milk/milk products contaminated with resistant bacteria as well as close interaction between livestock and people is a critical entry point of these microorganisms into the food chain. Indeed, recent studies in Kenya have shown high prevalence (>84%) of MRSA at human hospital settings leading to increased morbidity, mortality, and financial constraints. The current project is geared towards using modern molecular tests such as genetic typing to provide the much needed evidence that livestock associated-MRSA and other resistant bacteria are able to breach the livestock-human barrier and cause severe disease in man. Results of the project will be used in informing One Health policies on surveillance and management of AMR where veterinary and medical authorities work together in managing the menace.

Antibiotic resistance (ABR) is a public health threat and largely attributed to heavy selective pressures resulting from widespread of antibiotic use coupled with the exchange of genetic resistance genes between microorganisms through plasmids. These plasmids can be specific to a type of host(s) limiting their spread or may be broad range with capabilities of spreading across species. Deciphering the complex interaction exits between humans, animals and the environment that supports the spread and evolution of antibiotic resistance can provide clues to stopping the spread and curing antibiotic resistance. The overall aim of the project is to investigate the evolution, composition, overlap and relative importance of antibiotic resistance conferring plasmids and their bacterial host ranges in humans, animals and the environment. The study employs portable next-generation sequencing technologies and machine learning to understand the role of plasmids in the evolution and spread of resistance. The long-term goal is to aid the development of strategies that can slow the spread of antibiotic resistance by gaining insight into the co-evolutionary processes that allow bacteria to improve the persistence of newly acquired MDR plasmids. This fundamental knowledge will support research into drug therapies based on restricting the horizontal transfer or stable replication of drug resistance or virulence plasmids in human pathogens

To identify and understand drivers of antimicrobial resistance responsible for emergence of extended spectrum beta-lactamase (ESBL) Escherichia coli and Klebsiella pneumoniae in pediatric and maternal populations within Uganda.

This study will combine conventional microbiology methods, whole genome sequencing, as well as social and behavioral sciences-based methods and, a combination of both longitudinal and retrospective study designs to generate knowledge with the potential to transform our understanding of the local and global emergence and spread of antimicrobial resistance especially in hospitals and community settings. This study will also elaborate the acquisition and transmission dynamics of antimicrobial resistance between communities or within communities over time. Furthermore, this project will also provide an ecological perspective that can integrate environmental monitoring of antimicrobial resistance in community settings with monitoring of antimicrobial resistance in the hospital settings. In addition, the study will also expand the available primary data in regards the understanding of the global antimicrobial resistance epidemiology, will contribute towards improving infection control in hospitals and community settings as well as provide potentially leverageable opportunities to build in new ways on existing public health interventions and/or antimicrobial resistance monitoring platforms.

The project aims to discover molecular scaffolds that could be forerunners of EAEC therapeutics. Following a small molecule library screen, the team is evaluating hits, determining their mechanisms of action and their potential to be progressed as drug candidates. The group will also apply their anti-biofilm screen to other small libraries with a view to increasing the repertoire of promising leads against EAEC and other neglected enteric pathogens.

This project builds on intra-African and international (pharmaceutical companies, academic institutions) collaborations to identify medicinal chemistry starting points from the screening of target-based chemical libraries against the causative agents of malaria, leishmaniasis and Human African Trypanosomiasis (HAT), also known as sleeping sickness.

The project aims to develop an assay/test to measure the activity of antimalarial drugs on the transmission of the malaria parasite. This innovative work is anticipated to be a useful addition to the current tools for drug discovery and to support the malaria eradication agenda

The emergence of drug resistance has rendered most clinically used drugs ineffective. There is, therefore, the need to discover new, safe, effective and novel chemo types with new modes of action. This project seeks to continue medicinal chemistry efforts on a chemical class identified from the MMV Pathogen box to develop an early lead with in vivo antitubercular/antimalarial activity as a proof-of-concept

The propensity of malaria parasites to develop resistance motivates the ongoing discovery and development of antimalarials with new modes of action. Heinrich Hoppe’s research focuses on employing novel bioassays to find inhibitors of Arf1, a GTPase that regulates protein secretion, in order to validate it as an antimalarial drug target

This research proposes to apply new protocols that have been developed at RUBi combined with traditional computational drug discovery approaches to further improve our understanding of rational drug discovery in the context of tuberculosis and malaria. Additionally, where applicable, it aims to identify novel hits from African natural products against these diseases as screening of them may lead to the development of novel pharmaceutics in Africa

There is a compelling need for the development of new drugs for trematode infections since current drugs are often ineffective and/or have widespread resistance. Drug repurposing which is advantageous in fast-tracking compounds into clinical studies is a promising drug discovery approach. Edwin’s research involves the application of computational biology in the drug repurposing of kinase inhibitors as new therapies for trematode infections

The aim of the research is to identify selective modulators of the TB bacteria, Mycobacterium tuberculosis (Mtb), to support development of newer, more effective therapies.

Niaina Rakotosamimanana of the Pasteur Institute of Madagascar in Madagascar will develop a low-cost tuberculosis diagnostic and molecular test for pregnant women using dried blood samples drawn from finger pricks. This dried-blood spot based test is minimally invasive, can be used in remote areas where people lack access to all-weather roads and lack of infrastructure that has direct impact on health outcomes. The dried-blood spot can be sent via mail to the health centers for testing without established cold chain methods and meets several of the criteria set by the World Health Organization regarding quality of TB diagnostic tools. Dried-blood samples have a wide range of diagnostic capacity and have been shown to have advantages over other biological samples in terms of cost, ease of collection, transport, and storage.

Christine Musyimi of Africa Mental Health Foundation in Kenya will engage Traditional Birth Attendants (TBAs) in rural Kenya to provide psycho-social interventions to mothers during the perinatal period and refer complicated cases of depression to health facilities. This approach aims to ensure accessible and acceptable basic mental health care in under-resourced areas while linking these mothers to primary health care to ensure safe deliveries and promote their mental well-being and that of the baby even after birth. TBAs were selected for this project as they are an important resource for reducing gaps related to the scarcity of mental health professionals and demanding workloads due to the high number of patients in primary health care settings as well as due to their accessibility and affordability, thereby promoting a sense of belonging to the TBAs.

Muriel Vray of Institut Pasteur of Dakar in Senegal will evaluate a loop-mediated isothermal amplification assay (LAMP), a simple, robust and inexpensive nucleic acid amplification assay, to quantify/semi-quantify hepatitis B virus (HBV) DNA levels in Senegal. In the first step, they will validate the assay in a reference laboratory in Dakar, compared with the reference standard PCR assay. In the second step, they will validate the assay in a decentralized context at a rural health center in Senegal. They will also evaluate the feasibility and acceptability of the use of LAMP.

Dahabo Adi Galgallo of the Kenya Field Epidemology and Laboratory Training Programme (FELTP) in Kenya, will develop a waterproof, coin-sized, solar-powered GPS-tracking device that will be fitted into cultural jewelry of expectant mothers in pastoralist and nomadic communities so health workers can easily locate and provide them with pre- and antenatal care where they are. Integrated maternal mobile health delivered to pastoralist women will decrease maternal mortality, infant mortality and improve vaccination coverage, which will improve infant survival in this population because women can easily be traced and care given. They will identify expectant women in Marsabit county and immunize them against tetanus, conduct laboratory profiling to detect any diseases for early intervention and provide continuous antenatal care from the duration of the pregnancy to birth and then immunization of newborns up to 12-month in tandem with continuous health education.

Angela Koech Etyang of Aga Khan University in Kenya will make basic screening tests available to pregnant women at dispensaries and health centers that do not have laboratory facilities. These tests screen for conditions such as HIV, syphilis and anemia. They will employ simple existing technologies that enable these tests to be carried out during the clinic visit quickly, easily and by the nurse who is providing the care in pregnancy. They will evaluate whether introducing these tests as a package will integrate well with routine provision of care in these facilities and whether this will result in earlier screening and wider coverage of screening for pregnant women.

Jesse Gitaka of Mount Kenya University in Kenya will lead the development and deployment of a point-of-care diagnostic for bacterial infections that have been implicated in poor pregnancy outcomes such as premature deliveries, still births, maternal and newborn sepsis and deaths. Their project will enable quick detection of these bacteria allowing for prompt treatment. They will test whether treating for these bacterial infections, which are usually not diagnosed, improves pregnancy outcomes in field situations. This strategy has the potential to inform pregnancy monitoring and follow up practice and policy.

Eric Ogola of Jaramogi Oginga Odinga University of Science & Technology proposes to reduce deaths in young children by developing an easier way to decide which antibiotic to use in blood-borne infections in children less than one month old. This will lead to judicious use of antibiotics and prevent the development of drug resistance. Clinicians in health facilities without laboratories will be able to make an educated guess on the best treatment that is likely to give an effective outcome. Authorities will also be able to monitor the rate of treatment failure and recommend new guidelines in time, thereby preventing more deaths over time.