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.
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.
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.
Diawo Diallo of Institut Pasteur de Dakar in Senegal will validate and implement a timely and up-to-date surveillance system of zika virus prevalence in the mosquito population in the Kédougou area using an innovative integrated device developed by Gopaul from Institut Pasteur in Paris. This 3-in-1 device includes a mosquito trap, an analysis station that will carry an antibody-based detection system with an easy to read color change result and a mapping software to create a real-time map of arbovirus infected mosquitoes. The outcome will be the production of tools that can be used to implement focused and ecofriendly vector control interventions to improve maternal and neonatal health.