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.
The proposed project aims to investigate the prevalence of AMR in the human population and will work from the hypothesis that metagenomic sequencing of human sewage can be used to detect, and in combination with epidemiological / ecological modeling, explain and predict emergence and trends of AMR. Sewage will be sampled from eight cities and sites across South Africa, deep metagenomic sequenced, the raw data shared into a common global secure cloud and thoroughly analyzed for abundances of AMR genes, taxonomic composition and other genes of relevance using a standardized bioinformatics pipeline for this purpose.
The main aim of the project is to investigate use of molecular methods for detection of resistance genes without culturing organisms and the study proposes a new innovative approach to combine genomic data analysis with epidemiological evidence to explain patterns of AMR transmission between food animals and humans.
The aim of the project is to determine whether the antibiotic profile found WWTPS may be used as a proxy of community antibiotic usage. The antibiotic resistant bacteria (ARB) and antibiotic resistant gene (ARG) profiles in WWTPs and receiving environments in South Africa and other African countries will also be determined with the intention of providing mitigating strategies to prevent their release into the receiving environment.
The study focuses on the transmission dynamics of resistance in poultry farm workers to estimate the possibility of zoonotic transfer of pathogens. The study will be looking at humans, animals, air and water as well
The aim of the project is to use advanced statistical and machine learning techniques to interrogate the vast amount of existing data relating to antimicrobial resistance (AMR). It is of importance to identify key risk factors driving clinical cases of resistant infections in a One Health framework. The project relies on a network of researchers across Africa generating relevant data, which implies that the data will come in many different forms and wildly varying quality. This is seen as part of the key research question: instead of assessing the data from a binary perspective (i.e. "Yes, a correlation exists" or "No, there is no correlation"), we will ask "What is the most we can possibly get out of the data, and what is the most cost-effective way to improve prediction?" AMR surveillance will always be limited in resource constrained settings; our research will assess the potential of generated data and identify lowest hanging fruit within these constraints.
The goal of this project is to establish an ICU-based sentinel surveillance network in Africa, describe prevalent and incident patterns of colonization in representative ICU’s and evaluate the correlation between AMR patterns in clinical isolates and in surveillance cultures of the ICU microbiome. Sentinel surveillance through ICU AMR monitoring and data sharing could be used identify modifiable factors in the persistence and spread of AMR.
The study intends to implement an ecological multi-host surveillance to document the bacterial infections and antibacterial resistance (ABR) among humans, animals, birds and fishes sharing the environment and linkage with antibiotics and disinfectant exposures at individual, household/habitation and community levels from different sources. A multi-host and multi-species approach shall improve understanding on pattern and spread of bacterial infection and resistance considering the "One Health" perspective. The application of geospatial epidemiology technology shall allow integrating data from multiple sources. The evidence base generated shall address the gap to transform the public health action across sectors and trigger advanced molecular and genetic epidemiology.
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.