Attributing the source of antimicrobial resistant diarrheal pathogens in African children
Lead Research Organisation:
University of Bath
Department Name: Biology and Biochemistry
Abstract
Diarrhoeal disease is a major cause of mortality among children in low-income countries. Joining a large MRC funded
program you will collect and sequence metagenome samples to quantify the relative contribution of different
antimicrobial resistant pathogens to human infection. Time spent in Bath, Bristol and The Gambia will help understand
transmission networks, and bioinformatics and machine learning risk models will identify effective interventions.
House crowding, cohabitation with animals and poor sanitation/food safety are all potential risk factors, but effective
interventions depend upon quantitative estimates of infection sources. Genome sequencing technologies and
bioinformatics analyses provide a means for explaining these cryptic disease networks by identifying differences between
strains and tracking transmission.
Building on an established collaborative network in the UK and The Gambia, we will develop a program of globalized
enteropathogen surveillance. Specifically, we will: (i) sample and genome sequence thousands of isolates from multiple
sources; (ii) develop databases and novel analysis pipelines (machine learning) to identify source attribution markers; (iii)
quantify the relative contribution of different human infection sources; (iv) use a cost-benefit risk models to identify the
most effective interventions in the transmission network. This evidence-based approach will enable effective local
interventions and reduce the burden of diarrhoeal disease.
program you will collect and sequence metagenome samples to quantify the relative contribution of different
antimicrobial resistant pathogens to human infection. Time spent in Bath, Bristol and The Gambia will help understand
transmission networks, and bioinformatics and machine learning risk models will identify effective interventions.
House crowding, cohabitation with animals and poor sanitation/food safety are all potential risk factors, but effective
interventions depend upon quantitative estimates of infection sources. Genome sequencing technologies and
bioinformatics analyses provide a means for explaining these cryptic disease networks by identifying differences between
strains and tracking transmission.
Building on an established collaborative network in the UK and The Gambia, we will develop a program of globalized
enteropathogen surveillance. Specifically, we will: (i) sample and genome sequence thousands of isolates from multiple
sources; (ii) develop databases and novel analysis pipelines (machine learning) to identify source attribution markers; (iii)
quantify the relative contribution of different human infection sources; (iv) use a cost-benefit risk models to identify the
most effective interventions in the transmission network. This evidence-based approach will enable effective local
interventions and reduce the burden of diarrhoeal disease.
Organisations
People |
ORCID iD |
Tiffany Taylor (Primary Supervisor) | |
Shani ALI (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/T008741/1 | 01/10/2020 | 30/09/2028 | |||
2749182 | Studentship | BB/T008741/1 | 01/10/2022 | 30/09/2026 | Shani ALI |