Linking Infectious disease Front-liners’ control Efforts with central public health authorities in The African Great Lakes Region
The African Great Lakes region is one of the most densely populated areas in Africa and also one of the areas globally with the highest biodiversity, and forced or voluntary migrations. Very limited information is available on the prevalence and epidemiology of infectious diseases, especially gastrointestinal diseases and antimicrobial resistance that has been very understudied. Logistically it is difficult to sample and conventionally study infectious diseases in the area and such studies have limited real-time value locally. In this study we will take full advantage of the potential offered by combining field-deployable nanopore sequencing combined with metagenomics and lap-top bioinformatics, to establish frontline sequencing at remote sites and linking this with central sharing of analytic output. Newly developed bioinformatics solutions by us have made it possible to perform simple bioinformatics analyses in real-time using lap-tops and subsequently share the analytic output simply using the mobile net, avoiding the need of transferring large amounts of data and access to high-performance computing. In GREAT-LIFE we will establish sequencing across six countries in the region and use this to study the abundances of AMR in villages and refugee camps. Linking this with spatial and temporal epidemiological data will enable us to identify locally relevant drivers for AMR and provide data for changing empiric treatment and policies. In addition, we will utilize sequencing directly on GI samples to identify the causative agents (and their AMR) both to provide data for policies, but also to provide immediate results for direct patient care in the frontline. We will educate a number of people in the very frontline to utilize field-sequencing and bioinformatics, as well as more advanced bioinformaticians and epidemiologist centrally. The data generated will be linked in real-time through a central hub in Tanzania to public health authorities for actions.
The African Great Lakes region is one of the most densely populated areas in Africa and also one of the areas globally with the highest biodiversity, and forced or voluntary migrations. Very limited information is available on the prevalence and epidemiology of infectious diseases, especially gastrointestinal diseases and antimicrobial resistance that has been very understudied. Logistically it is difficult to sample and conventionally study infectious diseases in the area and such studies have limited real-time value locally. In this study we will take full advantage of the potential offered by combining field-deployable nanopore sequencing combined with metagenomics and lap-top bioinformatics, to establish frontline sequencing at remote sites and linking this with central sharing of analytic output. Newly developed bioinformatics solutions by us have made it possible to perform simple bioinformatics analyses in real-time using lap-tops and subsequently share the analytic output simply using the mobile net, avoiding the need of transferring large amounts of data and access to high-performance computing. In GREAT-LIFE we will establish sequencing across six countries in the region and use this to study the abundances of AMR in villages and refugee camps. Linking this with spatial and temporal epidemiological data will enable us to identify locally relevant drivers for AMR and provide data for changing empiric treatment and policies. In addition, we will utilize sequencing directly on GI samples to identify the causative agents (and their AMR) both to provide data for policies, but also to provide immediate results for direct patient care in the frontline. We will educate a number of people in the very frontline to utilize field-sequencing and bioinformatics, as well as more advanced bioinformaticians and epidemiologist centrally. The data generated will be linked in real-time through a central hub in Tanzania to public health authorities for actions.

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