The diagnostic of SARS-CoV-2 infection consists of sampling the oro- and nasopharynx (ONP) of people. This can be an amazing source of secondary data to fight the pandemic. Bacteria population composition has been shown multiple times to be a good indicator of the status and evolution of different ecosystems from soil to human microbiomes.
In this project, we want to use diagnostic swabs to build an algorithm that will allow predicting which patients may develop complications. The mapping of ONP microbial community difference in the SARS-CoV-2 positive (mild symptomatic and severe symptomatic) and SARS-CoV-2 negative populations by 16S rRNA sequencing. Further, metagenomic sequencing (which provides richer and likely more predictive data compared to 16S data) to identify bacterial species, strains, gene families, or cellular pathways that differ in abundance between severe and mildly symptomatic patient groups. The resulting data will indicate a refined list of markers generated using different machine learning methods, which can be applied to study SARS-CoV-2 progression and severity.
