mapPat: tracking pathogens evolution in space and time

Background: The COVID-19 pandemic highlighted the importance of genomic surveillance for monitoring pathogens evolution, mitigating the spread of infectious disorders and informing decision making by public health authorities. Since the need for the summarisation and interpretation of large bodies of data, computational methods are critical for the implementation of effective genomic surveillance strategies. Material and Methods: Developed in R (v4.4.1) under Shiny (v1.9.1), mapPat uses publicly-available metadata and sequences from GISAID or NextStrain to summarise key features of variants, lineages and mutations at different times and geographic levels. Results: mapPat is an R Shiny application for the interactive visualisation of pathogens genomic data in space and time. It collects data in tabular format by processing publicly available metadata and associated sequences. Users interact with mapPat by setting filters through a dedicated control panel; once a selection is applied, tabs are populated with informative plots and the selected data can be visualised and inspected. mapPat allows the dynamic monitoring of the evolution of variants, lineages and mutations in the genome of a pathogen at glance, through informative geographic maps and elegant data visuals. The tool was tested by retrospective analysis of SARS-CoV-2 circulation in Italy. Conclusion: mapPat may support fighting future pandemics providing a fine grained map of pathogens evolution and circulation, and thus represents a useful method for the genomic surveillance of pathogens. mapPat is available at GitHub (https://github.com/F3rika/mapPat.git) together with a dedicated Zenodo Repository (https://doi.org/10.5281/zenodo.14163899) of precomputed datasets.