@article{mbs:/content/journal/acmi/10.1099/acmi.ac2019.po0017, author = "Vlazaki, Myrto and Restif, Olivier", title = "Mathematical modelling to characterise the in vivo dynamics of Salmonella in the naïve and immunised host", journal= "Access Microbiology", year = "2019", volume = "1", number = "1A", pages = "", doi = "https://doi.org/10.1099/acmi.ac2019.po0017", url = "https://www.microbiologyresearch.org/content/journal/acmi/10.1099/acmi.ac2019.po0017", publisher = "Microbiology Society", issn = "2516-8290", type = "Journal Article", eid = "27", abstract = "Systemic salmonellosis encompasses typhoid and paratyphoid fever, and invasive non-typhoidal salmonellosis, with high mortality and morbidity amongst children and the immunocompromised in low-resource settings. Immunisation efforts remain hampered by the unavailability of safe vaccines with cross-protectivity against causative Salmonella strains. Characterisation of the within-host Salmonella dynamics in the naïve and immunised host can elucidate the mechanisms by which different vaccine types exert their protective effect, and help in vaccine selection and design. Experimental data tracking the changes in bacterial population composition in the different tissues of the host at different timepoints can be coupled with mechanistic mathematical models to estimate the parameters governing the processes of bacterial replication, killing and inter-organ migration. Using a recently described minimisation-divergence estimation approach, we extend a three-compartmental mechanistic model and re-analyse existing datasets to better characterise the bacterial migratory processes between the blood, liver and spleen in the early stages of infection, and the overall Salmonella dynamics in the later phases in the naïve host. We apply the same model to published data from mice immunised with either a live-attenuated or killed whole-cell vaccine to identify their in vivo differential impacts on Salmonella migration, replication and death. Finally, we identify alternative experimental designs to improve the statistical qualities of the mathematical model and allow better inference of parameters governing the unobserved processes of bacterial dynamics.", }