A hybrid individual-based mathematical model to study bladder infections
Abstract
Introduction : Bladder infections are common, affecting millions each year, and are often recurrent problems. Methods : We have developed a spatial mathematical framework consisting of a hybrid individual-based model to simulate these infections in order to understand more about the bacterial mechanisms and immune dynamics. We integrate a varying bacterial replication rate and model bacterial shedding as an immune mechanism. Results : We investigate the effect that varying the initial bacterial load has on infection outcome, where we find that higher bacterial burden leads to poorer outcomes, but also find that only a single bacterium is needed to establish infection in some cases. We also simulate an immunocompromised environment, confirming the intuitive result that bacterial spread typically progresses at a higher rate. Conclusions : With future model developments, this framework is capable of providing new clinical insight into bladder infections.
Citation
Lasri Doukkali , A , Lorenzi , T , Parcell , B J , Rohn , J L & Bowness , R 2023 , ' A hybrid individual-based mathematical model to study bladder infections ' , Frontiers in Applied Mathematics and Statistics , vol. 9 , 1090334 . https://doi.org/10.3389/fams.2023.1090334
Publication
Frontiers in Applied Mathematics and Statistics
Status
Peer reviewed
ISSN
2297-4687Type
Journal article
Rights
Copyright © 2023 Lasri Doukkali, Lorenzi, Parcell, Rohn and Bowness. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Description
Funding: RB was supported by a fellowship funded by the Medical Research Council, MR/P014704/1, and also acknowledges funding from the Academy of Medical Sciences (London), the Wellcome Trust (London), the UK Government Department of Business, Energy and Industrial Strategy (London), the British Heart Foundation (London), and the Global Challenges Research Fund (Swindon, UK; grant number SBF003\1052). TL gratefully acknowledges support from the Italian Ministry of University and Research (MUR) through the grant Dipartimenti di Eccellenza 2018-2022 (Project no. E11G18000350001) and the PRIN 2020 project (No. 2020JLWP23) Integrated Mathematical Approaches to Socio-Epidemiological Dynamics (CUP: E15F21005420006).Collections
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