A hybrid individual-based mathematical model to study bladder infections

Anas Lasri Doukkali; Tommaso Lorenzi; Benjamin J. Parcell; Jennifer L. Rohn; Ruth Bowness

doi:10.3389/fams.2023.1090334

A hybrid individual-based mathematical model to study bladder infections

Anas Lasri Doukkali, Tommaso Lorenzi, Benjamin J. Parcell, Jennifer L. Rohn, Ruth Bowness^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	1090334
Number of pages	15
Journal	Frontiers in Applied Mathematics and Statistics
Volume	9
DOIs	https://doi.org/10.3389/fams.2023.1090334
Publication status	Published - 3 Feb 2023

Keywords

Mathematical
Model
Individual-based
Simulation
Bladder
Infection
Escherichia coli

Access to Document

10.3389/fams.2023.1090334Licence: CC BY

Doukkali_2023_FAMS_Hybrid-individual_CC
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.
Final published version, 2.92 MBLicence: CC BY

UTImodel Datasets
Doukkali, A. L. (Creator), Zenodo, 2022
DOI: 10.5281/zenodo.7293870
Dataset

Cite this

@article{92d0cfa5af5041a193243e7d12a5cc1d,

title = "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.",

keywords = "Mathematical, Model, Individual-based, Simulation, Bladder, Infection, Escherichia coli",

author = "{Lasri Doukkali}, Anas and Tommaso Lorenzi and Parcell, {Benjamin J.} and Rohn, {Jennifer L.} and Ruth Bowness",

note = "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).",

year = "2023",

month = feb,

day = "3",

doi = "10.3389/fams.2023.1090334",

language = "English",

volume = "9",

journal = "Frontiers in Applied Mathematics and Statistics",

issn = "2297-4687",

publisher = "Frontiers Media S. A.",

}

TY - JOUR

T1 - A hybrid individual-based mathematical model to study bladder infections

AU - Lasri Doukkali, Anas

AU - Lorenzi, Tommaso

AU - Parcell, Benjamin J.

AU - Rohn, Jennifer L.

AU - Bowness, Ruth

N1 - 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).

PY - 2023/2/3

Y1 - 2023/2/3

N2 - 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.

AB - 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.

KW - Mathematical

KW - Model

KW - Individual-based

KW - Simulation

KW - Bladder

KW - Infection

KW - Escherichia coli

U2 - 10.3389/fams.2023.1090334

DO - 10.3389/fams.2023.1090334

M3 - Article

SN - 2297-4687

VL - 9

JO - Frontiers in Applied Mathematics and Statistics

JF - Frontiers in Applied Mathematics and Statistics

M1 - 1090334

ER -

A hybrid individual-based mathematical model to study bladder infections

Abstract

Keywords

Access to Document

Fingerprint

Datasets

UTImodel Datasets

Cite this