A Hybrid Individual-based Mathematical Model to study Bladder Infections

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).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.Publisher PDFPeer reviewe

A hybrid individual-based mathematical model to study bladder infections

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).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.Publisher PDFPeer reviewe

Developing a hybrid agent-based mathematical model to study bladder infections

Urinary Tract Infections (UTIs) are amongst the most common infections worldwide, affecting well over 150 million people each year. UTIs now account for 40% of all hospital-acquired infections and they are becoming harder to treat, with an estimated 1 in 3 being resistant to the most commonly used antibiotics. UTIs are also notorious for recurring. Using agent-based modelling techniques we have developed a model that describes the infection process in bladder infections. Gaining a better understanding of the pathophysiology can prove key to the development of new treatment strategies with greater success and less resistance rates. Data arising from animal and cell culture models are incredibly valuable but can be limited by ethical considerations and time constraints. Simulations from in silico models can be analysed and their results can complement clinical findings, adding to the understanding of how the infection behaves and how we might improve treatment. We simulate discrete agents in the system: E. Coli bacteria and the immune cell types reported to have critical roles in lower UTIs (LY6C macrophages, neutrophils and mast cells). The capacity of the bacteria to penetrate the bladder epithelial barrier and seek refuge in the bladder epithelial cells is a critical initiating step of infection and this process is simulated in our model. We also integrated in our model a treatment framework which we have used to investigate the effects of Trimethoprim, the most commonly used antibiotic for bladder infections, on the infection outcome

UTImodel Datasets

Here we find the datasets, example input parameters and Python code used to conduct the analysis of the UTImodel output and generate the plots used in the manuscript: "A Hybrid Individual-based Mathematical Model to study Bladder Infections"

A hybrid individual-based mathematical model to study bladder infections

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