Abstracts 2025/26

SBIDER Seminar Abstracts Term 3

27 April Frithjof Lutscher (University of Ottawa): Population dynamics in dynamic landscapes

Abstract

Historically, questions of persistence and spread of biological populations were studied on static landscapes. However, human activities create fragmented landscapes. Global change shifts local climates. So-called ecosystem engineering species alter local landscapes to improve conditions for their own growth. In short, landscape are dynamic, and we need to develop theory to study the dynamics of populations under these changing conditions. In this talk, I will start with the fundamental concepts of spreading speed and minimal patch size, developed in the 1930s and 1950s for static landscapes, and introduce some aspects of landscape change into these
models, to study how these concepts can be adapted to dynamic landscapes. Specifically, I will consider the case of (i) a shifting climate envelope and (ii) an ecosystem engineering species. I will formulate reaction-diffusion equations that (i) are non-autonomous or (ii) have a free boundary. I will then present conditions for population persistence and speeds of spread under such changing conditions. The presentation is aimed at a general audience; students are welcome.

11 May Chris Banks (Edinburgh) Developing Digital Disease Diagnostics

Abstract

Diagnostic tests that can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are more likely to be infected. I am developing methods from machine learning to assess the surrounding risk landscape under which a diagnostic test is applied to augment its interpretation.

Initially this has been developed to predict the occurrence of bovine tuberculosis (bTB) incidents in cattle herds, exploiting the availability of exceptionally detailed testing records. Without compromising test specificity, test sensitivity can be improved so that the proportion of infected herds detected improves significantly with a large number of additional infected herds detected per year beyond those detected by the skin test alone. It is then also possible to exploit feature importance testing for assessing the weighting of risk factors.

I will describe the methods used for bTB and how I am extending into other settings (e.g. BVD) and planning for field testing with a view to developing a general toolkit for machine learning -augmented digital diagnostics and risk analysis.

18 May Grant Lythe (Leeds) T-cell repertoires and cross-reactivity

Abstract

There are approximately 400000000000 naive CD4 T cells in your body, about the same as the number of stars in our galaxy. On the other hand, the number of cells of one TCR clonotype is a small integer that increases or decreases by one cell at a time, when cells divide or die. New clonotypes are released from the thymus and compete with other clonotypes in the periphery for specific and non-specific resources. Mean clonal sizes can therefore be calculated from mean clonal lifetimes. For example, if the ratio of thymic production to peripheral division is four percent, then the number of distinct T-cell clonotypes in the human body is about nine percent of the total number of (naive CD4) T cells. In mice, most TCR clonotypes may consist of just one or two T cells. TCRs recognize peptides (or epitopes), typically 8颅14 amino acids long, bound to MHC molecules on antigen-presenting cells. There cannot only exist a single TCR which recognizes a given peptide because the possible number of peptides is far greater than the number of T cells in a mouse or in one person. Therefore, individual TCRs must recognize multiple peptides if a mammal's T cell repertoire is to be capable of providing coverage against the majority of new pathogens a host might encounter in its lifetime. Patterns of recognition of epitopes by T cell clonotypes (a set of cells sharing the same T cell receptor) are represented as edges on a bipartite network. We introduce a circular space of epitopes, so that T cell cross-reactivity is a quantitative measure of the overlap between clonotypes that recognize similar (that is, close in epitope space) epitopes.

June 1 Samraat Pawar (Imperial) Structural Complementarity Maximizes Feasibility and Stability in Microbial Community Coalescence

Abstract

Microbial communities frequently coalesce through dispersal, disturbance, or deliberate transplantation, yet the dynamical consequences of such coalescence remain poorly understood. In this talk I will show new theoretical results that show how coalescence can be used to enhance microbial community robustness. Using a mechanistic consumer--resource model in which the balance between competition and metabolic cooperation is explicitly tunable, we quantify how interaction structure shapes both feasibility, namely the environmental domain supporting coexistence, and dynamical stability. Cooperation-dominated communities exhibit greater but broader feasibility and intrinsic stability than competition-dominated communities. Strikingly, coalescing communities with maximally distinct interaction structures consistently maximises both feasibility and stability of the resulting assemblage. Heterogeneous coalescence balances reduced facilitation, moderated interspecific effects, and stronger self-regulation. These results identify structural complementarity as a general principle for assembling robust microbial ecosystems and provide a theoretical foundation for microbiome engineering strategies that enhance persistence and functional stability.

15June Will Hart (Oxford) Mathematical modelling of the end of infectious disease outbreaks

Abstract

Following the apparent final case in an infectious disease outbreak, the decision to relax control measures must balance the risk of resurgence against societal costs of keeping interventions in place. For Ebola, the WHO recommends a waiting period of 42 days from the resolution of the final case until the outbreak can be declared over and interventions relaxed. However, such fixed-duration rules do not account for outbreak-specific determinants of the risk of resurgence, such as the reproduction number and the extent of under-reporting. This indicates the need for quantitative approaches to support decision making at the end of an outbreak.

Here, we present a renewal model-based framework for quantifying the risk of additional cases occurring given observed disease incidence data. We demonstrate applications to Ebola outbreaks in DR Congo, where the framework validated real-time decisions on response team deployment. Then, we discuss a range of extensions to our approach, considering under-reporting, reporting delays, overdispersed transmission, and seasonal pathogens.

22June Leonard Dekens (Francis Crick Institute)

Abstract

Abstract: A central question in evolutionary biology is to understand how complex organisms adapt to novel natural selection pressures shifting specific traits without disturbing already adapted traits. One particular difficulty comes from the intricacy and non-linearity of their Genotype-Phenotype map due to pervasive pleiotropy (the propensity of genes to influence multiple traits at once) and epistasis (non-additive interactions between genes). A leading hypothesis to explain how conflicting selection pressures on several traits are mediated is that the GP map evolves to be modular, with weaker or silenced pleiotropic links between traits. However, the scope of this hypothesis is not clear. In our work, we propose to investigate this phenomenon analytically, thanks to an integro-differential model describing the evolutionary population dynamics of complex organisms subject to conflicting selection pressures on two traits. Our model introduces a multi-dimensional operator encoding the transmission of the genetic architecture of these traits combining two major-effect loci accumulating mutations (one locus being a modifier of pleiotropy) and a polygenic background. In an asymptotic regime, we derive a limit system with a much smaller analytical and numerical complexity that accurately describes the dynamics of the different genetic components with the population. On the basis of analyzing this limit system, we are able to identify the conditions leading to an alternative compensatory mechanism based on polygenic compensation that does not involve the silencing of pleiotropy between the two traits._

Abstracts Term 2

16th February 2026: Dimitris Volteras (Francis Crick Institute) Mechanistic modelling and inference of gene regulation from time-resolved transcriptomics

Abstract

Single-cell RNA sequencing (scRNA-seq) reveals extensive variability in gene expression, yet identifying mechanisms underlying gene regulatory dynamics from static snapshots remains challenging. Time-resolved single-cell transcriptomics with metabolic RNA labelling provides access to transcription dynamics, offering opportunities to address these challenges. In this talk, I present a modelling framework that exploits time-resolved transcriptomics to infer global transcriptional principles and gene regulatory relationships. First, I introduce a stochastic model of gene expression in growing and dividing cells that integrates metabolic RNA labelling and cell cycle phase information. Using a scalable approximate Bayesian computation approach, we infer genome-wide transcriptional burst parameters and RNA degradation rates while accounting for technical noise. This reveals scaling of transcription with cell size and waves of transcriptional regulation across the cell cycle. I next show how this framework is extended to model gene regulatory interactions, including direct regulation and co-regulation, while accounting for key confounders such as extrinsic noise, cell cycle coupling and technical noise. Synthetic data generated from the model are used to supervise a neural network that learns to classify gene regulatory scenarios. This allows to predict gene-gene regulatory relationships from time-resolved scRNA-seq data, while quantifying uncertainty. Together, these results demonstrate how integrating stochastic modelling, Bayesian inference and machine learning enables interpretable inference of gene regulation from single-cell data.

19th January 2026: Zena Hadjivasiliou (Frances Crick Institute) Patterning in dynamic environments

Abstract

Patterning, growth and morphogenesis are fundamental and interdependent processes in development. However, the mechanisms through which they interact, and the implications of their simultaneous occurrence and feedback between them, are poorly understood. In the first part of my talk, I will present a combination of experimental and theoretical work where we show that transitions in tissue-scale physical properties are coupled to morphogen signalling and transport during early zebrafish development. Our findings show that morphogen transport is actively regulated by cell and tissue architecture in vivo. We propose that feedback loops between morphogen signalling and tissue organization lock patterning and morphogenesis in a closed feedback loop that ensures that their dynamics are kept in sync. In the second part of the talk, I will present ongoing work in my group that investigates the interplay between tissue geometry, growth, and GRN-driven patterning. By integrating tools from dynamical systems theory with models of growth, I will demonstrate how cells can autonomously interpret global information about tissue size without the need for global scaling mechanisms.

26th January 2026: Liza Semenova (Imperial College London) Pre-trained deep generative surrogates aiding Bayesian inference

Abstract

Recent advances have demonstrated the potential of deep generative models, such as variational autoencoders (VAEs), to encode Gaussian Process (GP) priors or their finite realizations, thereby creating deep generative surrogates. These learned generators can serve as drop-in replacements for original priors within Markov Chain Monte Carlo (MCMC) methods, significantly enhancing inference efficiency. However, this approach loses information about the original priors' hyperparameters, rendering hyperparameter inference impossible and the learned priors less distinct. To overcome this limitation, a new method called PriorCVAE has been developed. By conditioning the VAE on stochastic process hyperparameters, PriorCVAE enables the joint encoding and inference of both hyperparameters and GP realizations. Notably, PriorCVAE is model-agnostic, making it suitable for a wide range of applications, including encoding solutions to ordinary differential equations (ODEs) or stochastic process realisations. This talk will begin with an overview of spatial statistics before introducing the PriorVAE method for encoding prior realizations. I will discuss the advantages and limitations of PriorVAE, which will lead into the introduction of PriorCVAE and the broader concept of deep generative surrogates. Finally, I will demonstrate practical applications and highlight promising directions for future research, particularly in spatial and spatiotemporal inference contexts.

16th February 2026: Dimitris Volteras (Francis Crick Institute) Mechanistic modelling and inference of gene regulation from time-resolved transcriptomics

Abstract

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23rd February 2026: Dan Hungerford Maximising real world vaccine impact for reducing inequalities

Abstract

This talk will start by exploring inequalities in the uptake and impact of childhood vaccines in the UK. Then using the example of infant rotavirus vaccination, I will examine both the successes, ongoing challenges locally and globally in relation to global socio-economic inequalities. Drawing on interdisciplinary research that combines laboratory science, epidemiology, and mathematical modelling, I will highlight key insights. The second part of the talk will focus on our current work on adult influenza vaccines in the UK, including their potential impact on antimicrobial prescribing and post-acute infection outcomes. Throughout I will discuss pragmatic methods we have used to deal with confounding in real-world vaccine studies.

2nd March 2026: Raiha Browning New Bayesian methods and tools to address health challenges

Abstract

The Bayesian framework offers powerful advantages in modelling complex data structures, uncertainty quantification, and incorporating prior knowledge and expert opinion. However, model development and implementation can be challenging for non-technical practitioners, and implementation of Bayesian models often requires extensive computation. In this talk, I will explore the role of Bayesian statistics in decision-making and addressing societal challenges with examples from COVID-19 and neglected tropical diseases.

9th March 2026: Rob Deardon Bayesian behavioural change models: alarm, memory, and feedback

Abstract

The COVID-19 pandemic highlighted the value of epidemic models, but also exposed several limitations in existing modelling approaches. One significant shortcoming is the failure to account for human behaviour change, a key driver of infectious disease transmission. Recent work has introduced Bayesian behavioural change epidemic models, which allow the outbreak-related behavioural change exhibited by a population to be captured by a so-called alarm function. Here, we describe this new modelling framework and use it to motivate a set of open questions about how behavioural responses should be represented in epidemic models. In particular, we ask how population-level responsiveness evolves over time; for example, whether and how phenomena such as fatigue or waning compliance can be identified from data. We also consider whether short-term fluctuations in behaviour driven by external factors such as news reports, holidays, or weather, can be incorporated into the model. Finally, we examine how populations process and remember outbreak information, asking how much weight is given to recent versus more distant signals when forming behavioural responses, and whether this 鈥渕emory鈥� can be inferred from epidemic data. We introduce these ideas via simulated data and data from outbreaks of disease such as COVID-19, influenza, Ebola, foot-and-mouth disease and measles.

Abstracts Term 1

13 October 2025: Luke Davis (University of Edinburgh)

Title: Stochastic geometry of active (living) matter

Abstract: Living and biological systems are typically found in, or are proximal to, nonequilibrium environments. Indeed, much of this nonequilibriumness arises from the energy conversion of individual constituents which, when coupled with many-body interactions, results in striking collective behaviour. Such systems are the focus of a growing field of statistical physics called active matter. As one can imagine, the far-from-equilibrium behaviour of active matter presents several serious challenges to the equilibrium framework of thermodynamics. Here, in an effort to bridge these problems, we explore (for the first time) the stochastic geometry of active matter. This stochastic geometry, e.g., the averaged room to accommodate another sphere, relates exactly to the equation of state for equilibrium hard spheres. We here extend this idea to active systems by analysing the insertion space for repulsive active particles in one and two dimensions using both on- and off-lattice models. In 1D we derive closed-form expressions for the mean insertion cavity size, cavity number, and total insertion volume, all in excellent agreement with simulations. Strikingly, activity increases the total insertion volume and tends to keep the insertion space more connected. These results provide the first quantitative foundation for the stochastic geometry of active matter, and opens up a new route to building a thermodynamics of active and living systems.

Refs: Insertion space in repulsive active matter, L. K. Davis & K. Proesmans arXiv 2509.08131 (2025)

20 October 2025: Jonathan Potts (University of Sheffield)

Title: Nonlocal advection-diffusion for modelling organism space use and movement

Abstract: How do mobile organisms situate themselves in space? This is a fundamental question in both ecology and cell biology but, since space use is an emergent feature of movement processes operating on small spatio-temporal scales, it requires a mathematical approach to answer. In recent years, increasing empirical research has shown that non-locality is a key aspect of movement processes, whilst mathematical models have demonstrated its importance for understanding emergent space use patterns. In this talk, I will describe a broad class of models for modelling the space use of interacting populations, whereby directed movement is in the form of non-local advection. I will detail various methods for ascertaining pattern formation properties of these models, fundamental for answering the question of how organisms situate themselves in space, and describe some of the rich variety of patterns that emerge. I will also explain how to connect these models to data on animal and cellular movement.

27 October 2025: Mrinal Kanti Pal (The Institute of Mathematical Sciences, India)

Title: Complex Trophic Structure Imparts Stability to Large Diverse Ecosystems

Abstract: The long-standing diversity-stability debate in ecology stems from a fundamental paradox: while empirical observations suggest that highly diverse ecosystems, such as tropical rainforests and coral reefs, exhibit stability, mathematical models—including the generalized Lotka-Volterra framework and May鈥檚 random matrix theory—predict that increasing diversity should lead to instability. Resolving this contradiction is crucial, especially in the face of accelerating biodiversity loss, necessitating theoretical frameworks that reconcile these opposing perspectives and generate ecologically realistic predictions. Existing models mostly use pre-assembled assembly of species, and thus ignores the evolving, dynamic nature of species assembly (through invasions and extinctions) in real ecosystems. By incorporating sequential species introduction, we demonstrate that higher diversity can coexist with stability - in contrast to earlier modeling approaches. Furthermore, prevailing models assume species homogeneity and intrinsic stability for all species, whereas we distinguish between producers and consumers, with the latter not possessing inherent stability. We find that reducing consumer proportion relative to producers enhances overall diversity. Further, consumer species extinctions trigger significantly stronger cascading effects than producer extinctions. Another critical aspect we uncover is the variety of interaction types among species. We show that incorporating more exploitative interactions leads to higher stability, with greater proportion of predation among the consumers enhancing system robustness and supporting greater species diversity. Finally, we address a relatively unexplored phenomenon in community assembly, viz., sequential species additions and deletions help in creating different trophic levels. Our findings reveal that ecosystems with more trophic levels exhibit greater robustness to perturbations, emphasizing the stabilizing role of apex predators in complex ecological networks.

03 November 2025: Ruth Bowness (University of Bath)

Title: Modelling Infectious Diseases Within the Host: From Tuberculosis to COVID-19

Abstract: Most infectious disease models focus on transmission between individuals, yet the processes occurring within an infected host are equally critical to understanding disease dynamics and improving treatment outcomes. This seminar will explore mathematical and computational models that capture the complex interactions between pathogens, immune responses, and therapeutics within host tissues.
Focusing on pulmonary infections, particularly tuberculosis (TB) and COVID-19, we will discuss multi-scale individual-based and network models that simulate infection progression across lung environments. For TB, the models reproduce granuloma formation, bacterial phenotypic switching, and antibiotic pharmacokinetics/pharmacodynamics, offering insights into relapse mechanisms and treatment personalisation. For SARS-CoV-2, adaptations of the TB modelling framework allow exploration of epithelial infection dynamics, cytokine regulation, and immune response timing.
Together, these models highlight how within-host computational approaches can bridge experimental data and clinical application, supporting the development of personalised therapies and improved understanding of infectious disease behaviour in complex biological systems.

10 November 2025: Vasthi Alonso Chavez (Rothamsted Research)

Title: Surveillance of ash trees under multiple threats: integrating Emerald Ash Borer dynamics and Ash Dieback prevalence with a) land manager behaviour, and b) volunteer surveillance

Abstract: UK tree species face increasing threats from pests and diseases, leading to significant economic, environmental, and social costs. This research focuses on safeguarding UK ash trees, which are particularly vulnerable to ash dieback (ADB) and the emerald ash borer (EAB). ADB is a fungal disease that has spread and causes substantial damage to ash trees in the UK. The EAB, an insect pest, causes little damage as an adult; however, its larvae disrupt ash trees' water and nutrient transportation by feeding on their inner bark. While the EAB is not currently present in the UK, its spread from Russia and Ukraine poses a serious threat as it 鈥渉itchhikes鈥� on vehicles, in traded firewood and on live plants. In the US, where the EAB has been introduced, it has resulted in the loss of millions of trees. If it reaches the UK, it will further endanger weakened ash trees affected by ash dieback.
Preventing the introduction of new threats like the EAB is often challenging, making early detection and successful management crucial areas of research. However, science-based approaches often overlook the willingness and ability of managers to adopt surveillance and management advice, the contribution that volunteers add to surveillance efforts, as well as the acceptability of different approaches in wider society.
This interdisciplinary approach combines forest epidemiology, entomology, modelling, social science and citizen science to understand ADB, EAB dynamics and human behavioural factors that drive the invasion and spread of tree threats.
We link a spatial model of the spread of EAB to assess the impact of surveillance and management strategies throughout the UK with an agent-based model of stakeholder behaviour describing stakeholder values, actions, and acceptance regarding EAB surveillance. Additionally, as a second example, we use data from Observatree volunteers to quantify the potential contribution of citizens to EAB surveillance.
This research identifies optimal surveillance strategies, unravels motivations and disincentives influencing land managers鈥� surveillance efforts and assesses the potential contribution that volunteers can provide to pests and disease surveillance.

17 November 2025: Diana Meza (University of 糖心TV)

Title: Taming Ecological Complexity: Statistical Models of Bighorn Sheep Contact Networks

Abstract: Understanding how environmental conditions influence host contact patterns is critical for predicting disease dynamics, yet remains challenging in wildlife populations. My research applies quantitative modelling frameworks across diverse wildlife disease systems to uncover mechanisms driving pathogen transmission across landscapes. In this talk, I present findings from my study of desert bighorn sheep in the Mojave Desert. By combining long-term GPS tracking data with geospatial environmental variables, we construct social networks and apply probabilistic models to predict host contacts as functions of spatially and temporally varying environmental conditions. Our results demonstrate that population-level environmental factors at the time of disease introduction structure transmission opportunities, providing mechanistic insight into observed variation in disease outcomes. This work establishes a quantitative framework for understanding how ecological gradients shape contact networks and pathogen spread in wildlife systems.

17 November 2025: Steve Wu (University of 糖心TV)

Title: The role of ducks in detecting Highly Pathogenic Avian Influenza in small-scale backyard poultry farms

Abstract: Previous research efforts on highly pathogenic H5N1 avian influenza (HPAI) suggest that different avian species exhibit a varied severity of clinical signs after infection. Waterfowl, such as ducks or geese, can be asymptomatic and act as silent carriers of H5N1, making detection harder and increasing the risk of further transmission, potentially leading to significant economic losses. For backyard hobby farmers, passive reporting is a common HPAI detection strategy. We aim to develop a computational, mechanistic model to quantify the effectiveness of this strategy by simulating the spread of H5N1 in a mixed-species, small-population backyard flock. Quantities such as detection time and undetected burden of infection in various scenarios are compared. Our results indicate that the presence of ducks can lead to a higher risk of an outbreak and a higher burden of infection. If most ducks within a flock are resistant to H5N1, detection can be significantly delayed. We find that within-flock infection dynamics can heavily depend on the species composition in backyard farms. Ducks, in particular, can pose a higher risk of transmission within a flock or between flocks. Our findings can help inform surveillance and intervention strategies at the flock and local levels.

17 November 2025: Elliot Vincent (University of 糖心TV)

Title: Modelling the adoption of Integrated Pest Management (IPM) as a sustainable method of crop disease control

Abstract: Integrated Pest Management (IPM) is widely acknowledged to be an effective method of crop disease control. It is increasingly being seen as a viable way forward as chemical pesticides face increasing legal constraints, as well as disease resistance, globally. In the UK government's 2025 Pesticides National Action Plan they listed "Encourage uptake of Integrated Pest Management" as the first of three major objectives. Despite this, adoption of IPM in the UK has been limited. Many farmers incorporate some IPM practices into their farming, but few consider it as a large-scale, long-term alternative to pesticide use. There are a large number of behavioural factors contributing to this, including the complexity and effort of IPM compared to conventional fungicide regimes; and the perception that IPM is expensive and ineffective.

In our work, we combine epidemiological and behavioural modelling to explore the current state of IPM adoption in the UK. We investigate whether the 2030 pesticide-reduction targets outlined in the Pesticides National Action Plan are likely to be achieved under current conditions, and following this, we investigate the potential effectiveness of different types of incentive schemes at encouraging IPM adoption.

24 November 2025: Caroline Trotter (University of Cambridge)

Title: Estimating Vaccine Impact across Gavi, the Vaccine Alliance鈥檚 portfolio

Abstract:

鈥�The impact of vaccination on the health of the world鈥檚 peoples is hard to exaggerate. With the exception of safe water, no other modality has had such a major effect on mortality reduction and population growth.鈥�— Stanley Plotkin and Edward Mortimer

However, the impact of vaccination is not a trivial thing to measure! In this talk I will highlight the work of the Vaccine Impact Modelling Consortium, which aims to provide high-quality estimates of the public health impact of vaccination, to inform and improve decision making. This includes estimates of lives saved and DALYs averted for most of the vaccines supported by Gavi, the Vaccine Alliance. VIMC modellers also provide extensive analytic support for a range of vaccine policy decisions. In the context of large reductions in global development assistance for health, VIMC鈥檚 estimates are becoming increasingly important in framing prioritisation decisions.

1 December 2025: cancelled

Title: TBC

Abstract: TBC

8 December 2025: Freya Bull (UCL)

Title: Blood and urine: mathematical modelling for health

Abstract: The richness of life gives rise to many interesting physical and mathematical problems. I will talk about two of them: multi-scale modelling of blood rheology in sickle cell disease, and predicting the incidence of (urinary) catheter-associated bacteriuria.
Sickle cell disease (SCD) is a haematological disorder, caused by a genetic mutation, in which mutant haemoglobin molecules can polymerise under low-oxygen conditions, altering the biophysical properties of the red blood cells. These cell-level differences then result in changes in the whole-blood rheology -- and those rheological properties are in turn linked to the pathophysiology of SCD. I use mathematical modelling and numerical simulation to develop descriptions of cell-cell interactions within blood flow, and validate these against experimental data.
Urinary catheters are prone to colonisation by bacteria. When this leads to symptoms such as fever, pain, or inflammation, it is known as catheter associated urinary tract infection (CAUTI). CAUTI constitute up to 40% of hospital acquired infections, and the potential of novel materials and coatings to reduce the incidence of CAUTI has attracted much attention; however none of these design changes have been found to be effective, with patient studies finding mixed or limited effects. Here we apply a simple biophysical model for bacterial colonisation of urinary catheters to predict the incidence of bacteriuria (the presence of bacteria in the urine) in a clinical trial of antimicrobial catheter coatings -- the CATHETER trial.