arXiv subjects

Ruth Bowness

Publications and source records attributed to Ruth Bowness.

3 recordsLinked to original sources

arXiv2026

Equation Learning for multiscale models of infectious diseases

Tuberculosis (TB) is an airborne disease caused by the pathogen Mycobacterium tuberculosis. In 2023, according to the World Health Organization, it ''probably'' replaced COVID-19 as the leading cause of death from an infectious agent globally; in the nineteenth century, one in seven of all humans deaths were as a result of tuberculosis. More than 10 million people are diagnosed with TB every year. The majority of cases in adults occur in males (62.5% of all global adult cases in 2023, compared to 37.5% in females). The main reasons for males suffering from a higher burden of global TB cases, compared to females, is likely to be a combination of within-host factors, such as differences in immune response, and population-scale factors, such as likelihood of completing treatment. To investigate the impact different scales have in determining this higher TB burden in males, we have developed a gender/sex-stratified multiscale framework. We have learnt ordinary differential equations (ODEs) to capture the average output of an agent-based within-host model, and used the resulting equations to describe the within-host scales of the multiscale framework. We evolve the population demographics at the between-host scale using ODEs, and link the scales with stochastic coupling functions. We have considered counterfactual scenarios to elucidate the impact of sex and gender on the infectious disease dynamics of TB. This paper is intended to provide a proof-of-concept for the development and implementation of the presented multiscale framework.

q-bio.PE↗

arXiv2026

A model of tuberculosis progression using CompuCell3D

Tuberculosis (TB) is an airborne disease caused by the bacterium Mycobacterium tuberculosis (M. tb). Prior to the COVID-19 pandemic, TB was the leading cause of death from an infectious agent globally. However, most people exposed to M. tb do not develop active TB and go on to display symptoms. Instead, in the majority of cases, the bacteria are contained within a granuloma (an aggregation of immune cells) without being eliminated; this is called latent TB. The spatial organisation of the bacteria and immune cells is important in determining whether an individual exposed to M. tb will develop latent or active TB. In this paper, we present a multi-cell, multiscale model of TB progression to investigate the importance of the spatial organisation. This is a novel TB within-host dynamics modelling framework, having been developed using CompuCell3D (CC3D), an open-source computer software used for simulating cellular biological processes both within and between cells. We used this model to compare the generated results with those from a previously developed within-host infectious disease model. We found that, although the results of our CC3D model mostly agree qualitatively with those from the previously developed model, there are quantitative differences. Additionally, we conducted a robustness analysis of key model parameters from the CC3D model to determine their importance to the CC3D model output, using a methodology specifically designed for agent-based models. The model output appears to be robust in response to perturbations in parameters controlling chemotactic movement, but less so in response to perturbations in parameters controlling persistence of movement in cells, cell adhesion and volume constraints. This work compares our CC3D model of TB progression with another agent-based modelling approach to the same problem.

q-bio.QM↗

arXiv2026

An agent-based modelling approach to investigate the impact of gender on tuberculosis transmission in Uganda

Tuberculosis (TB) is an airborne disease caused by the pathogen Mycobacterium tuberculosis. In 2023, it returned to being the leading cause of death from an infectious agent globally, replacing COVID-19; in the nineteenth century, one in seven of all humans died of tuberculosis. More than 10 million people are diagnosed with TB every year. The majority of cases in adults occur in males (62.5% of all global adult cases in 2023, compared to 37.5% in females). The main reasons for males suffering from a higher burden of global TB cases, compared to females, may be in large part due to population-scale factors, such as employment type, the quantity and type of social contacts they make, and their health-seeking behaviours (e.g. differences in diagnostic and treatment delays between genders). To investigate which population-scale factors are most important in determining this higher TB burden in males, we have developed an age- and gender-stratified, spatially heterogeneous epidemiological agent-based model. We have focused specifically on Kampala, the capital of Uganda, which is a high-burden TB country. We considered counterfactual scenarios to elucidate the impact of gender on the epidemiology of TB. Setting disease progression parameters equal between the genders leads to a reduction in both male-to-female case ratio and total case numbers.

q-bio.PE↗