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Life sciences · Journal article
Bulletin of Mathematical Biology · September 10, 2026
Raises a question worth testing. It does not answer one.
This is a spatially heterogeneous agent-based epidemiological model developed to explore which population-scale factors (employment, contact patterns, health-seeking behaviour) explain the higher TB burden observed in males versus females in Kampala, Uganda. The model is presented as a proof-of-concept framework and does not provide empirical validation; it generates hypotheses about the relative importance of within-host parameters and behavioural factors in explaining observed sex differences in TB transmission.
Agent-based modelling study with counterfactual scenario analysis. Modelled population of Kampala, Uganda; stratified by age and sex/gender. No empirical cohort enrolled.. Intervention: Counterfactual scenarios manipulating sex/gender-related factors (employment, contact patterns, health-seeking behaviour). Compared with: Baseline model scenario and observed male-to-female TB case ratio. Kampala, Uganda.
Within-host parameters had the largest effect on overall case numbers among scenarios considered Behavioural factors (assortative mixing and gender-specific contact patterns) appear important in explaining elevated male-to-female case ratio Super-spreaders cause a majority of infections; males and individuals with cavitary TB cause more infections on average
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This modelling framework could inform public health policy targeting in high-burden TB settings, but requires empirical validation against real-world transmission data before guiding clinical or population-level interventions.
Agent-based modelling study exploring mechanistic explanations for observed sex differences in TB epidemiology; raises testable questions rather than providing empirical validation in a real-world population.
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This modelling framework could inform public health policy targeting in high-burden TB settings, but requires empirical validation against real-world transmission data before guiding clinical or population-level interventions.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Abstract 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. 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. To investigate which population-scale factors are most important in determining this higher TB burden in males, we have developed an age- and sex/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 sex and gender on the epidemiology of TB, in order to deduce which factors have greater explanatory power in producing the observed differences between sexes/genders. Within-host parameters had the largest effect on overall case numbers among the scenarios considered. On the other hand, behavioural factors (particularly assortative mixing and gender-specific contact patterns) appear important in explaining the elevated male-to-female case ratio. We also found that super-spreaders appear to cause a majority of infections, with males and individuals with cavitary TB causing more infections on average. Our model provides a proof-of-concept framework that could help support public health policy after further validation.
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