Alex James

We develop a novel individual-based population-dynamics model of academic career progression, using 15 years of data from over 1000 academics from one university. Our model improves on previous models, which, by homogenizing career progression, may underestimate the costs of being female. We find multiple effects that compound to slow career progression for women. Women are hired at lower ranks than men, then face the sticky floor problem of getting stuck at the bottom for longer. Further, individuals in science, technology, engineering and mathematics (STEM) fields are promoted more quickly; this disproportionately affects women who are more prevalent in non-STEM fields. Our model reveals that age is more complicated than others have found with ordinary differential equation (ODE)-based models. Women are older when hired, and promotions favour the young; hence, age costs women more. Finally, the probability of attrition rises with years spent at the same rank, regardless of gender. Since women are promoted more slowly, they experience higher attrition rates. We also deploy our model to test possible interventions. We find that just hiring more women will not work. A more nuanced set of interventions is required. Gender parity will only be achieved at the highest ranks if hiring rates are jointly equalized across gender, academic rank and discipline.

We develop a novel individual-based population dynamics model of academic career progression, using 15 years of data from over 1,000 academics from one university. Our model improves on previous models, which, by homogenising career progression, may underestimate the costs of being female. We find multiple effects that compound to slow career progression for women. Women are hired at lower ranks than men, then face the sticky floor problem of getting stuck at the bottom for longer. Further, individuals in STEM fields are promoted more quickly; this disproportionately affects women who are more prevalent in non-STEM fields. Our model reveals age is more complicated than others have found with ODE-based models. Women are older when hired, and promotions favour the young; hence age costs women more. Finally, the probability of attrition rises with years spent at the same rank, regardless of gender. Since women are promoted slower, they experience higher attrition rates. We also deploy our model to test possible interventions. We find just hiring more women will not work. A more nuanced set of interventions is required. Gender parity will only be achieved at the highest ranks if hiring rates are jointly equalised across gender, academic rank, and discipline.

Gender inequities in scientific research persist, from citation indices to collaboration networks and funding success. Current estimates for achieving equal representation remain in timescales of decades to centuries. To help individuals and groups improve their gender representation in a shorter timeframe, we present the W -index, a simple metric to evaluate gender ratios, and specifically the representation of women, in research collaborations and other aspects of scientific life. Here it is applied to three case studies: internal collaborations derived from six years of research outputs across a university, co-author groups from 60 years of publications across a journal collection, and supervisor-student relationships over 60 years. Despite contrasting sources, these datasets show common features: W -index increases as the proportion of women increases W -index also increases with age or career stage, and women have a consistently higher W -index compared to men. We finish with reflections and recommendations for individuals, organisations, and funding bodies to action positive and timely change towards gender equity.

When COVID-19 restrictions were removed, many countries observed infection surges in respiratory pathogens like respiratory syncytial virus (RSV) and influenza. This has been postulated to have been caused by reduced immunity in populations due to non-pharmaceutical interventions that reduced transmission of these pathogens. This pandemic-related phenomenon has been termed ‘immunity debt’ or ‘immunity gap’. We propose a simple extension of the classic susceptible–immune–susceptible model to explore this phenomenon. The model is parametrized using RSV antibody data derived from healthy adults in Aotearoa, New Zealand. We consider a case study based on the prolonged stringent public health measures during the border closure years of 2020–2022 and compare these findings to observed hospitalization trends in Aotearoa, New Zealand. Our model predicts that diseases with very fast waning immunity are less likely to see increased infection rates after prolonged periods of stringent public health measures. However, diseases that wane moderately fast, such as RSV, are more likely to see a strong resurgence of cases when restrictions ease. Our results can be used to predict disease characteristics most likely to lead to strong resurgences after periods of prolonged restrictions and thus inform future public health responses.

Tradescantia fluminensis is a herbaceous monocot originally from South America which is an invasive weed in many countries. Manual control is highly intensive and efforts have been made to establish colonies of three biocontrol beetles: Neolema ogloblini , Neolema abbreviata and Lema basicostata . Each beetle has its own characteristic life cycle and pattern of tradescantia damage. We construct a model of both plant and beetle population to assess the initial stages of interaction after the release of each biocontrol species. We parametrize the model using field data and give values for parameters which are hard to observe. We use the model to estimate the size of the released population needed for immediate plant control and test the sensitivity of this estimate to parameter estimates using a novel Monte Carlo method. Our results show that this may be a successful method of biocontrol under some circumstances, but the initial number of beetles released would be unrealistically large in some cases. We consider ways that this model could be expanded in future to evaluate longer-term interactions.

Toxoplasma gondii is one of the world’s most prevalent parasites and has significant impacts on the health of humans, domestic animals and wildlife. In New Zealand’s rural environments, T. gondii creates economic losses for the farming industry and threatens vulnerable native avifauna and marine mammals. Predator control of rodents and feral cats has potential to reduce or even eliminate transmission of T. gondii on farms; however, the efficacy of such management is uncertain. We apply a mathematical model of T. gondii transmission dynamics in feral cat and rodent populations in New Zealand farmland and simulate varying intensities of predator control to predict changes in T. gondii prevalence and environmental contamination levels over time. The model predicts that predator control is relatively ineffective for reducing transmission in areas with high environmental contamination rates. However, assuming low rates of environmental contamination, local elimination of T. gondii could be achievable, for example, by control that sustains large reductions of 88%, 69% and 59% in feral cat, house mouse and ship rat populations, respectively, over 56 weeks. Predator control is, therefore, a potentially viable approach for managing T. gondii in some rural environments, but only if high levels of population control are sustained.

Surges in infections caused by respiratory pathogens have been documented in multiple settings after relaxation of pandemic restrictions. Antibodies to major antigens from respiratory syncytial virus and group A Streptococcus waned significantly in a longitudinal adult cohort throughout the pandemic. This waning may have contributed to the pathogen-surges that followed.

We use data from 30 countries and find that the more women in a discipline, the lower quality the research in that discipline is evaluated to be and the lower the funding success rate is. This affects men and women, and is robust to age, number of research outputs, and bibliometric measures where such data are available. Our work builds on others’ findings that women’s work is valued less, regardless of who performs that work.

We use data from 30 countries and find that the more women in a discipline, the lower quality the research in that discipline is evaluated to be and the lower the funding success rate is. This affects men and women, and is robust to age, number of research outputs, and bibliometric measures where such data are available. Our work builds on others’ findings that women’s work is valued less, regardless of who performs that work.

We use data from 30 countries and find that the more women in a discipline, the lower quality the research in that discipline is evaluated to be and the lower the funding success rate is. This affects men and women, and is robust to age, number of research outputs, and bibliometric measures where such data are available. Our work builds on others’ findings that women’s work is valued less, regardless of who performs that work.