A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK.

COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long-established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. The objective of this study is to investigate how a reduction in the exposure of vulnerable individuals to COVID-19 can minimise the number of deaths caused by the disease, using the UK as a case study. To overcome the limitation of long-established compartmental epidemiological models, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation (100,000 runs) along the proposed SEIR-v model is used to study the number of deaths which could be avoided as a function of the decrease in the exposure of vulnerable individuals to the disease. The results indicate a large number of deaths could be avoided by a slight realistic decrease in the exposure of vulnerable groups to the disease. The mean values across the simulations indicate 3681 and 7460 lives could be saved when such exposure is reduced by 10% and 20% respectively. From the encouraging results of the modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer.

Harnessing artificial intelligence for analysing the impacts of nectar and pollen feeding in conservation biological control.

Plant-derived foods, such as nectar and pollen, have garnered substantial research attention due to their potential to support natural enemies of pests. This review is a pioneering exploration of the potential for artificial intelligence approaches to provide insights into the factors that drive the success of conservation biological control (CBC). Nectar and pollen were confirmed as key plant food resources for natural enemies. These have been widely used across differing crop systems and provided by a wide range of CBC interventions, such as field margin flower strips. The combined use of parasitoids and predators is revealed as more successful than either of these guilds alone. CBC success was greater in field crops than in vine and berry crops, whilst interventions using dicotyledonous species that produce nectar in addition to pollen were more successful than using grassy interventions.

The impact of intersectional racial and gender biases on minority female leadership over two centuries.

This study scrutinizes the enduring effects of racial and gender biases that contribute to the consistent underrepresentation of minority women in leadership roles within American private, public, and third sector organizations. We adopt a behavioural data science approach, merging psychological schema theory with sociological intersectionality theory, to evaluate the enduring implications of these biases on female leadership development using mixed methods including machine learning and econometric analysis. Our examination is concentrated on Black female leaders, employing an extensive analysis of leadership rhetoric data spanning 200 years across the aforementioned sectors. We shed light on the continued scarcity of minority female representation in leadership roles, highlighting the role of intersectionality dynamics. Despite Black female leaders frequently embracing higher risks to counter intersectional invisibility compared to their White counterparts, their aspirations are not realized and problems not solved generation after generation, forcing Black female leaders to concentrate on the same issues for dozens and, sometimes, hundreds of years. Our findings suggest that the compound influence of racial and gender biases hinders the advancement of minority female leadership by perpetuating stereotypical behavioral schemas, leading to persistent discriminatory outcomes. We argue for the necessity of organizations to initiate a cultural transformation that fosters positive experiences for future generations of female leaders, recommending a shift in focus from improving outcomes for specific groups to creating an inclusive leadership culture.

Female babies and risk-aversion: Causal evidence from hospital wards.

Using ultrasound scan data from paediatric hospitals, and the exogenous 'shock' of learning the gender of an unborn baby, the paper documents the first causal evidence that offspring gender affects adult risk-aversion. On a standard Holt-Laury criterion, parents of daughters, whether unborn or recently born, become almost twice as risk-averse as parents of sons. The study demonstrates this in longitudinal and cross-sectional data, for fathers and mothers, for babies in the womb and new-born children, and in a West European nation and East European nation. These findings may eventually aid our understanding of risky health behaviors and gender inequalities.

Global network centrality of university rankings.

Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.