Classroom of the apes: is teaching monkey business?

Between 1973 and 2000, social scientists conducted one of the most significant, innovative and challenging programmes in the history of linguistic and educational research. ‘Project Nim’ investigated both the interaction between nature and nurture and attempted to bring human level gestural communication to a chimpanzee called ‘Nim’. The study offered some of the most important insights into our understanding of language and cognition and what it means to be human, and represents a landmark in our thinking about teaching and learning, and education itself. Here, the authors contend that essential lessons from the experiment have been overlooked and risk being forgotten. This article revisits the study, exploring some of the issues it raises, and attempts to site what we learnt from Nim in the context of modern teaching practice. Through this re‐examination we intend to provoke thinking not only about ‘Project Nim’, but perhaps also about other lost lessons in education. We conclude by reflecting on the importance of remembering the lessons we learnt when trying to teach Nim, and how they can enhance our practice as teachers for all learners

Modelling the impact of social mixing and behaviour on infectious disease transmission: application to SARS-CoV-2

In regard to infectious diseases socioeconomic determinants are strongly associated with differential exposure and susceptibility however they are seldom accounted for by standard compartmental infectious disease models. These associations are explored here with a novel compartmental infectious disease model which, stratified by deprivation and age, accounts for population-level behaviour including social mixing patterns. As an exemplar using a fully Bayesian approach our model is fitted, in real-time if required, to the UKHSA COVID-19 community testing case data from England. Metrics including reproduction number and forecasts of daily case incidence are estimated from the posterior samples. From this UKHSA dataset it is observed that during the initial period of the pandemic the most deprived groups reported the most cases however this trend reversed after the summer of 2021. Forward simulation experiments based on the fitted model demonstrate that this reversal can be accounted for by differential changes in population level behaviours including social mixing and testing behaviour, but it is not explained by the depletion of susceptible individuals. In future epidemics, with a focus on socioeconomic factors the approach outlined here provides the possibility of identifying those groups most at risk with a view to helping policy-makers better target their support.Comment: Main article: 25 pages, 6 figures. Appendix 2 pages, 1 figure. Supplementary Material: 15 pages, 14 figures. Version 2 - minor updates: fixed typos, updated mathematical notation and small quantity of descriptive text added. Version 3 - minor update: made colour coding consistent across all time series figure

A new method for obtaining the star formation law in galaxies

We present a new observational method to evaluate the star formation law as formulated by Schmidt: the power-law expression assumed to relate the rate of star formation in a volume of space to the local total gas volume density. Volume densities in the clouds surrounding an OB association are determined with a simple model which considers atomic hydrogen as a photodissociation product on cloud surfaces. The photodissociating flux incident on the cloud is computed from the far-UV luminosity of the OB association and the geometry. We have applied this "PDR Method" to a sample of star-forming regions in M33 using VLA 21-cm data for the HI and GALEX imagery in the far-UV. It provides an estimate of the total volume density of hydrogen (atomic + molecular) in the gas clouds surrounding the young star cluster. A logarithmic graph of the cluster UV luminosity versus the surrounding gas density is a direct measure of the star formation law. However, this plot is severely affected by observational selection, rendering large areas of the diagram inaccessible to the data. An ordinary least-squares regression fit therefore gives a strongly biased result. Its slope primarily reflects the boundary defined when the 21-cm line becomes optically thick, no longer reliably measuring the HI column density. We use a maximum-likelihood statistical approach which can deal with truncated and skewed data, taking into account the large uncertainties in the derived total gas densities. The exponent we obtain for the Schmidt law in M33 is 1.4 \pm 0.2.Comment: Accepted for publication in Ap

Social Contact Patterns During the COVID-19 Pandemic : Implications for Public Health and Hospital Infection Control

Social contact patterns are an important driver of respiratory epidemics. Human behaviour is likely to change during an outbreak; this may be due to imposed control measures or as a personal-risk judgement. Following the emergence of the COVID19 pandemic in 2020, numerous interventions were implemented in the UK to curb community transmission of SARS-CoV-2 by reducing social contact. It was therefore important to quantify contact patterns during the pandemic to understand how social networks had changed and identify key routes of transmission. Chapters 2 and 3 of this thesis outline two cross-sectional population-based surveys which quantified and characterised social contact patterns in different populations during the pandemic. Social contact patterns are quantified at the national scale in Chapter 2, following the relaxation of pandemic restrictions in July 2020. We investigated the association of demographic characteristics and behaviour, such as shielding and selfisolating, with non-household mixing. Chapter 3 describes an occupational study conducted in December 2020. In this study, we quantified social contact patterns of home delivery drivers at delivery depots and with customers, and identified the protective measures that they adopted during the pandemic. In Chapter 4 we outline a statistical framework to identify the role of hospital structure and staff interactions in nosocomial transmission of SARS-CoV-2 during the first wave of the pandemic. We present an efficient method to infer epidemiological event times and quantify relative routes of transmission, while accounting for the intricacies of the staff-patient contact network. This thesis demonstrates how social contact data can provide insight into adherence to non-pharmaceutical interventions, identify subgroups of the population which may be at a greater risk of infection, and quantify relative routes of transmission in high-risk settings. We identify the wider implications of social contact patterns during the COVID-19 pandemic for public health and hospital infection control

Social mixing patterns in the UK following the relaxation of COVID-19 pandemic restrictions, July to August 2020: a cross-sectional online survey

Objectives: To quantify and characterize non-household contact and to identify the effect of shielding and isolating on contact patterns. Design: Cross-sectional study. Setting and participants: Anyone living in the UK was eligible to take part in the study. We recorded 5,143 responses to the online questionnaire between 28 July and 14 August 2020. Outcome measures: Our primary outcome was the daily non-household contact rate of participants. Secondary outcomes were propensity to leave home over a 7 day period, whether contacts had occurred indoors or outdoors locations visited, furthest distance travelled from home, ability to socially distance, and membership of support bubble. Results: The mean rate of non-household contacts per person was 2.9 d-1. Participants attending a workplace (adjusted incidence rate ratio (aIRR) 3.33, 95%CI 3.02 to 3.66), self-employed (aIRR 1.63, 95%CI 1.43 to 1.87) or working in healthcare (aIRR 5.10, 95%CI 4.29 to 6.10) reported significantly higher non-household contact rates than those working from home. Participants self-isolating as a precaution or following Test and Trace instructions had a lower non-household contact rate than those not self-isolating (aIRR 0.58, 95%CI 0.43 to 0.79). We found limited evidence that those shielding had reduced non-household contacts compared to non-shielders. Conclusion: The daily rate of non-household interactions remained lower than pre-pandemic levels measured by other studies, suggesting continued adherence to social distancing guidelines. Individuals attending a workplace in-person or employed as healthcare professionals were less likely to maintain social distance and had a higher non-household contact rate, possibly increasing their infection risk. Shielding and self-isolating individuals required greater support to enable them to follow the government guidelines and reduce non-household contact and therefore their risk of infection

Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak

In the event of a disease outbreak emergency, such as COVID-19, the ability to construct detailed stochastic models of infection spread is key to determining crucial policy-relevant metrics such as the reproduction number, true prevalence of infection, and the contribution of population characteristics to transmission. In particular, the interaction between space and human mobility is key to prioritising outbreak control resources to appropriate areas of the country. Model-based epidemiological intelligence must therefore be provided in a timely fashion so that resources can be adapted to a changing disease landscape quickly. The utility of these models is reliant on fast and accurate parameter inference, with the ability to account for large amount of censored data to ensure estimation is unbiased. Yet methods to fit detailed spatial epidemic models to national-level population sizes currently do not exist due to the difficulty of marginalising over the censored data. In this paper we develop a Bayesian data-augmentation method which operates on a stochastic spatial metapopulation SEIR state-transition model, using model-constrained Metropolis-Hastings samplers to improve the efficiency of an MCMC algorithm. Coupling this method with state-of-the-art GPU acceleration enabled us to provide nightly analyses of the UK COVID-19 outbreak, with timely information made available for disease nowcasting and forecasting purposes

Vitamin D deficiency is endemic in neurosurgical patients and is associated with a longer length of inpatient stay

Introduction Vitamin D deficiency is common in spinal surgery and critical care. Hypovitaminosis D may impact on outcomes in cranial neurosurgical care and play roles in underlying disease processes. Methods A prospective observational cohort study was performed. All emergency cranial neurosurgical ward admissions from 1st January to 10th May 2017 were screened for inclusion (n = 406). Patients already receiving vitamin D supplementation, spinal patients and elective admissions were excluded. Admission vitamin D levels were checked for all remaining patients (n = 95). Patients with vitamin D <30 nmol/L were defined as “deficient” and those 30‐50 nmol/L as “inadequate.” All patients with levels <50 nmol/L were replaced, as per local guidelines. Descriptive analyses of the cohorts were undertaken, with multivariate regression used to assess the effect of vitamin D on length of stay, inpatient morbidity and mortality. Results The median age of participants was 61 years (n = 95; 57% male, 43% female). The median vitamin D level was 23 nmol/L (deficient). 84% (n = 80) of patients had low vitamin D levels, with 61% (n = 58) classed as deficient (<30 nmol/L). Vitamin D deficiency rates were similar in those aged below 65 years (86%; n = 38/44) and those above 65 years (82%; n = 42/51). Deficient vitamin D level was associated with longer hospital stay (P = .03), and this relationship persisted after adjusting for potential confounders such as age, sex and preadmission Charlson co‐morbidity index. No statistically significant association was seen with vitamin D status and inpatient morbidity or mortality. Conclusions Vitamin D deficiency is common in cranial neurosurgical patients, even in predefined low‐risk groups (age <65). Lower vitamin D level was associated with longer length of stay. This study supports the need for: (a) further investigation into the roles of vitamin D in neurosurgical pathologies and management and (b) an appropriately powered, randomised investigation into the impact of vitamin D status upon neurosurgical diagnoses and complications