Bayesian inference for biological time series

Inferring the parameters of time series models from observed data is essential across many areas of science. Bayesian statistics provides a powerful framework for this purpose, but significant challenges arise when time series models are misspecified due to complexities in the underlying process (e.g., heterogeneity in the modelled population, or when parameter values fluctuate over time), inaccurate numerical approximation of the forward model (e.g., in models involving differential equations), or the presence of non-stationary, non-independent error terms. We introduce a series of models and computational strategies for dealing with misspecification in time series inference problems, with a particular focus on time series problems arising in epidemiology and problems involving ordinary differential equations. The models and inference strategies discussed include: 1. A generalisation of the Poisson renewal model to allow heterogeneous behaviour between local and imported cases, which we use to show that accounting for such heterogeneous behaviour is essential for accurate inference of the time-varying reproduction number (Rt); 2. A Bayesian nonparametric approach to flexibly learn time variation in Rt, which we show is capable of learning accurate and precise estimates of the parameter; 3. Estimates of the gradient and the error in the log-likelihood arising from numerical approximation of differential equations derived from a posteriori error analysis; and 4. A flexible noise process accommodating correlated and heteroscedastic error terms and whose form can be inferred from time series data using kernel functions. We motivate our methodological innovation by a comprehensive examination of the biases in inference that result from insufficiently accurate numerical approximation of differential equations, as well as time series inverse problems and models drawn from epidemiology, hydrology, and cardiac electrophysiology

Creating a learning environment to promote food sustainability issues in primary schools? Staff perceptions of implementing the food for life partnership programme

There is increasing interest in the role that schools can play in promoting education for sustainable development (ESD), and evidence is emerging that schools can be influential in the emerging agenda around the ecological, ethical and social aspects of food, diet and nutrition. With regard to such food sustainability issues, this paper analyses the role of the Food for Life Partnership national programme in supporting garden and farm-based learning activities in 55 primary schools in England, UK. Using a mixed methods approach, the study examined the programme's implementation through staff perceptions and a range of school change indicators. The study found that the programme delivery was associated with widespread institutional reforms. According to staff, implementation of the programme provided a range of opportunities for pupils to learn about food production and sustainability, but addressing these issues was challenging for teachers and raised a number of questions concerned with effective, equitable and on-going implementation. At a pedagogical level, teachers also reflected on conceptually challenging aspects of food sustainability as a topic for primary school education. The study identified ways that ESD programmes could support schools to think about and implement learning opportunities as well as identifying significant barriers related to resourcing such programmes. © 2013 by the authors; licensee MDPI, Basel, Switzerland

A Bayesian nonparametric method for detecting rapid changes in disease transmission

Whether an outbreak of infectious disease is likely to grow or dissipate is determined through the time-varying reproduction number, Rt. Real-time or retrospective identification of changes in Rt following the imposition or relaxation of interventions can thus contribute important evidence about disease transmission dynamics which can inform policymaking. Here, we present a method for estimating shifts in Rt within a renewal model framework. Our method, which we call EpiCluster, is a Bayesian nonparametric model based on the Pitman-Yor process. We assume that Rt is piecewise-constant, and the incidence data and priors determine when or whether Rt should change and how many times it should do so throughout the series. We also introduce a prior which induces sparsity over the number of changepoints. Being Bayesian, our approach yields a measure of uncertainty in Rt and its changepoints. EpiCluster is fast, straightforward to use, and we demonstrate that it provides automated detection of rapid changes in transmission, either in real-time or retrospectively, for synthetic data series where the Rt profile is known. We illustrate the practical utility of our method by fitting it to case data of outbreaks of COVID-19 in Australia and Hong Kong, where it finds changepoints coinciding with the imposition of non-pharmaceutical interventions. Bayesian nonparametric methods, such as ours, allow the volume and complexity of the data to dictate the number of parameters required to approximate the process and should find wide application in epidemiology

Generativity in College Students: Comparing and Explaining the Impact of Mentoring

Preparing college students to be active contributors to the next generation is an important function of higher education. This assumption about generativity forms a cornerstone in this mixed methods study that examined generativity levels among 273 college students at a 4-year public university. MANCOVA results indicated that college students who mentor demonstrated significantly higher generativity than nonmentoring students. Interviews with 9 mentoring students revealed that, although a “seed of generativity” may have already been planted, their mentoring experience served as a “lab” for learning how to be generative. The integrated findings offer important contributions relative to leadership and social responsibility

Generativity in College Students: Comparing and Explaining the Impact of Mentoring

Preparing college students to be active contributors to the next generation is an important function of higher education. This assumption about generativity forms a cornerstone in this mixed methods study that examined generativity levels among 273 college students at a 4-year public university. MANCOVA results indicated that college students who mentor demonstrated significantly higher generativity than nonmentoring students. Interviews with 9 mentoring students revealed that, although a “seed of generativity” may have already been planted, their mentoring experience served as a “lab” for learning how to be generative. The integrated findings offer important contributions relative to leadership and social responsibility

Stepping Up The Pressure: Arousal Can Be Associated With A Reduction In Male Aggression

The attentional myopia model of behavioral control [Mann and Ward, 2007] was tested in an experiment investigating the relationship between physiological arousal and aggression. Drawing on previous work linking arousal and narrowed attentional focus, the model predicts that arousal will lead to behavior that is relatively disinhibited in situations in which promoting pressures to aggress are highly salient. In situations in which inhibitory pressures are more salient, the model predicts behavior that is relatively restrained. In the experiment, 81 male undergraduates delivered noise-blasts against a provoking confederate while experiencing either high or low levels of physiological arousal and, at the same time, being exposed to cues that served either to promote or inhibit aggression. In addition to supporting the predictions of the model, this experiment provided some of the first evidence for enhanced control of aggression under conditions of heightened physiological arousal. Implications for interventions designed to reduce aggression are discussed. Aggr. Behav. 34:584–592, 2008. © 2008 Wiley-Liss, Inc

Ten simple rules for teaching sustainable software engineering

Computational methods and associated software implementations are central to every field of scientific investigation. Modern biological research, particularly within systems biology, has relied heavily on the development of software tools to process and organize increasingly large datasets, simulate complex mechanistic models, provide tools for the analysis and management of data, and visualize and organize outputs. However, developing high-quality research software requires scientists to develop a host of software development skills, and teaching these skills to students is challenging. There has been a growing importance placed on ensuring reproducibility and good development practices in computational research. However, less attention has been devoted to informing the specific teaching strategies which are effective at nurturing in researchers the complex skillset required to produce high-quality software that, increasingly, is required to underpin both academic and industrial biomedical research. Recent articles in the Ten Simple Rules collection have discussed the teaching of foundational computer science and coding techniques to biology students. We advance this discussion by describing the specific steps for effectively teaching the necessary skills scientists need to develop sustainable software packages which are fit for (re-)use in academic research or more widely. Although our advice is likely to be applicable to all students and researchers hoping to improve their software development skills, our guidelines are directed towards an audience of students that have some programming literacy but little formal training in software development or engineering, typical of early doctoral students. These practices are also applicable outside of doctoral training environments, and we believe they should form a key part of postgraduate training schemes more generally in the life sciences.Comment: Prepared for submission to PLOS Computational Biology's 10 Simple Rules collectio

Autocorrelated measurement processes and inference for ordinary differential equation models of biological systems

Ordinary differential equation models are used to describe dynamic processes across biology. To perform likelihood-based parameter inference on these models, it is necessary to specify a statistical process representing the contribution of factors not explicitly included in the mathematical model. For this, independent Gaussian noise is commonly chosen, with its use so widespread that researchers typically provide no explicit justification for this choice. This noise model assumes `random' latent factors affect the system in ephemeral fashion resulting in unsystematic deviation of observables from their modelled counterparts. However, like the deterministically modelled parts of a system, these latent factors can have persistent effects on observables. Here, we use experimental data from dynamical systems drawn from cardiac physiology and electrochemistry to demonstrate that highly persistent differences between observations and modelled quantities can occur. Considering the case when persistent noise arises due only to measurement imperfections, we use the Fisher information matrix to quantify how uncertainty in parameter estimates is artificially reduced when erroneously assuming independent noise. We present a workflow to diagnose persistent noise from model fits and describe how to remodel accounting for correlated errors

Managed moves: schools collaborating for collective gain

Government guidance in the United Kingdom encourages groups of schools to take collective responsibility for supporting and making provision for excluded pupils and those at risk of exclusion. Managed-moves are one way that some schools and authorities are enacting such guidance. This paper presents the results of an evaluation of one such scheme. The scheme, involving seven neighbouring secondary schools, was nearing its first year of completion. The paper draws primarily on interview data with pupils, parents and school staff to describe a number of positive outcomes associated with the scheme and to explore how these were achieved. We found that while some of these could be attributed directly to the managed-move, others arose from the more inclusive ethos and practices of particular schools. The concepts of tailored support, care and commitment emerged as strong themes that underpinned the various practical ways in which some schools in the cluster were able to re-engage 'at-risk' pupils. As managed moves become more widely practiced it will be important to remember that it is how the move proceeds and develops rather than the move itself that will ultimately make the difference for troubled and troublesome pupils

(Re)presenting heritage: laser scanning and 3D visualisations for cultural resilience and community engagement.

Cultural heritage is increasingly being viewed as an economic asset for geographic areas who aim to capitalise in the surge in interest in local history and heritage tourism from members of the public. Digital technologies have developed that facilitate new forms of engagement with heritage and allow local areas to showcase their history, potentially broadening interest to a wider audience, thus acting as a driver for cultural and economic resilience. The research presented in this paper explores this through interdisciplinary research utilising laser scanning and visualisation in combination with social research in Elgin. 3D data capture technologies were used to develop and test 3D data visualisations and protocols through which the urban built heritage can be digitally recorded. The main focus of this paper surrounds the application and perceptions of these technologies. Findings suggest that the primary driver for cultural heritage developments was economic (with an emphasis on tourism) but further benefits and key factors of community engagement, social learning and cultural resilience were also reported. Stakeholder engagement and partnership working, in particular, were identified as critical factors of success. The findings from the community engagement events demonstrate that laser scanning and visualisation provide a novel and engaging mechanism for co-producing heritage assets. There is a high level of public interest in such technologies and users who engaged with these models reported that they gained new perspectives (including spatial and temporal perspectives) on the built heritage of the area