Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.

Depression in Working Adults: Comparing the Costs and Health Outcomes of Working When Ill

Objective Working through a depressive illness can improve mental health but also carries risks and costs from reduced concentration, fatigue, and poor on-the-job performance. However, evidence-based recommendations for managing work attendance decisions, which benefit individuals and employers, are lacking. Therefore, this study has compared the costs and health outcomes of short-term absenteeism versus working while ill (“presenteeism”) amongst employed Australians reporting lifetime major depression. Methods Cohort simulation using state-transition Markov models simulated movement of a hypothetical cohort of workers, reporting lifetime major depression, between health states over one- and five-years according to probabilities derived from a quality epidemiological data source and existing clinical literature. Model outcomes were health service and employment-related costs, and quality-adjusted-life-years (QALYs), captured for absenteeism relative to presenteeism, and stratified by occupation (blue versus white-collar). Results Per employee with depression, absenteeism produced higher mean costs than presenteeism over one- and five-years ($42,573/5-years for absenteeism, $37,791/5-years for presenteeism). However, overlapping confidence intervals rendered differences non-significant. Employment-related costs (lost productive time, job turnover), and antidepressant medication and service use costs of absenteeism and presenteeism were significantly higher for white-collar workers. Health outcomes differed for absenteeism versus presenteeism amongst white-collar workers only. Conclusions Costs and health outcomes for absenteeism and presenteeism were not significantly different; service use costs excepted. Significant variation by occupation type was identified. These findings provide the first occupation-specific cost evidence which can be used by clinicians, employees, and employers to review their management of depression-related work attendance, and may suggest encouraging employees to continue working is warranted.

Public private partnership infrastructure delivery: Benefits and costs for society

This research provides additional knowledge on the benefits and costs to society, in particular of road transport procured through Public-Private Partnership (PPP) arrangements. Currently, the public sector comparator (PSC) and cost-benefit analysis (CBA) used to evaluate and measure the benefits and costs of PPP are limited in their capacity to predict and forecast long-term events. PPP is attractive to governments due to the non-upfront payment, perceived value for money, and risk allocation and transfer to the private investor. However, public sector remains the guarantor, and under-writer of the private investor's loan from financial institutions and other voluntary risks which are unlimited to future compensatory claims. The new knowledge from this research is the introduction of a framework capable of evaluating, and measuring the associated PPP benefits, as well as the costs, effects, and impacts to society which are protracted and sporadic by nature.

Hospital infection control units: Staffing, costs, and priorities

Background: This article describes infection prevention and control professionals’ (ICPs’) staffing levels, patient outcomes, and costs associated with the provision of infection prevention and control services in Australian hospitals. A secondary objective was to determine the priorities for infection control units. Methods: A cross-sectional study design was used. Infection control units in Australian public and private hospitals completed a Web-based anonymous survey. Data collected included details about the respondent; hospital demographics; details and services of the infection control unit; and a description of infection prevention and control-related outputs, patient outcomes, and infection control priorities. Results: Forty-nine surveys were undertaken, accounting for 152 Australian hospitals. The mean number of ICPs was 0.66 per 100 overnight beds (95% confidence interval, 0.55-0.77). Privately funded hospitals have significantly fewer ICPs per 100 overnight beds compared with publicly funded hospitals (P < .01). Staffing costs for nursing staff in infection control units in this study totaled $16,364,392 (mean, $380,566). Infection control units managing smaller hospitals (<270 beds) identified the need for increased access to infectious diseases or microbiology support. Conclusion: This study provides valuable information to support future decisions by funders, hospital administrators, and ICPs on service delivery models for infection prevention and control. Further, it is the first to provide estimates of the resourcing and cost of staffing infection control in hospitals at a national level. Copyright

A theoretical framework for reducing tendering costs in the procurement of infrastructure projects

Purpose of this paper One way in which the tendering process can be further improved is by reviewing and clarifying the high costs that participants face during the course of the tendering phase. The study aims to provide project teams working in construction tender preparation a clear picture of what to expect when tendering for infrastructure projects. Design/methodology/approach Firstly, a review of current literature on tendering in infrastructure projects is conducted to identify the associated costs affecting traditional and PPP procurements as well as the potential measures contributing to tendering cost-reduction. A theoretical framework and its corresponding research hypotheses, which are based on the literature reviewed, are then proposed. An industry-wide questionnaire survey is currently under design to solicit industry practitioners’ views on tendering costs and the associated tendering cost-reduction measures. The data collected in the survey will subject to statistical analysis to test the proposed research hypotheses, which will be reported in a forthcoming paper. Findings and value The direct and indirect costs in public-private procurement have been identified and have been categorised into internal and external costs arising from working on tender submissions. A theoretical framework, mainly composed of five mechanisms of cost reduction, has been proposed and will be tested in a forthcoming industry-wide questionnaire survey. Originality/value of paper The findings are expected to lead to a transparent tendering process in infrastructure procurement, in which there is increased engagement from the private sector as well as an increase in competitive tendering.

Receivers' future liability for costs

In Juniper Property Holdings No.15 Pty Ltd v Caltabiano [2015] QSC 95, Jackson J considered what he described as a 'novel point' as to whether the court had jurisdiction to make a determination of the liability of receivers and managers appointed to the plaintiff to pay any costs orders that may be made in favour of the defendant.

Costs assessor selection: A question of timing, or cost?

In Lessbrook Pty Ltd (in liq) v Whap; Stephen; Bowie; Kepa & Kepa [2014] QCA 63 the Queensland Court of Appeal dealt with significant questions of general application relating to the appointment of assessors to conduct an assessment of costs under the Uniform Civil Procedure Rules 1999 (Qld) (UCPR).

Stochastic analysis of a radial-inflow turbine in the presence of parametric uncertainties

This paper presents an uncertainty quantification study of the performance analysis of the high pressure ratio single stage radial-inflow turbine used in the Sundstrand Power Systems T-100 Multi-purpose Small Power Unit. A deterministic 3D volume-averaged Computational Fluid Dynamics (CFD) solver is coupled with a non-statistical generalized Polynomial Chaos (gPC) representation based on a pseudo-spectral projection method. One of the advantages of this approach is that it does not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic and geometric fields. The stochastic results highlight the importance of the blade thickness and trailing edge tip radius on the total-to-static efficiency of the turbine compared to the angular velocity and trailing edge tip length. From a theoretical point of view, the use of the gPC representation on an arbitrary grid also allows the investigation of the sensitivity of the blade thickness profiles on the turbine efficiency. The gPC approach is also applied to coupled random parameters. The results show that the most influential coupled random variables are trailing edge tip radius coupled with the angular velocity.

Projecting excess emergency department visits and associated costs in Brisbane, Australia, under population growth and climate change scenarios

The direct and indirect health effects of increasingly warmer temperatures are likely to further burden the already overcrowded hospital emergency departments (EDs). Using current trends and estimates in conjunction with future population growth and climate change scenarios, we show that the increased number of hot days in the future can have a considerable impact on EDs, adding to their workload and costs. The excess number of visits in 2030 is projected to range between 98–336 and 42–127 for younger and older groups, respectively. The excess costs in 2012–13 prices are estimated to range between AU$51,000–184,000 (0–64) and AU$27,000–84,000 (65+). By 2060, these estimates will increase to 229–2300 and 145–1188 at a cost of between AU$120,000–1,200,000 and AU$96,000–786,000 for the respective age groups. Improvements in climate change mitigation and adaptation measures are likely to generate synergistic health co-benefits and reduce the impact on frontline health services.

Characterization of alluvial formation by stochastic modelling of paleo-fluvial processes: The concept and method

Modelling fluvial processes is an effective way to reproduce basin evolution and to recreate riverbed morphology. However, due to the complexity of alluvial environments, deterministic modelling of fluvial processes is often impossible. To address the related uncertainties, we derive a stochastic fluvial process model on the basis of the convective Exner equation that uses the statistics (mean and variance) of river velocity as input parameters. These statistics allow for quantifying the uncertainty in riverbed topography, river discharge and position of the river channel. In order to couple the velocity statistics and the fluvial process model, the perturbation method is employed with a non-stationary spectral approach to develop the Exner equation as two separate equations: the first one is the mean equation, which yields the mean sediment thickness, and the second one is the perturbation equation, which yields the variance of sediment thickness. The resulting solutions offer an effective tool to characterize alluvial aquifers resulting from fluvial processes, which allows incorporating the stochasticity of the paleoflow velocity.

Stochastic linear multistep methods for the simulation of chemical kinetics

In this paper, we introduce the Stochastic Adams-Bashforth (SAB) and Stochastic Adams-Moulton (SAM) methods as an extension of the tau-leaping framework to past information. Using the theta-trapezoidal tau-leap method of weak order two as a starting procedure, we show that the k-step SAB method with k >= 3 is order three in the mean and correlation, while a predictor-corrector implementation of the SAM method is weak order three in the mean but only order one in the correlation. These convergence results have been derived analytically for linear problems and successfully tested numerically for both linear and non-linear systems. A series of additional examples have been implemented in order to demonstrate the efficacy of this approach.

Online hidden Markov model parameter estimation and minimax robust quickest change detection in uncertain stochastic processes

Stochastic (or random) processes are inherent to numerous fields of human endeavour including engineering, science, and business and finance. This thesis presents multiple novel methods for quickly detecting and estimating uncertainties in several important classes of stochastic processes. The significance of these novel methods is demonstrated by employing them to detect aircraft manoeuvres in video signals in the important application of autonomous mid-air collision avoidance.

Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method

Background Biochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need for numerical methods that are both fast and accurate. The Bulirsch-Stoer method is an established method for solving ordinary differential equations that possesses both of these qualities. Results In this paper, we present the Stochastic Bulirsch-Stoer method, a new numerical method for simulating discrete chemical reaction systems, inspired by its deterministic counterpart. It is able to achieve an excellent efficiency due to the fact that it is based on an approach with high deterministic order, allowing for larger stepsizes and leading to fast simulations. We compare it to the Euler τ-leap, as well as two more recent τ-leap methods, on a number of example problems, and find that as well as being very accurate, our method is the most robust, in terms of efficiency, of all the methods considered in this paper. The problems it is most suited for are those with increased populations that would be too slow to simulate using Gillespie’s stochastic simulation algorithm. For such problems, it is likely to achieve higher weak order in the moments. Conclusions The Stochastic Bulirsch-Stoer method is a novel stochastic solver that can be used for fast and accurate simulations. Crucially, compared to other similar methods, it better retains its high accuracy when the timesteps are increased. Thus the Stochastic Bulirsch-Stoer method is both computationally efficient and robust. These are key properties for any stochastic numerical method, as they must typically run many thousands of simulations.

Stochastic dynamics of interacting haematopoietic stem cell niche lineages

Since we still know very little about stem cells in their natural environment, it is useful to explore their dynamics through modelling and simulation, as well as experimentally. Most models of stem cell systems are based on deterministic differential equations that ignore the natural heterogeneity of stem cell populations. This is not appropriate at the level of individual cells and niches, when randomness is more likely to affect dynamics. In this paper, we introduce a fast stochastic method for simulating a metapopulation of stem cell niche lineages, that is, many sub-populations that together form a heterogeneous metapopulation, over time. By selecting the common limiting timestep, our method ensures that the entire metapopulation is simulated synchronously. This is important, as it allows us to introduce interactions between separate niche lineages, which would otherwise be impossible. We expand our method to enable the coupling of many lineages into niche groups, where differentiated cells are pooled within each niche group. Using this method, we explore the dynamics of the haematopoietic system from a demand control system perspective. We find that coupling together niche lineages allows the organism to regulate blood cell numbers as closely as possible to the homeostatic optimum. Furthermore, coupled lineages respond better than uncoupled ones to random perturbations, here the loss of some myeloid cells. This could imply that it is advantageous for an organism to connect together its niche lineages into groups. Our results suggest that a potential fruitful empirical direction will be to understand how stem cell descendants communicate with the niche and how cancer may arise as a result of a failure of such communication.

A stochastic exponential Euler scheme for simulation of stiff biochemical reaction systems

In order to simulate stiff biochemical reaction systems, an explicit exponential Euler scheme is derived for multidimensional, non-commutative stochastic differential equations with a semilinear drift term. The scheme is of strong order one half and A-stable in mean square. The combination with this and the projection method shows good performance in numerical experiments dealing with an alternative formulation of the chemical Langevin equation for a human ether a-go-go related gene ion channel mode