New methodologies for the estimation of population vulnerability to diseases: a case study of Lassa fever and Ebola in Nigeria and Sierra Leone.

Public health practitioners require measures to evaluate how vulnerable populations are to diseases, especially for zoonoses (i.e. diseases transmitted from animals to humans) given their pandemic potential. These measures would be valuable to support strategic and operational decision making and allocation of resources. Although vulnerability is well defined for natural hazards, for public health threats the concept remains undetermined. Here, we develop new methodologies to: (i) quantify the impact of zoonotic diseases and the capacity of countries to cope with these diseases, and (ii) combine these two measures (impact and capacity) into one overall vulnerability indicator. The adaptive capacity is calculated from estimations of disease mortality, although the method can be adapted for diseases with no or low mortality but high morbidity. As an example, we focused on the vulnerability of Nigeria and Sierra Leone to Lassa Fever and Ebola. We develop a simple analytical form that can be used to estimate vulnerability scores for different spatial units of interest, e.g. countries or regions. We show how some populations can be highly vulnerable despite low impact threats. We finally outline future research to more comprehensively inform vulnerability with the incorporation of relevant factors depicting local heterogeneities (e.g. bio-physical and socio-economic factors). This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.FRSF Pump Prime Gran

The Effect of Liquid Viscosity on the Rise Velocity of Taylor Bubbles in Small Diameter Bubble Column

The rise velocity of Taylor bubbles in small diameter bubble column was measured via cross-correlation between two planes of time-averaged void fraction data obtained from the electrical capacitance tomography (ECT). This was subsequently compared with the rise velocity obtained from the high-speed camera, manual time series analysis and likewise empirical models. The inertia, viscous and gravitational forces were identified as forces, which could influence the rise velocity. Fluid flow analysis was carried out using slug Reynolds number, Froude number and inverse dimensionless viscosity, which are important dimensionless parameters influencing the rise velocity of Taylor bubbles in different liquid viscosities, with the parameters being functions of the fluid properties and column diameter. It was found that the Froude number decreases with an increase in viscosity with a variation in flow as superficial gas velocity increases with reduction in rise velocity. A dominant effect of viscous and gravitational forces over inertia forces was obtained, which showed an agreement with Stokes law, where drag force is directly proportional to viscosity. Hence, the drag force increases as viscosity increases (5 < 100 < 1000 < 5000 mPa s), leading to a decrease in the rise velocity of Taylor bubbles. It was concluded that the rise velocity of Taylor bubbles decreases with an increase in liquid viscosity and, on the other hand, increases with an increase in superficial gas velocity

Systematic review of interventions for treating or preventing antipsychotic-induced tardive dyskinesia

Background: Antipsychotic medication can cause tardive dyskinesia (TD) – late-onset, involuntary, repetitive movements, often involving the face and tongue. TD occurs in > 20% of adults taking antipsychotic medication (first-generation antipsychotics for > 3 months), with this proportion increasing by 5% per year among those who continue to use these drugs. The incidence of TD among those taking newer antipsychotics is not different from the rate in people who have used older-generation drugs in moderate doses. Studies of TD have previously been found to be limited, with no treatment approach shown to be effective. Objectives: To summarise the clinical effectiveness and safety of treatments for TD by updating past Cochrane reviews with new evidence and improved methods; to undertake public consultation to gauge the importance of the topic for people living with TD/the risk of TD; and to make available all data from relevant trials. Data sources: All relevant randomised controlled trials (RCTs) and observational studies. Review methods: Cochrane review methods, network meta-analysis (NMA). Design: Systematic reviews, patient and public involvement consultation and NMA. Setting: Any setting, inpatient or outpatient. Participants: For systematic reviews, adults with TD who have been taking a stable antipsychotic drug dose for > 3 months. Interventions: Any, with emphasis on those relevant to UK NHS practice. Main outcome measures: Any measure of TD, global assessments and adverse effects/events. Results: We included 112 studies (nine Cochrane reviews). Overall, risk of bias showed little sign of improvement over two decades. Taking the outcome of ‘TD symptoms improved to a clinically important extent’, we identified two trials investigating reduction of antipsychotic dose [n = 17, risk ratio (RR) 0.42, 95% confidence interval (CI) 0.17 to 1.04; very low quality]. Switching was investigated twice in trials that could not be combined (switching to risperidone vs. antipsychotic withdrawal: one RCT, n = 42, RR 0.45, 95% CI 0.23 to 0.89; low quality; switching to quetiapine vs. haloperidol: one RCT, n = 45, RR 0.80, 95% CI 0.52 to 1.22; low quality). In addition to RCTs, six observational studies compared antipsychotic discontinuation with decreased or increased dosage, and there was no clear evidence that any of these strategies had a beneficial effect on TD symptoms (very low-quality evidence). We evaluated the addition to standard antipsychotic care of several treatments, but not anticholinergic treatments, for which we identified no trials. We found no clear effect of the addition of either benzodiazepines (two RCTs, n = 32, RR 1.12, 95% CI 0.6 to 2.09; very low quality) or vitamin E (six RCTs, n = 264, RR 0.95, 95% CI 0.89 to 1.01; low quality). Buspirone as an adjunctive treatment did have some effect in one small study (n = 42, RR 0.53, 95% CI 0.33 to 0.84; low quality), as did hypnosis and relaxation (one RCT, n = 15, RR 0.45, 95% CI 0.21 to 0.94; very low quality). We identified no studies focusing on TD in people with dementia. The NMA model found indirect estimates to be imprecise and failed to produce useful summaries on relative effects of interventions or interpretable results for decision-making. Consultation with people with/at risk of TD highlighted that management of TD remains a concern, and found that people are deeply disappointed at the length of time it has taken researchers to address the issue. Limitations: Most studies remain small and poorly reported. Conclusions: Clinicians, policy-makers and people with/at risk of TD are little better informed than they were decades ago. Underpowered trials of limited quality repeatedly fail to provide answers. Future work: TD reviews have data from current trials extracted, tabulated and traceable to source. The NMA highlights one context in which support for this technique is ill advised. All relevant trials, even if not primarily addressing the issue of TD, should report appropriate binary outcomes on groups of people with this problem. Randomised trials of treatments for people with established TD are indicated. These should be large (> 800 participants), necessitating accrual through accurate local/national registers, including an intervention with acceptable treatments and recording outcomes used in clinical practice. Study registration: This study is registered as PROSPERO CRD4201502045. Funding: The National Institute for Health Research Health Technology Assessment programme

Meta-model assisted calibration of computational fluid dynamics simulation models.

Computational fluid dynamics (CFD) is a computer-based analysis of the dynamics of fluid flow, and it is widely used in chemical and process engineering applications. However, computation usually becomes a herculean task when calibration of the CFD models with experimental data or sensitivity analysis of the output relative to the inputs is required. This is due to the simulation process being highly computationally intensive, often requiring a large number of simulation runs, with a single simulation run taking hours or days to be completed. Hence, in this research project, the kriging meta-modelling method was coupled with expected improvement (EI) global optimisation approach to address the CFD model calibration challenge. In addition, a kriging meta-model based sensitivity analysis technique was implemented to study the model parameter input-output relationship. A novel EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution, where existing normal distribution-based EI measures are not applicable. This novel EI measure suggested the values of CFD model parameters to simulate with, hence minimising SSE and improving the match between simulation and experiments. To test the proposed methodology, a non-CFD numerical simulation case of the semi-batch reactor was considered as a case study which confirmed a saving in computational time, and an improvement of the simulation model with the actual plant data. The usefulness of the developed method has been subsequently demonstrated through a CFD case study of a single-phase flow in both a straight type and convergent-divergent type annular jet pump, where both a single turbulent model parameter, C_μ and two turbulent model parameters, C_μ and C_2ε where considered for calibration. Sensitivity analysis was subsequently based on C_μ as the input parameter. In calibration using both single and two model parameters, a significant improvement in the agreement with experimental data was obtained. The novel method gave a significant reduction in simulation computational time as compared to traditional CFD. A new correlation was proposed relating C_μ to the flow ratio, which could serve as a guide for future simulations. The meta-model based calibration aids exploration of different parameter combinations which would have been computationally challenging using CFD. In addition, computational time was significantly reduced with kriging-assisted sensitivity analysis studies which explored effect of different C_μ values on the output, the pressure coefficient. The numerical simulation case of the semi-batch reactor was also used as a basis of comparison between the previous EI measure and the newly proposed EI measure, which overall revealed that the latter gave a significant improvement at fewer number of simulation runs as compared to the former. The research studies carried out has hence been able to propose and successfully demonstrate the use of a novel methodology for faster calibration and sensitivity analysis studies of computational fluid dynamics simulations. This is essential in the design, analysis and optimisation of chemical and process engineering systems

Meta-model assisted calibration of computational fluid dynamics simulation models.

Computational fluid dynamics (CFD) is a computer-based analysis of the dynamics of fluid flow, and it is widely used in chemical and process engineering applications. However, computation usually becomes a herculean task when calibration of the CFD models with experimental data or sensitivity analysis of the output relative to the inputs is required. This is due to the simulation process being highly computationally intensive, often requiring a large number of simulation runs, with a single simulation run taking hours or days to be completed. Hence, in this research project, the kriging meta-modelling method was coupled with expected improvement (EI) global optimisation approach to address the CFD model calibration challenge. In addition, a kriging meta-model based sensitivity analysis technique was implemented to study the model parameter input-output relationship. A novel EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution, where existing normal distribution-based EI measures are not applicable. This novel EI measure suggested the values of CFD model parameters to simulate with, hence minimising SSE and improving the match between simulation and experiments. To test the proposed methodology, a non-CFD numerical simulation case of the semi-batch reactor was considered as a case study which confirmed a saving in computational time, and an improvement of the simulation model with the actual plant data. The usefulness of the developed method has been subsequently demonstrated through a CFD case study of a single-phase flow in both a straight type and convergent-divergent type annular jet pump, where both a single turbulent model parameter, C_μ and two turbulent model parameters, C_μ and C_2ε where considered for calibration. Sensitivity analysis was subsequently based on C_μ as the input parameter. In calibration using both single and two model parameters, a significant improvement in the agreement with experimental data was obtained. The novel method gave a significant reduction in simulation computational time as compared to traditional CFD. A new correlation was proposed relating C_μ to the flow ratio, which could serve as a guide for future simulations. The meta-model based calibration aids exploration of different parameter combinations which would have been computationally challenging using CFD. In addition, computational time was significantly reduced with kriging-assisted sensitivity analysis studies which explored effect of different C_μ values on the output, the pressure coefficient. The numerical simulation case of the semi-batch reactor was also used as a basis of comparison between the previous EI measure and the newly proposed EI measure, which overall revealed that the latter gave a significant improvement at fewer number of simulation runs as compared to the former. The research studies carried out has hence been able to propose and successfully demonstrate the use of a novel methodology for faster calibration and sensitivity analysis studies of computational fluid dynamics simulations. This is essential in the design, analysis and optimisation of chemical and process engineering systems

Kriging meta-model assisted calibration of computational fluid dynamics models

Computational fluid dynamics (CFD) is a simulation technique widely used in chemical and process engineering applications. However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. In this research, the kriging meta-modelling approach (also termed Gaussian process) was coupled with expected improvement (EI) to address this challenge. A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. The new EI measure is to suggest the CFD model parameter to simulate with, hence minimising SSE and improving match between simulation and experiments. The usefulness of the developed method was demonstrated through a case study of a single-phase flow in both a straight-type and a convergent-divergent-type annular jet pump, where a single model parameter was calibrated with experimental data

Kriging meta-model assisted calibration of computational fluid dynamics models

Computational fluid dynamics (CFD) is a simulation technique widely used in chemical and process engineering applications. However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. In this research, the kriging meta-modelling approach (also termed Gaussian process) was coupled with expected improvement (EI) to address this challenge. A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. The new EI measure is to suggest the CFD model parameter to simulate with, hence minimising SSE and improving match between simulation and experiments. The usefulness of the developed method was demonstrated through a case study of a single-phase flow in both a straight-type and a convergent-divergent-type annular jet pump, where a single model parameter was calibrated with experimental data

Experimental investigation of liquid viscosity's effect on the flow behaviour and void fraction in a small diameter bubble column: How much do we know?

The viscosities of heavy oils and bitumen are significantly above those of water and light crude oil and are found in the oil and gas, polymer, metallurgical, and process industries. Unfortunately, little research attention has been given to the void fraction, and the behaviours of such flows as bubbles rise over them in bubble columns. How the liquid viscosity influences the behaviour of these flows and void fraction were examined using the electrical capacitance tomography (ECT), level swell technique and visual technique with a high-speed camera. It was found that: an excellent agreement was achieved within ±10% for comparison between the obtained void fraction using both techniques for all the liquid viscosities considered. In contradiction to previous findings, an increase in liquid viscosity provokes a corresponding increase in the void fraction, yet the influence is seen to reverse for gas superficial velocities beyond 0.25 m/s. In concordance with Mori et al. (1999)'s observations, a liquid viscosity increment brings about a corresponding decrease in the bubble frequency. The Taylor bubble's rise velocity was heavily dependent on the liquid viscosity range, whereby increases in liquid viscosity bring about a rise in the flow distribution coefficient. The bubble drift velocity, on the contrary, reduces with increasing liquid viscosity

Investigating the Behaviour of Air–Water Upward and Downward Flows: Are You Seeing What I am Seeing?

Understanding the behaviour of gas–liquid flows in upward and downward pipe configurations in chemical, petroleum, and nuclear industries is vital when optimal design, operation, production, and safety are of paramount concern. Unfortunately, the information concerning the behaviour of such flows in large pipe diameters is rare. This article aims to bridge that gap by reporting air–water upward and downward flows in 127 mm internal diameter pipes using advanced conductance ring probes located at two measurement locations. The liquid and gas flow rates are 0.021 to 0.33 m/s and 3.52 to 16.1 m/s, correspondingly, covering churn and annular flows. To achieve the desired objectives, several parameters, probability density function (PDF), power spectral density (PSD), Slippage Number (SN), drift velocity (Ugd), and distribution coefficient (C0) were employed. The flow regimes encountered in the two pipe configurations were distinguished employing a flow regime map available in the literature and statistical analysis. The obtained results were supported by visual inspection. The comparison between the present study against the reported studies reveals the same tendency for the measured experimental data. The Root Mean Square Error (RMSE) method within 4% was utilized in recommending the best void fraction prediction correlation for the downward and upward flows

Experimental investigation of the effect of liquid viscosity on slug flow in small diameter bubble column

The effect of liquid viscosity on slug flow in a 50 mm diameter bubble column was investigated experimentally using air-silicone oil as operating fluid with silicone oil of viscosities 5, 100, 1000 and 5000 mPa.s. Data was collected using Electrical Capacitance Tomography (ECT), a non-intrusive advanced instrumentation measuring technique and the high Speed Video Camera, through which the slug parameters such as length of Taylor bubbles and liquid slug, void fraction in Taylor bubbles and liquid slug, slug frequency, film thickness and pressure gradient in the slug, were measured and analyzed. The analysis was done using the void fraction time series, probability density function and power spectral density plots. Superficial gas velocities of 0.02≤Ugs≤0.361 m/s were used in the experiment. It was also observed that as viscosity increases, slug frequency, structure velocity, length of liquid slug, void fraction in liquid slug and void fraction in Taylor bubbles decreases; while the length of Taylor bubble, film thickness and pressure gradient in the slug increases