Standard Sensitivity Analysis Research Articles

Causal effects of plasma proteome on intervertebral disc degeneration: a comprehensive mendelian randomization study.

Intervertebral disc degeneration (IVDD) considerably impacts global disability and quality of life. Although potential links between plasma proteins and IVDD exist, their causal correlation remains undefined. This study explored the causal links between plasma proteins and IVDD employing genome-wide association study data. For this observational study, summary statistics for plasma proteins were derived from an Icelandic population, paralleled with genome-wide data on IVDD obtained from the FinnGen consortium. Using two-sample Mendelian randomization, we assessed the causal relationship between 4,907 plasma proteins and IVDD. Standard sensitivity analyses encompassing heterogeneity, pleiotropy, and leave-one-out cross-validation tests confirmed the stability and robustness of the findings. Subsequently, through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein interaction analyses, we delved into the functional interrelation among identified plasma proteins. A causal association with IVDD was identified for 47 plasma proteins; myoneurin, intersectin 1, and eukaryotic translation initiation factor 4 gamma 3 maintained significant correlations post multiple correction tests (P < 1.02 × 10- 5), influencing IVDD development positively. GO/KEGG pathway analyses confirmed that multiple pathways may be involved in IVDD development. The study underscores the causal correlation between plasma protein levels and IVDD risk. These identified proteins could emerge as unique biomarkers for IVDD, contributing to its predictive measures. The findings further our understanding of IVDD pathomechanisms and prospective therapeutic targets.

Methods to Quantify the Importance of Parameters for Model Updating and Distributional Adaptation.

Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA). Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study. In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these. These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters. It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis.

Small modular nuclear reactors: A pathway to cost savings and environmental progress in SAGD operations

Small Modular Nuclear Reactors (SMRs) offer a promising option for environmentally friendly bitumen recovery operations. The extraction of oil sands in Western Canada is vital for the economy, but traditional methods like Steam-Assisted Gravity Drainage (SAGD) contribute significantly to greenhouse gas (GHG) emissions. In SAGD, steam generation, primarily fueled by natural gas combustion, is the main source of emissions. Given the imperative to reduce carbon intensity, less emissive recovery methods are needed to sustain production and economic viability in Canadian oil sands. Currently, there are limited non-carbon alternatives for steam generation in oil sands applications. The utilization of SMRs for steam generation presents a clean alternative. In this study, we examine the feasibility of employing SMRs in in-situ oil sands recovery operations. Through standardized economic metrics and sensitivity analysis, we demonstrate that integrating SMRs into SAGD operations eliminates GHG emissions significantly and can potentially outperform conventional natural gas-based steam generation in terms of net present value, under certain operational scenarios. Hence, our findings indicate that SMRs hold promise for decarbonizing oil sands recovery processes.

Life cycle assessment for evaluation of novel solvents and technologies: A case study of flavonoids extraction from Ginkgo biloba leaves

Innovative solvents such as deep eutectic solvents (DESs) and process intensification technologies assisted by ultrasound have been demonstrated to be promising pathways for enhancing solid-liquid extraction. Nevertheless, quantitative and systematic knowledge of their environmental impact is still limited. In this work, a case study of flavonoids extraction from Ginkgo biloba leaves was evaluated by using life cycle assessment (LCA) for comparison of three extraction scenarios. The first used DES as extractant (DESE), and the other two adopted ethanol, including heat reflux extraction (HRE), and ultrasound-assisted extraction (UAE). Among eight key midpoints investigated, all these from UAE were 10.0 %–80.0 % lower than from DESE and HRE except water consumption. The UAE was the eco-friendliest option due to its higher extraction yield, shorter duration and lower solvent consumption. The DESE exhibited the lowest water consumption, the highest freshwater ecotoxicity and human carcinogenic toxicity, while HRE had the highest impacts for the other 6 midpoints. Moreover, solvent production was the key contributor for all the categories. The standardized sensitivity analysis showed that the overall environmental footprint can be further decreased by 15.4 % for DESE pathways via substituting choline chloride/glycerine with choline chloride/ethylene glycol. Furthermore, all pathways using DESs had higher standardized impacts than those employing ethanol from sugarcane or wood. Replacing ethanol from maize with other feedstocks can significantly lessen the overall impacts, among which the UAE using ethanol from sugarcane demonstrated the least environmental impacts. The promotion of DESs as “green and sustainable” alternative to traditional solvents requires careful consideration.

Sensitivity analysis in the wavelet domain: a comparison study

Sensitivity analysis plays a pivotal role for the development and calibration of hydrological models, since they are often affected by equifinality. Despite a lot of effort has been placed for the development of effective sensitivity analysis methods, hydrological models remain over parametrized. We take advantage of the evidence that hydrological processes can be described as the superposition of effects occurring at different temporal scales (e.g., seasonal precipitation patterns, seasonal and daily snow and glacier melt, seasonal, daily and sub-daily water management operations) to develop a new framework to perform sensitivity analysis. We apply discrete and continuous wavelet transforms to disentangle hydrological signals occurring at different temporal scales and we take advantage of the different information stored at different temporal scales of the wavelet spectrum to perform a scale-dependent sensitivity analysis. This approach aims to increase the number of identifiable model parameters in comparison to standard sensitivity analysis performed in the time domain. As an exemplary problem, we apply the methodology to synthetic data describing surface water-groundwater interaction in rivers affected by hydropeaking (i.e., sudden fluctuations in the river stage due to hydropower production). The method could be applied also to other models displaying the superposition of processes with different intensities at different temporal scales such as ocean tide propagation in aquifers as well as snow and glacier melt models. The results indicate that considering multiple temporal scales allows us to increase the number of parameters that can be identified and hence calibrated with only a little increase in the computational effort.

The causal effect of educational attainment on stress urinary incontinence: a two-sample mendelian randomization study

BackgroundStress urinary incontinence (SUI) is characterized by involuntary urine leakage in response to increased abdominal pressure, such as coughing, laughing, or sneezing. It significantly affects women’s quality of life and imposes a substantial disease burden. While pregnancy and childbirth have been previously identified as risk factors for SUI, educational attainment may also play a role. Therefore, this paper investigates the causal relationship between educational attainment and SUI using two-sample Mendelian randomization (TSMR) analysis, years of schooling (YOS), and college or university degree (CUD) as proxies.MethodsSummary statistics of YOS, CUD, and SUI were obtained from genome-wide association studies (GWAS), and TSMR analysis was applied to explore potential causal relationships between them. Causal effects were mainly estimated using the standard inverse variance weighting (IVW) method, and complementary and sensitivity analyses were also performed using multiple methods.ResultsThe results indicate that both YOS (OR = 0.994, 95% CI: 0.992–0.996; P = 7.764E-10) and CUD (OR = 0.987, 95% CI: 0.983–0.991; P = 1.217E-09) may have a negative causal effect on SUI.ConclusionsImproving educational attainment may go some way towards reducing the risk of SUI. Therefore, it is important to increase efforts to improve the imbalance in educational development and safeguard women’s health.

Extending system dynamics modeling using simulation decomposition to improve the urban planning process

Urban planning often involves decision-making under highly uncertain circumstances. System dynamics and multi-agent modeling frameworks are commonly employed to model the social phenomena in this type of urban planning. However, because the outputs from these approaches are regularly characterized as a function of time, the majority of studies in this modeling domain lack appropriate sensitivity analysis. Consequently, important insights into model behavior are frequently overlooked. Monte Carlo simulation has been used to incorporate uncertain features in urban planning with the outputs displayed as probability distributions. Recently simulation decomposition (SimDec) has been used to enhance the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SimDec maps each output value of a Monte Carlo simulation on to the multivariable groups of inputs or scenarios from which it originated. By visually projecting the subdivided scenarios onto the overall output, SimDec can reveal previously unidentified influences between the various combinations of inputs on to the outputs. SimDec can be generalized to any Monte Carlo method with insignificant computational overhead and is, therefore, extendable to any simulated urban planning analysis. This study demonstrates the efficacy of adapting SimDec for the sensitivity analysis of urban dynamics modeling on a paradigmatic simplified version of Forrester's Urban Dynamics- URBAN1 model. SimDec reveals complexities in model behavior that are not, and can not be, captured by standard sensitivity analysis methods and highlights, in particular, the intricate joint effect of immigration and outmigration on system development.

A Bootstrapped Modularised method of Global Sensitivity Analysis applied to Probabilistic Seismic Hazard Assessment

Probabilistic Seismic Hazard Assessment (PSHA) evaluates the probability of exceedance of a given earthquake intensity threshold like the Peak Ground Acceleration, at a target site for a given exposure time. The stochasticity of the occurrence of seismic events is modelled by stochastic processes and the propagation of the earthquake wave in the soil is typically evaluated by empirical relationships called Ground Motion Prediction Equations. The large uncertainty affecting PSHA is quantified by defining alternative model settings and/or model parametrizations. In this work, we propose a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA) method for identifying the model parameters most important for the uncertainty in PSHA, that consists in generating alternative artificial datasets by bootstrapping an available input-output dataset and aggregating the individual rankings obtained with the modularized method from each of those.The proposed method is tested on a realistic PSHA case study in Italy. The results are compared with a standard variance-based Global Sensitivity Analysis (GSA) method of literature. The novelty and strength of the proposed BMGSA method are both in the fact that its application only requires input-output data and not the use of a PSHA code for repeated calculations.

Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility

Even accurate and precisely specified models with excellent underlying data quality can be inhibited by parameter uncertainty that reflects uncertain factors. This paper suggests adopting portfolio management methods from finance in policy planning models as a practical tool for explicitly reckoning the parameter uncertainty when defining optimal policy. We demonstrate the approach in an economywide model that aims to find an optimal pro-poor agricultural value chain in Senegal under the world market uncertainty. We show that prioritizing the rice sector is the most effective policy in terms of expected policy return, but this policy is also associated with the highest risk, increasing poverty under unfavorable yet realistic scenarios. However, like diversified portfolios in finance, balancing rice promotion with support for other sectors can reduce risk at the cost of reduced expected policy return. The suggested methodology allows for explicit risk-based decision-making and can be used in policy prioritization models that cannot define robust optimal policy under standard parameter sensitivity analysis tests.

Comparative effectiveness of exercise-based cardiac rehabilitation for heart failure: a Bayesian network analysis of 44 randomized trials and 5896 patients

Abstract Introduction The prevalence of heart failure (HF) is increasing nowadays. Exercise-based cardiac rehabilitation (CR) reduces mortality and further improves the outcome of patients with HF. However, the effect of different types of CR on HF remains unclear. Data comparing these CR have not been synthesized. Thus, the purpose of this study is to determine the relative efficacy of different types of exercise-based CR for individuals with HF using a Bayesian network meta-analysis. Methods We followed a pre-specified protocol (PROSPERO: CRD42021278351). We conducted a systematic literature review of any randomized controlled trials which evaluated exercise-based CR for patients with HF. Databases including Embase, Medline, the Cochrane Central Register of Controlled Trials, and Web of Science were screened up to 31 December 2021. The primary outcomes focus on functional capacity (peak oxygen uptake and 6-minute walk distance) and health-related quality of life (hr-QOL). The main analysis was complemented by network subanalysis, standard pairwise comparisons, and subgroup and sensitivity analyses. The pooled estimates were quantified as mean differences (MD) or standardized mean difference (SMD)where appropriate, at 95% confidence intervals (CI). Results We identified 44 randomized controlled trials with a total of 5896 HF participants. Among 23 studies that compared exercise-based CR and usual care, participants in center-based CR (CBCR) had improvement of peak oxygen uptake (MD: 2.30 mL/kg per minute; 95% CI, 0.43–4.40) and 6-minute walk distance (MD: 33 meters; 95% CI, 11.00–58.00).18 studies that compared exercise-based CR with usual care showed that CBCR and cardiac telerehabilitation (CTR) had greater improvements in hr-QOL (SMD: −0.38; 95% CI, −0.49 to −0.15 and SMD: −0.18,95% CI, −0.36 to −0.00 respectively). Treatment ranking indicated CTR has the highest probability (46.0%, 50.2%) of being the most effective CR for peak oxygen uptake and hr-QOL elevation respectively. CBCR was similarly indicated as the most effective treatment for 6-min walk distance (38.7%). Conclusion Exercise-based CR programs provide broader cardiovascular benefits compared with UC. CBCR significantly improved functional capacity. CBCR and CTR have favorable effects on hr-QOL. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): China Scholarship Council

Age-specific effects of childhood body mass index on multiple sclerosis risk

ObjectiveHigher body mass index (BMI) during early life is thought to be a causal risk factor for multiple sclerosis (MS). We used longitudinal Mendelian randomisation (MR) to determine whether there is a critical window during which BMI influences MS risk.MethodsSummary statistics for childhood BMI (n ~ 28,000 children) and for MS susceptibility were obtained from recent large genome-wide association studies (GWAS) (n = 14,802 MS, 26,703 controls). We generated exposure instruments for BMI during four non-overlapping age epochs (< 3 months, 3 months–1.5 years, 2–5 years, and 7–8 years) and performed MR using the inverse variance weighted method with standard sensitivity analyses. Multivariable MR was used to account for effects mediated via later-life BMI.ResultsFor all age epochs other than birth, genetically determined higher BMI was associated with an increased liability to MS: Birth [Odds Ratio (OR) 0.81, 95% Confidence Interval (CI) 0.50–1.31, Number of Single-Nucleotide Polymorphisms (NSNPs) = 7, p = 0.39], Infancy (OR 1.18, 95% CI 1.04–1.33, NSNPs = 18, p = 0.01), Early childhood (OR 1.31, 95% CI 1.03–1.66, NSNPs = 4, p = 0.03), Later childhood (OR 1.34, 95% CI 1.08–1.66, NSNPs = 4, p = 0.01). Multivariable MR suggested that these effects may be mediated by effects on adult BMI.ConclusionWe provide evidence using MR that genetically determined higher BMI during early life is associated with increased MS risk. This effect may be driven by shared genetic architecture with later-life BMI.

Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input—precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.

Adjusting COVID-19 Seroprevalence Survey Results to Account for Test Sensitivity and Specificity.

Population-based seroprevalence surveys can provide useful estimates of the number of individuals previously infected with serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and still susceptible, as well as contribute to better estimates of the case-fatality rate and other measures of coronavirus disease 2019 (COVID-19) severity. No serological test is 100% accurate, however, and the standard correction that epidemiologists use to adjust estimates relies on estimates of the test sensitivity and specificity often based on small validation studies. We have developed a fully Bayesian approach to adjust observed prevalence estimates for sensitivity and specificity. Application to a seroprevalence survey conducted in New York State in 2020 demonstrates that this approach results in more realistic-and narrower-credible intervals than the standard sensitivity analysis using confidence interval endpoints. In addition, the model permits incorporating data on the geographical distribution of reported case counts to create informative priors on the cumulative incidence to produce estimates and credible intervals for smaller geographic areas than often can be precisely estimated with seroprevalence surveys.

Assess the Application of the E-Value in the Unmeasured Confounder Evaluation of Observational Pharmaceutical Studies

Public health is very important in big cities, and data analysis on public health studies is always a demanding issue that determines the study effectiveness. E-value was proposed as a standard sensitivity analysis tool to assess unmeasured confounders in observational studies, but its value is doubted. To evaluate the usefulness of E-value, in this paper, we collected 368 observational studies on drug effectiveness evaluation published from 1998 to September 2019 (out of 3426 searched studies) and evaluated the features of E-value. We selected the effects of primary outcomes or the largest effects in terms of hazard ratio, risk ratio, or odds ratio. Effects were transformed into estimated effect sizes following a standard E-value computation. In all 368 studies, the disease with the highest percentage was infections and infestations, at 21.7% (80/368). Our results showed that the median relative effect size was 1.89 (Q1-Q3: 1.41–2.95), and the corresponding median E-value was 3.19 with 95% confidence interval lower bound 1.77. Smaller studies yielded larger E-values for the effect size estimate and the relationship was considerably attenuated when considering the E-value for the lower bound of 95% confidence interval on the effect size. Notably, E-values have a monotonic, almost linear relationship with effect estimates. We found that E-value may cause misimpressions on the unmeasured confounder, and the same E-value does not reflect the varying nature of the unmeasured confounders in different studies, and there lacks a guidance on how E-value can be deemed as small or large, all of which limits the capability of E-value as a standard sensitivity analysis tool in real applications.

Network meta-analysis (NMA) of immuno-oncology (IO) monotherapy as first-line (1L) treatments (txs) for advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%.

e21091 Background: For advanced NSCLC patients (pts) with high (≥50%) PD-L1 expression, effective IO mono options with survival benefits are approved (pembrolizumab mono, current standard of care) and emerging (cemiplimab). In a recent Phase 3 trial, cemiplimab, a high-affinity, highly potent human PD-1 inhibitor approved for tx of advanced cutaneous squamous cell carcinoma, demonstrated significantly improved overall survival (OS) and progression-free survival (PFS) vs chemotherapy (CT) in advanced NSCLC pts with PD-L1 ≥50%. A systematic literature review and NMA were conducted to identify/compare the efficacy/safety from randomized controlled trials (RCTs) for cemiplimab vs pembrolizumab or other IO mono published 2010–19. Methods: Relevant RCTs were identified by searching Embase, MEDLINE, Cochrane, and conference proceedings with predefined search strategies according to ISPOR, NICE, and PRISMA guidelines. An NMA with time-varying hazard ratios (HRs) was performed for OS and PFS. Analyses were conducted for objective response rate (ORR), Grade (G) 3–5 all-cause adverse events (AE), G3–5 immune-mediated AE (IMAE) and discontinuation due to AEs (DAE). Fixed-effect models were used due to limited evidence. Results with standard constant HRs and various sensitivity analyses were conducted to account for differences in RCT designs and other txs. Results: The feasibility assessment determined that EMPOWER-Lung 1, KEYNOTE-024, and KEYNOTE-042 trials were eligible. IMpower110 was excluded since an incompatible PD-L1 assay (SP142) was used for pt selection. For 1L advanced NSCLC with PD-L1 ≥50%, cemiplimab was associated with significantly greater PFS and ORR, and comparable OS, G3–5 AEs, IMAEs, and all-cause DAEs vs pembrolizumab (Table). At 2 yrs, numerically more pts receiving cemiplimab vs pembrolizumab were alive (59% vs 49%) and significantly more were alive w/o progression (37% vs 18%). Conclusions: In advanced NSCLC pts with PD-L1 ≥50%, cemiplimab mono demonstrated significant improvements in PFS and ORR, and comparable OS, safety/tolerability vs pembrolizumab.[Table: see text]

Exploring the variance contributions of correlated model parameters: A sampling-based approach and its application in traffic simulation models

Analyzing the impacts of model parameters on model outputs is an important but challenging topic for scientific research involving simulation models. Global Sensitivity Analysis (SA) has been recently employed by many transportation researchers for such task, but a proper SA is still not a common practice. In particular, many modelers simply assume that all parameters are uncorrelated in the SA. However, this assumption is often unrealistic for traffic simulation models, in which many parameters are actually correlated, leading to wrong conclusions.In this paper, a sampling-based approach is provided for the SA of correlated parameters. It uses Gaussian copula to link the marginal distributions of individual parameters with their global distributions and correlations, and utilizes the extended Sobol’ formula to estimate the variance-based sensitivity indexes in a Monte Carlo framework.Its application is illustrated using two different car-following models: the Intelligent Driver Model (IDM) and the Wiedemann-74 (W74) model. Results show that this method is able to accurately quantify the sensitivity of all model parameters. As a general method, this approach can be transferred like a standard quantitative SA tool to any traffic model or complex model in the wider scientific community, especially when correlated parameters exist.

Functional principal component analysis for global sensitivity analysis of model with spatial output

Motivated by risk assessment of coastal flooding, we consider time-consuming simulators with a spatial output. The aim is to perform sensitivity analysis (SA), quantifying the influence of input parameters on the output. There are three main issues. First, due to computational time, standard SA techniques cannot be directly applied on the simulator. Second, the output is infinite dimensional, or at least high dimensional if the output is discretized. Third, the spatial output is non-stationary and exhibits strong local variations.We show that all these issues can be addressed all together by using functional PCA (FPCA). We first specify a functional basis, such as wavelets or B-splines, designed to handle local variations. Secondly, we select the most influential basis terms, either with an energy criterion after basis orthonormalization, or directly on the original basis with a penalized regression approach. Then FPCA further reduces dimension by doing PCA on the most influential basis coefficients, with an ad-hoc metric. Finally, fast-to-evaluate metamodels are built on the few selected principal components. They provide a proxy on which SA can be done. As a by-product, we obtain analytical formulas for variance-based sensitivity indices, generalizing known formula assuming orthonormality of basis functions.

A Study of Within-Host Dynamics of Dengue Infection incorporating both Humoral and Cellular Response with a Time Delay for Production of Antibodies

Abstract a. Background: Dengue is an acute illness caused by a virus. The complex behaviour of the virus in human body can be captured using mathematical models. These models helps us to enhance our understanding on the dynamics of the virus. b. Objectives: We propose to study the dynamics of within-host epidemic model of dengue infection which incorporates both innate immune response and adaptive immune response (Cellular and Humoral). The proposed model also incorporates the time delay for production of antibodies from B cells. We propose to understand the dynamics of the this model using the dynamical systems approach by performing the stability and sensitivity analysis. c. Methods used: The basic reproduction number (R0) has been computed using the next generation matrix method. The standard stability analysis and sensitivity analysis were performed on the proposed model. d. Results: The critical level of the antibody recruitment rate(q) was found to be responsible for the existence and stability of various steady states. The stability of endemic state was found to be dependent on time delay(τ). The sensitivity analysis identified the production rate of antibodies (q) to be highly sensitive parameter. e. Conclusions: The existence and stability conditions for the equilibrium points have been obtained. The threshold value of time delay (τ0) has been computed which is critical for change in stability of the endemic state. Sensitivity analysis was performed to identify the crucial and sensitive parameters of the model.

A density‐based boundary evolving method for buckling‐induced design under large deformation

AbstractThis paper proposes a density‐based boundary evolving algorithm for continuum‐based topology optimization. The boundary of voids in the design domain is described by RBF (radial basis function) function controlled by RBF knots in polar coordinate, where the voids are projected onto a fixed grid using Heaviside function. For merging overlapped multiple voids, the p‐norm function is introduced to describe composite density field. The differentiability of the projection‐based boundary description algorithm allows for the sensitivity analysis via the chain rule, and therefore, it enables an efficient gradient‐based optimization method. The goal of this paper is to optimize the initial design to generate buckling‐induced mechanism under large deformation, and without loss of generality, the hyperelastic material model is chosen to describe the material behavior. Notably, this method possesses the merit of level set method, where the intermediate density only exists at the boundary of topology shape. At the same time, the proposed method is still in the density‐based optimization framework and standard sensitivity analysis of density‐based methods can be directly derived based on the chain rule. Several numerical examples are presented and discussed in detail to demonstrate the effectiveness of the proposed density‐based boundary evolving method.

CCT: continuous care trial - a randomized controlled trial of the provision of continuous care during labor by maternity care assistants in the Netherlands

BackgroundIn 2009, the Steering Committee for Pregnancy and Childbirth in the Netherlands recommended the implementation of continuous care during labor in order to improve perinatal outcomes. However, in current care, routine maternity caregivers are unable to provide this type of care, resulting in an implementation rate of less than 30%. Maternity care assistants (MCAs), who already play a nursing role in low risk births in the second stage of labor and in homecare during the postnatal period, might be able to fill this gap. In this study, we aim to explore the (cost) effectiveness of adding MCAs to routine first- and second-line maternity care, with the idea that these MCAs would offer continuous care to women during labor.MethodsA randomized controlled trial (RCT) will be performed comparing continuous care (CC) with care-as-usual (CAU). All women intending to have a vaginal birth, who have an understanding of the Dutch language and are > 18 years of age, will be eligible for inclusion. The intervention consists of the provision of continuous care by a trained MCA from the moment the supervising maternity caregiver establishes that labor has started. The primary outcome will be use of epidural analgesia (EA). Our secondary outcomes will be referrals from primary care to secondary care, caesarean delivery, instrumental delivery, adverse outcomes associated with epidural (fever, augmentation of labor, prolonged labor, postpartum hemorrhage, duration of postpartum stay in hospital for mother and/or newborn), women’s satisfaction with the birth experience, cost-effectiveness, and a budget impact analysis. Cost effectiveness will be calculated by QALY per prevented EA based on the utility index from the EQ-5D and the usage of healthcare services. A standardized sensitivity analysis will be carried out to quantify the outcome in addition to a budget impact analysis. In order to show a reduction from 25 to 17% in the primary outcome (alpha 0.05 and bèta 0.20), taking into account an extra 10% sample size for multi-level analysis and an attrition rate of 10%, 2 × 496 women will be needed (n = 992).DiscussionWe expect that adding MCAs to the routine maternity care team will result in a decrease in the use of epidural analgesia and subsequent costs without a reduction in patient satisfaction. It will therefore be a cost-effective intervention.Trial registrationTrial Registration: Netherlands Trial Register, NL8065. Registered 3 October 2019 - Retrospectively registered.