Calculation Of Confidence Intervals Research Articles

Multiple parameter identification using genetic algorithm in vanadium redox flow batteries

We propose a multiple parameter identification method for vanadium redox flow batteries (VRFBs) to estimate the model parameter in a VRFB model. The proposed method consists of an evaluation of identifiability based on the Fisher Information Matrix (FIM) to determine the best subset of model parameters to be identified, a numerical modeling of semi-two-dimensional steady-state VRFB model, and a genetic algorithm to estimate optimal model parameters. In the optimization, we introduce a fitness function involving the mean square errors of the voltage between available experimental data and results of the VRFB model. We validate the proposed method by calculating confidence intervals of identifying parameters in the subset based on the FIM from the state of charge-voltage data obtained from a small VRFB cell experiment; we compare the curves of the identified-parameter model with those obtained experimentally. Further, we demonstrate the robustness of the proposed method through its application to a kW-scale VRFB stack utilizing advanced mixed electrolytes. The capacity-voltage curves predicted by the identified-parameter model show good agreement with those obtained experimentally under various operating conditions, with mean relative errors of less than 1.9%.

OAR sparing 3D radiotherapy planning supported by fMRI brain mapping investigations

The human brain as an organ has numerous functions; some of them can be visualized by functional imaging techniques (e.g., functional MRI [fMRI] or positron emission tomography). The localization of the appropriate activity clusters requires sophisticated instrumentation and complex measuring protocol. As the inclusion of the activation pattern in modern self-tailored 3D based radiotherapy has notable advantages, this method is applied frequently. Unfortunately, no standardized method has been published yet for the integration of the fMRI data into the planning process and the detailed description of the individual applications is usually missing. Thirteen patients with brain tumors, receiving fMRI based RT planning were enrolled in this study. The delivered dose maps were exported from the treatment planning system and processed for further statistical analysis. Two parameters were introduced to measure the geometrical distance Hausdorff Distance (HD), and volumetric overlap Dice Similarity Coefficient (DSC) of fMRI corrected and not corrected dose matrices as calculated by 3D planning to characterize similarity and/or dissimilarity of these dose matrices. Statistical analysis of bootstrapped HD and DSC data was performed to determine confidence intervals of these parameters. The calculated confidence intervals for HD and DSC were (5.04, 7.09), (0.79, 0.86), respectively for the 40 Gy and (5.2, 7.85), (0.74, 0.83), respectively for the 60 Gy dose volumes. These data indicate that in the case of HD < 5.04 and/or DSC > 0.86, the 40 Gy dose volumes obtained with and without fMRI activation pattern do not show a significant difference (5% significance level). The same conditions for the 60 Gy dose volumes were HD < 5.2 and/or DSC > 0.83. At the same time, with HD > 7.09 and/or DSC < 0.79 for 40 Gy and HD > 7.85 and/or DSC < 0.74 for 60 Gy the impact of fMRI utilization in RT planning is excessive. The fMRI activation clusters can be used in daily RT planning routine to spare activation clusters as critical areas in the brain and avoid their high dose irradiation. Parameters HD (as distance) and DSC (as overlap) can be used to characterize the difference and similarity between the radiotherapy planning target volumes and indicate whether the fMRI delivered activation patterns and consequent fMRI corrected planning volumes are reliable or not.

Dissecting the statistical properties of the linear extrapolation method of determining protein stability.

The linear extrapolation method to determine protein stability from denaturant-induced unfolding experiments is based on the observation that the free energy of unfolding is often a linear function of the denaturant concentration. The value in the absence of denaturant is then estimated by extrapolation from this linear relationship. Parameters and their confidence intervals are typically estimated by nonlinear least-squares regression. We have compared different methods for calculating confidence intervals and found that a simple method based on linear theory gives accurate results. We have also compared three different parameterizations of the linear extrapolation method and show that the most commonly used form is problematic since the stability and m-value are correlated in the nonlinear least-squares analysis. Parameter correlation can in some cases causes problems in the estimation of confidence intervals and regions and should be avoided when possible. Two alternative parameterizations show much less correlation between parameters.

Comparison of Confidence Intervals for the TG Estimator in Capture-recapture Data

Capture-recapture techniques are very powerful tool and widely used for estimating an elusive target population size. Capture-recapture count data is presented in form of frequencies of frequencies data. They consist of the frequency of unites detected exactly once, twice, and so on, and the frequency of undetected unites is unknown. As consequence, the resulting distribution is a zero-truncated count distribution. The binomial distribution is selected as a simple model if the maximum number of counting occasions is known. It counting occasions are not known in advance, the series of frequencies assumed to be the Poisson distribution. In fact, the target population might be heterogeneous because it has different characteristics, resulting in over or under dispersion based on the basic models. The mixed Poisson, which is the exponential-Poisson mixture model, have been widely used to construct population size estimator for capture-recapture data. The original Turing estimator provides a good performance under the Poisson distribution. Additionally, an extension of Turing estimator, called the Turing-based geometric distribution with non-parametric approach was proposed (TG) for the heterogeneous population. It gives an easy way to estimate the target population size. In this work, we derived uncertainty measures for the TG estimator by considering two sources of variance (M1), and the second way is using only one source of variance (M2). It is emphasised that although the analytic approaches to compute uncertainty measures can be easily used in practice, there are valid asymptotically and requires a large sample size. Therefore, re-sampling approaches, true bootstraps (M3), imputed bootstrap (M4) and reduced bootstrap (M5), are proposed as alternative methods to get uncertainty measures. The study compares performance of variance and confidence interval of paralytics and re-sampling methods by using a simulation study. Overall, the imputed bootstrap is the best choice for estimating variance and constructing confidence interval for the TG estimator. The analytic approach with two sources of variance remains successful to estimate variance and calculate confidence interval in the case of large. It is very clear that the reduced bootstrap and the analytic approach with one source of variance are not appropriate in all situations. For the true bootstrap, the true value of population size is often unknown in nature; therefore, it quite useless for capture-recapture study.

Expanding access to maternal, newborn and primary healthcare services through private-community-government partnership clinic models in rural Kenya: the Ubuntu-Afya kiosk model

BackgroundFifteen counties contribute 98.7% of the maternal and newborn morbidity and mortality in Kenya. The dismal maternal and newborn (MNH) outcomes in these settings are mostly attributable to limited access to skilled MNH services. Public health services are stretched and limited in reach, and many social programmes are not sustainably designed. We implemented a network of 16 self-sustaining community medical centres (Ubuntu-Afya Kiosks) in Homa Bay County, to facilitate access to MNH and other primary health services. We investigated the effect of these centres on MNH access indicators over a 2-year period of initial implementation.MethodsWe conducted a baseline and end-line survey in June 2016 and May 2018 respectively, in 10 community health units (CHU) served by Ubuntu-Afya Kiosks. We targeted women of child bearing age, ensuring equal sample across the 10 CHUs. The surveys were powered to detect a 10% increase in the proportion of women who deliver under a skilled birth attendant from a perceived baseline of 55%. Background characteristics of the respondents were compared using Fisher’s exact test for the categorical data. STATA ‘svy’ commands were used to calculate confidence intervals for the proportions taking into account the clustering within CHU.ResultsThe coverage of antenatal care during previous pregnancy was 99% at end-line compared to 81% at baseline. Seventy one percent of mothers attended at least four antenatal care visits, compared to 64% at baseline. The proportion of women who delivered under a skilled birth attendant during previous pregnancy was higher at end-line (90%) compared to baseline (85%). There was an increase in the proportion of women who had their newborns examined within 2 day of delivery from 74 to 92% at end-line. A considerable proportion of the respondents visited private clinics at end-line (31%) compared to 3% at baseline.ConclusionsUbuntu-Afya Kiosks were associated with enhanced access to MNH care, with significant improvements observed in newborn examination within 2 days after delivery. More women sought care from private clinics at end-line compared to baseline, indicating potential for private sector in supporting health service delivery gaps in under-served settings.

The Fed's Communications: Suggestions for Improvement

The chief economist of Berenberg Capital Markets proposes three broad ways of improving the Fed's communications: (1) establish a more systematic approach to achieving its dual mandate; (2) clarify the proper role of monetary policy in achieving those objectives by distinguishing what is within the scope of monetary policy from what is clearly beyond it; and (3) articulate the Fed's goals and role in achieving macro‐prudential risk management and financial stability.With these three ends in view, the author begins by urging FOMC members to refrain from making public comments immediately following government data releases and, when making public speeches and statements, to relate their comments on the economy to the Fed's dual mandate. The author also suggests three modifications of the Fed's official Policy Statement following FOMC meetings. First, each statement should start with an assessment of monetary policy and its consistency with achieving the Fed's statutory mandate, rather than the Fed's assessments of the economy and its subsectors with which such statements now begin. Second, the Fed should communicate separate explicit risk assessments of inflation and of the prospects for employment and the economy. (The Fed's current practice of sometimes dropping the risk assessments from statements and replacing them with nuanced language—for example, on changes in inflation and inflationary expectations—can be a source of confusion.) Third, all statements should discuss as clearly as possible the Fed's strategy for its balance sheet and unwind policy.The Fed's quarterly Summary of Economic Projections (SEPs) should be redesigned to include FOMC estimates of forecast uncertainties and what they imply for monetary policy, and such alternative forecasts should be presented in place of the current central tendency and range of forecasts. The redesigned SEPs should be (1) based on a rigorous Fed assessment of expected monetary policy under different situations and contingency planning, and (2) as transparent as possible about the Fed's economic and inflation outlooks, the uncertainties in forecasting, and the conditionality of monetary policy. An illustration is provided of the alternative SEPs that would replace the current “dot plots” and include the Fed's forecasts of nominal GDP, calculated confidence intervals around the FOMC's median forecasts, and three separate forecasts of the Fed's perceived appropriate Fed funds rate.Finally, the author views the “optimal solution” as a more systematic approach in which the Fed publishes a single forecast based on a model consistent with its dual mandate that shows how the appropriate Fed funds rate path would be expected to vary under different economic and inflation outcomes. Such an approach, by thus mapping likely monetary policy responses to alternative plausible economic and inflation outcomes, would increase the Fed's accountability as well as its transparency.

Trends in Tanning Bed Use, Motivation, and Risk Awareness in Germany: Findings from Four Waves of the National Cancer Aid Monitoring (NCAM).

Indoor tanning is an important risk factor for the development of melanoma and non-melanoma skin cancer. With our nationally representative monitoring, we aimed at describing tanning bed use, user characteristics, reasons for use, and risk awareness over time. In the framework of the National Cancer Aid Monitoring (NCAM), we collected representative data on 12,000 individuals aged 14 to 45 years in annual waves of n = 3,000 participants in Germany between 2015 and 2018. We used descriptive statistics and chi²-tests to uncover group differences. To compare data from the different waves, we calculated confidence intervals. The use of tanning beds decreased from 2015 (11.0%, 95%-CI: 9.9%–12.1%) to 2018 (8.8%, 95%-CI: 7.8%–9.8%). However, this decrease did not affect all subgroups. For instance, there was an (non-significant) increase in minors and the prevalence remained stable for individuals with immigrant background and males. Attractiveness was an important reason for tanning bed use in each wave. Over time, there was an increase in medical-related reasons for use. Furthermore, monitoring showed a decrease in risk awareness regarding tanning bed use and ultraviolet (UV) radiation. While it is a positive development that the overall use of tanning beds in Germany has decreased over time, the increasing use by minors despite the legal ban is alarming. Due to the declining risk awareness it is necessary to implement prevention and education campaigns specifically targeted at this group.

High-resolution 13C metabolic flux analysis.

Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. 13C metabolic flux analysis (13C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for 13C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with 13C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for 13C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account.

The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as early as possible in the drug-discovery process. Here, we provide detailed instructions for capitalizing on bioactivity predictions to elucidate the MoAs of small molecules and infer their underlying phenotypic effects. We illustrate how these predictions can be used to infer the underlying antidepressive effects of marketed drugs. We also provide the necessary functionalities to model cytotoxicity data using single and ensemble machine-learning algorithms. Finally, we give detailed instructions on how to calculate confidence intervals for individual predictions using the conformal prediction framework. © 2019 by John Wiley & Sons, Inc.

Statistical confidence intervals for relative abundances and abundance-based ratios: Simple practical solutions for an old overlooked question

Abstract Micropaleontologists often consider relative abundances of taxa to infer past ecological, environmental and climate conditions and dynamics. However, most published micropaleontological studies involving relative abundance data still do not routinely consider the counting uncertainty inherent to any sample, and thus simply ignore the statistical confidence interval (CI) related to a relative abundance or abundance-based ratio value. In an attempt to make this rather classic computation freely and easily available to the scientific community, we highlight here the calculation of binomial proportion CIs based on the ‘exact’ Clopper–Pearson method as implemented in the user-friendly PAST freeware. We also introduce a general solution for the computation of the CI related to any abundance-based ratio. In all cases, we strongly recommend that future studies involving taxonomic abundance-based data should systematically display the CIs associated to sample estimates, the only way to integrate sampling uncertainties into result interpretation.

LO75: The impact of snowfall on patient attendance at an urban academic emergency department

Introduction: Accurate forecasting of emergency department (ED) patient visits can inform better resource matching. Calendar variables such as day of week and time of day are routinely used as predictors of ED volume. Further improvement in forecasting will likely come from dynamic variables. The effect of snowfall on ED volumes in colder climates remains poorly understood. We sought to determine whether accounting for snowfall improves ED patient volume forecasting. Our secondary objective was to characterize the magnitude of effect of snowfall on ED volume. Methods: This was a retrospective observational study using historical patient volume data and local snowfall records from April 1st, 2011 to March 31st, 2018 (2,542 days) at a single urban ED. We fit a series of four generalized linear models: a baseline model which included calendar variables and three different snowfall models which contained the variables in the baseline model plus an indicator variable for modelling snowfall. Each snowfall model had a different daily threshold for its indicator variable: any snowfall ( &amp;gt;0cm), moderate snowfall ( &amp;gt; = 1 cm), or high snowfall ( &amp;gt; = 5 cm). We modeled daily ED volume as the dependent variable using a Poisson distribution. To evaluate model fit, we examined the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in each of the four models. In both cases, a lower number indicates better model fit. Incident rate ratios were calculated to determine the effect of snowfall. We used the delta method to calculate confidence intervals. Results: A total of 2542 days were used to develop the model. All three snowfall models demonstrated improved model fit compared to the baseline model with lower AIC and BIC values. The best fitting model included a binary variable for moderate snowfall ( &amp;gt; = 1cm/day). This model showed a statistically significant decrease in ED volume of 2.65% (95% CI: 1.23% -4.00%) on snowfall days, representing 5.4 (95% CI: 2.5 -8.2) patients per day at our hospital with an average daily volume of 205 patients. Conclusion: The addition of a snowfall variable results in improved forecasting model performance in ED volume forecasting with optimal threshold set at 1 cm of snow in our setting. Snowfall is associated with a modest, but statistically significant reduction in ED volume.

An analysis of trends, uncertainty and species selection shows contrasting trends of widespread forest and farmland birds in Europe

Abstract 1. Composite, multispecies biodiversity indices are increasingly used to report against international and national environmental commitments and targets, the Wild Bird Index being a prominent example in Europe, but methods to assess trends, error and species selection for such indices are poorly developed. 2. In this study, we compare methods to compute multispecies supranational indices and explore different approaches to trend and error estimation, the presentation of indices, and species selection. We do so using population trend data on forest and farmland birds from 28 European countries, 1980–2015. 3. We find relative stability in common European forest bird populations over this period, but a severe decline in farmland bird populations. Altering the benchmark year affects index characteristics and ease of interpretation. We show that using annual species’ indices and their SEs to calculate confidence intervals delivers greater precision in index estimates than bootstrapping across species. The inclusion of individual species within indices has limited leverage on index characteristics, but subjective selection of species based on specialisation has the potential to generate bias. 4. Multispecies indices are valuable policy-relevant tools for describing biodiversity health. Their calculation and presentation need to be tailored to meet specific policy objectives, and they must be supported by clear interpretative information. We recommend methods for indicator analysis, forms of presentation, and the adoption of an objective species selection protocol to ensure indicators are representative and sensitive to environmental change.

Uncertainty assessment of extreme flood estimation in the Dongting Lake basin, China

Abstract In this paper, we study uncertainty in estimating extreme floods of the Dongting Lake basin, China. We used three methods, including the Delta, profile likelihood function (PLF), and the Bayesian Markov chain Monte Carlo (MCMC) methods, to calculate confidence intervals of parameters of the generalized extreme value (GEV) distribution and quantiles of extreme floods. The annual maximum flow (AMF) data from four hydrologic stations were selected. Our results show that AMF data from Taoyuan and Xiangtan stations followed the Weibull class distribution, while the data from Shimen and Taojiang stations followed the Fréchet class distribution. The three methods show similar confidence intervals of design floods for short return periods. However, there are large differences between results of the Delta and the other two methods for long return periods. Both PLF and Bayesian MCMC methods have similar confidence intervals to reflect the uncertainty of design floods. However, because the PLF method is quite burdensome in computation, the Bayesian MCMC method is more suitable for practical use.

Confidence intervals in dermatology

This study from the U.S.A. aimed to find out how often confidence intervals are used when reporting dermatology research. Confidence intervals relate to two values (‘intervals’) that the researchers, using data analysis, believe that the actual, true value will fall within. A 95% confidence interval indicates that if an experiment were repeated 100 times, and a 95% confidence interval was calculated each time, 95 of the intervals would contain the true value while five of the intervals would not contain the true value. A confidence interval might be used when you want to estimate something about a very large number of people, and can therefore only use a sample of those people. For example, if you want to know how many people in the UK have dry skin, you cannot ask every person, so you use a sample and then calculate a range for the mean number of people in your sample who have dry skin. As the sample is never going to be an exact representation of the whole population, you might say that you are 95% confident that the mean number of people in the UK with dry skin is, for example, between 20% and 30%. Confidence intervals help readers understand both the statistical significance and the clinical significance more easily. Most modern statistical software packages conveniently calculate confidence intervals and provide an intuitive way to understand study results. The authors collected dermatology articles from a variety of major dermatology journals as well as dermatology articles from the New England Journal of Medicine over the past decade. The authors found that reporting of this important metric is disappointingly low in dermatology journals, with only 1 in 5 articles containing confidence intervals, which was significantly lower than reporting in the New England Journal of Medicine. Articles published more recently were more likely to report confidence intervals. After controlling for factors like type of study and year of publication, the New England Journal of Medicine still reported confidence intervals more frequently. This is likely due to their extremely rigorous statistical peer review process. While such strict requirements cannot be adopted by all journals, increased awareness of this simple metric could significantly improve the statistical reporting of the dermatology literature. We urge dermatology researchers to include this important metric in their findings. Likewise, we recommend improved statistical training as part of dermatology residency training.

WORK-PLACE CONDITIONS AND HEALTH OF WORKERS IN BUILDING MATERIALS INDUSTRY OF THE RUSSIAN FEDERATION ACCORDING TO 40-YEARS CLINICAL AND HYGIENIC RESEARCH

Introduction. Building materials industry, including the production of clay and ceramic building bricks, cement, concrete products, saw building stone, sawn wood, granite blocks and rubble, is one of the most important economy units, prompting authors to generalize research data about working conditions and the incidence of occupational diseases in the main production building materials industry/ The data were accumulated during many years. Material and methods. Research methods included a comprehensive study of the working conditions of employees at enterprises of construction materials industry, followed by in-depth periodic medical examination. Then, with statistical methods (determination of the confidence interval index of the binomial distribution) calculated confidence interval for the prevalence of patients with an established diagnosis of occupational diseases in similar enterprises. Results. Results of 40-years research on occupational hazards parameters and physical examination of workers employed at productions of clay and ceramic bricks, cement, armed concrete, saw building stone, lumber wood, granite blocks crush stone were summarized because of the absence of complex estimation of work-place conditions and incidence of occupational diseases in main production of building materials industry. Typical occupational hazards were established for the every production. Many productions show the greatest disadvantage concerning levels of physical overload that was neglected by specialists in occupational health and safety for many years. The prevalence of patients suffering from chronic occupational diseases was predicted for employees of investigated productions and the most relevant forms of occupational pathology were determined. About 7.7% of workers in investigated productions appeared to suffer from occupational diseases that were not timely diagnosed. This figure is approximately 10 times higher than official statistics. The dependence of the prevalence of patients with occupational diseases from the level of technological modernization of production was revealed. If we can accept the proportion of patients with occupational diseases among employees working in the complex automated production (the highest degree of organization of labor) for referent unit than at the enterprises with the other technologies the proportion will be different: automated production - 10, mechanical work - 4, mechanical hand work - 5, hand work - 3.

The Spatial Variation of the Maximum Possible Contaminant Concentration From a Steady Line Source

When considering the possible hazard or nuisance associated with a release of toxic or malodorous gas into the atmosphere, large concentrations are especially important, but relatively little work has been done on measuring or modelling the probability distribution of large concentrations of a contaminant dispersing in a turbulent flow. We have previously applied statistical extreme-value theory to field experiment measurements, analyzing large concentrations at a handful of positions. We have also been involved in using a related moment-based method to give more comprehensive spatial coverage for a steady line source in a wind tunnel. In the latter case, however, we used a method which would be expected to have shortcomings, and we did not calculate confidence intervals for the estimates. In the present paper we use an improved method, with confidence intervals derived from bootstrapping. Again we analyze the measurements from steady line-source wind-tunnel experiments, with particular emphasis on the spatial variation of the estimated maximum possible concentration. We show that the moment-based method agrees well with maximum-likelihood fitting to exceedances of a high threshold. We find that the centreline maximum concentration, normalized by the centreline mean concentration, increases downwind from a value just greater than 1 near the source, to a peak value of 5–6, before decreasing with distance from the source. Across the plume the maximum concentration only varies slowly for the downwind distances in these experiments. These observations are explained in terms of the physical processes of turbulent advection and molecular diffusion.

Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models

BackgroundMathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic models.MethodsWe describe a parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental models that correspond with applications to pandemic influenza, Ebola, and Zika.ResultsOverall, parameter identifiability issues are more likely to arise with more complex models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R0 is often robust to parameter identifiability issues affecting individual parameters in the model. Despite large confidence intervals and higher mean squared error of other individual model parameters, R0 can still be estimated with precision and accuracy.ConclusionsBecause public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental dynamic models.

Determinación de ácidos grasos no esterificados, β-hidroxibutirato, triacilglicerol y colesterol durante el balance energético negativo en vacas Holstein

The objective of the present work was to quantify the concentration of non-esterified fatty acids, β-hydroxybutyrate, triacylglycerol and cholesterol during the negative energy balance, in Holstein cows. The study was carried out by sampling and analyzing 250 blood sera obtained from 50 Holstein cows. After delivery, the first sampling was performed at 7 d, the second at 15 d and the three remaining at 30, 45 and 60 d. Mean values for non-esterified fatty acids, β-hydroxybutyrate, triacylglycerol and cholesterol were determined. The resulting data set was analyzed by Gaussian distribution and descriptive statistics. Confidence intervals of 95% were established. The comparison between groups was carried out by Analysis of Variance and frequency histogram. The non-esterified fatty acids, β-hydroxybutyrate and triacylglycerol, are consistent with the values reported internationally. The cholesterol concentration showed a slightly higher value than the references. The AGNE did not present differences between groups, the β-hydroxybutyrate registered its highest value at 15 d postpartum, and lowest at 60 d postpartum. Triacylglycerol and cholesterol showed their highest concentration at 60 d postpartum, and the lowest at 15 d postpartum. The calculated confidence intervals could be used at the herd level to detect alert situations when at least 5% of the cows in the sample fall outside the reference range.

Evaluation of stability of directly standardized rates for sparse data using simulation methods

BackgroundDirectly standardized rates (DSRs) adjust for different age distributions in different populations and enable, say, the rates of disease between the populations to be directly compared. They are routinely published but there is concern that a DSR is not valid when it is based on a “small” number of events. The aim of this study was to determine the value at which a DSR should not be published when analyzing real data in England.MethodsStandard Monte Carlo simulation techniques were used assuming the number of events in 19 age groups (i.e., 0–4, 5–9, ... 90+ years) follow independent Poisson distributions. The total number of events, age specific risks, and the population sizes in each age group were varied. For each of 10,000 simulations the DSR (using the 2013 European Standard Population weights), together with the coverage of three different methods (normal approximation, Dobson, and Tiwari modified gamma) of estimating the 95% confidence intervals (CIs), were calculated.ResultsThe normal approximation was, as expected, not suitable for use when fewer than 100 events occurred. The Tiwari method and the Dobson method of calculating confidence intervals produced similar estimates and either was suitable when the expected or observed numbers of events were 10 or greater. The accuracy of the CIs was not influenced by the distribution of the events across categories (i.e., the degree of clustering, the age distributions of the sampling populations, and the number of categories with no events occurring in them).ConclusionsDSRs should not be given when the total observed number of events is less than 10. The Dobson method might be considered the preferred method due to the formulae being simpler than that of the Tiwari method and the coverage being slightly more accurate.

A statistical comparative performance analysis of mobile network operators

Mobile telephony is one of the most widely utilized technologies in the modern world. Records of the usage behaviour of mobile users can provide valuable information for understanding the behaviour of networks for Mobile Network Operators (MNOs). For different reasons, MNOs are interested in knowing how their competitors’ performance varies based on location, phone category, phone Operating System (OS) for various cellular network technology (CNT). This can help MNOs to invest intelligently in locations where they operate with inferior performance. Therefore, Key Performance Indicator (KPI) comparisons among MNOs are of interest for all MNOs. In this article, we investigate cellular network performance statistical comparisons of major Mobile Network Operators (MNOs) in Turkey using a large scale real-world proprietary mobile traffic dataset over a period of 18 months. Focusing our approach on different dimensions of crowd-sourced dataset allows us: (i) to know end-to-end nationwide network performance comparisons of MNOs using real-world measurement data, (ii) to calculate Confidence Intervals (CIs) for the mean difference of KPIs (such as downlink speed, latency, jitter and packet loss) for obtaining useful comparative statistical information of MNO performances and (iii) to observe the existence of significant performance differences between MNOs depending on the region which they are operating, phone category, phone OS as well as CNTs.