PERT Distribution Research Articles

Modeling body size information within weight labels using probability distributions.

What images of bodies do we associate with thinness and fatness? Can our representations of weight-related words be described by simple probability distributions? To answer these questions, the present study examined participants' perceptions of a set of weight-related words using a pictural scale. 259 French women indicated the thinnest, fattest, and best-fitting figures for 13 words. We then used their responses to construct PERT probability distributions, simple skewed distributions allowing to visualize what body sizes were associated with each word. In particular, the variability of the distributions showed how different weight labels can have more or less precise meanings. We found some evidence that the lowest body mass index associated with a label shifted towards thinner figures as body dissatisfaction increased. Using the same method, we investigated the boundaries of what participants consider the ideal body, and showed that the inclusion of their own body in these boundaries predicted their levels of body dissatisfaction. We argue that PERT distributions can be a useful, easy-to-use tool in body image research for modeling the representations of weight labels in different populations.

Quantitative microbial risk assessment of Salmonella in fresh chicken patties

Quantitative microbial risk assessment (QMRA) has witnessed rapid development within the context of food safety in recent years. As a means of contributing to these advancements, a QMRA for Salmonella spp. in fresh chicken patties for the general European Union (EU) population was developed. A two-dimensional (Second Order) Monte-Carlo simulation method was used for separating variability and uncertainty of model’s parameters. The stages of industrial processing, retail storage, domestic storage, and cooking in the domestic environment were considered in the exposure assessment. For hazard characterization, a dose-response model was developed by combining 8 published dose-response models using a Pert distribution for describing uncertainty. The QMRA model predicted a mean probability of illness of 1.19*10−4 (5.28*10−5 – 3.57*10−4 95 % C.I.), and a mean annual number of illnesses per 100,000 people of 2.13 (0.96 – 6.59 95 % C.I.). Moreover, sensitivity analysis was performed, and variability in cooking preferences was found to be the most influential model parameter (r = −0.39), followed by dose-response related variability (r = 0.22), and variability in the concentration of Salmonella spp. at the time of introduction at the processing facility (r = 0.11). Various mitigation strategy scenarios were tested, from which, “increasing the internal temperature of cooking” and “decreasing shelf life” were estimated to be the most effective in reducing the predicted risk of illness. Salmonella-related illnesses exhibit particularly high severity, making them some of the most prominent zoonotic diseases in the EU. Regular monitoring of this hazard in order to further highlight its related parameters and causes is a necessary procedure. This study not only provides an updated assessment of Salmonella spp. risk associated with chicken patties, but also facilitates the identification of crucial targets for scientific investigation and implementation of real-world intervention strategies.

Assessment of the stand structure of protective forest monitoring based on statistical models in Irano-Turanian phytogeographical regions of Iran.

One of the most important data that forest management planners need for effective decisions is the data related to the forest structure. The aim of the study is to investigate and analyse the structure of protective forests in Irano-Turanian phytogeographical regions, Iran. Since there are many shrubs in this phytogeographical region, it is very difficult to measure the stand diameter at any height (breast or root height). For this reason, it is necessary to analyse the parameters of height and crown cover to investigate and analyse forest structure. For that purpose, two study plots were selected, and basic data were analysed by using statistical distributions, scatter plots and R2 coefficients. With EasyFit software and Anderson‒Darling test, it was found that the Weibull (3P) and Pearson 6 (4P) distributions for the crown cover factor and the Gen-Pareto and Pert distributions for the height factor have the best goodness-of-fit for the distribution of the different crown cover classes and heights in the studied forest. Moreover, the results confirm that there is a very weak R2 coefficient between crown cover and root collar diameter, with R2 = 0.513 and 0.369 in plots 1 and 2, respectively. Therefore, the combination of crown cover and height parameters is more suitable for use when analysing stand structure in such forests, although the values of R2 are still low (0.673 and 0.524 in plots 1 and 2, respectively). The results of this study show that in protective forests with many shrubs, it is better to focus on the height and crown cover of ​trees and of shrubs rather than on parameters related to stand/tree diameter when analysing stand structure.

Cost analysis for mirabegron use in the treatment of children with neurogenic bladder

Mirabegron is a beta-3 adrenergic receptor agonist that received FDA approval in 2021 to treat neurogenic detrusor overactivity (NDO) in children ages three years and older. Despite its safety and efficacy, access to mirabegron frequently remains restricted by payor coverage policies. This cost minimization study sought to determine the cost implications from a payor perspective of mirabegron use at different points in the treatment pathway for pediatric NDO. A Markov decision analytic model was constructed to assess the costs for eight treatment strategies over a 10-year period, using six-month cycles (Table). Five strategies involve mirabegron use as first-, second-, third-, or fourth-line therapy. Two strategies, including the "base case," entail use of anticholinergic medications followed by onabotulinum toxin type A (Botox) injection and augmentation cystoplasty. A strategy involving first-line Botox was also modeled. The effectiveness, adverse event rates, attrition rates, and costs associated with each treatment option were obtained from the clinical literature and adjusted to a six-month cycle. Costs were adjusted to 2021-dollar value. A discount rate of 3% was used. To quantify uncertainty, costs and treatment transition probabilities were modeled as gamma and PERT distributions, respectively. One-way sensitivity analyses were performed. Probabilistic sensitivity analysis (PSA) was conducted using a Monte Carlo simulation with 100,000 iterations. Analyses were performed using Treeage Pro (Healthcare Version). The least costly strategy involved first-line mirabegron (expected cost $37,954). All strategies involving mirabegron were less costly than the base case ($56,417). On PSA, first-line mirabegron was the least costly strategy in 88.9% of cases (mean $37,604, 95% CI: $37,579-37,628); in 100% of cases, the least costly strategy involved mirabegron use. Cost savings associated with mirabegron use were attributable to decreased use of augmentation cystoplasty and Botox injections. This is the first study to compare costs across multiple strategies involving mirabegron to treat pediatric NDO. Mirabegron use likely yields cost savings for the payor: the least costly strategy involved first-line mirabegron, and all pathways incorporating mirabegron were less costly than those without mirabegron use. These findings provide an updated cost analysis for the treatment of NDO by investigating mirabegron use alongside more established treatment options. Use of mirabegron for the treatment of pediatric NDO is likely associated with cost savings as compared to treatment pathways without mirabegron. Expansion of payor coverage for mirabegron, as well as clinical studies to study first-line mirabegron use, should be considered.

Uncertain project network analysis with fuzzy–PERT and Interval Type–2 fuzzy activity durations

Project network analysis is often subject to different uncertainties that can occur in real world scenarios. These uncertainties are not always measurable which makes its statistical estimation hard, so the use of third party information coming from experts is useful to provide an estimation of activity durations of the project. Projects usually have multiple experts who provide different estimates of their perceptions about activity durations which can be represented as Type-2 fuzzy sets in cases where experts do not agree on their estimates. Such disagreement/ambiguitites can be either expressed with Interval Type-2 fuzzy numbers or the proposed fuzzy-PERT set which is an extension of the classical PERT distribution to a fuzzy environment. To do so, a mathematical formulation of the fuzzy problem is presented, two solution methods are described and two illustrative examples are solved.

Optimal Structure of Real Estate Portfolio Using EVA: A Stochastic Markowitz Model Using Data from Greek Real Estate Market

The purpose of this paper is to examine the issue of portfolio optimization. Optimization consists of minimizing the risk for a given rate of return or achieving a bigger return for a given level of risk. We use historical data from the Bank of Greece to calculate the net return and the standard deviation (std) for each type of property that is available. The objective is to maximize the economic value added (EVA) of a property’s assets portfolio under a specific rate of standard deviation, following the classic Markowitz model (M-V). The stochastic procedure entry in the model uses the Monte Carlo Simulation method with debt to equity (DTE) following PERT distribution for the portfolio’s invested budget, and the net return for the normal distribution with the mean of the expected return and std are taken from historical data, correspondingly. The returns verify that they follow the base assumption of normality through the Lilliefors test in the Greek real estate market. We observe the maximization of EVA and the expected return maximizing concurrently, but the minimizing risk of EVA is diversified with the minimization of portfolio risk. We observe that the max weight that a residential asset takes is 22.7% because a bigger percent reduces both mean and std. The study provides an explicit portfolio optimization procedure under uncertainty in the real estate market and enriches the academic debate about EVA and revenue.

Quantitative microbial risk assessment indicates very low risk for Vibrio parahaemolyticus foodborne illness from Jeotgal in South Korea

In this study, a microbial risk assessment was performed for the bacteria Vibrio parahaemolyticus, which causes a foodborne illness following the consumption of Jeotgal, a fermented seafood in South Korea. The assessment comprised of six stages: product, market, home, consumption, dose-response, and risk. The initial contamination level (IC) was calculated based on the prevalence of V. parahaemolyticus in 90 Jeotgal samples. The kinetic behavior of V. parahaemolyticus was described using predictive models. The data on transportation conditions from manufacturer to market and home were collected through personal communication and from previous studies. Data for the Jeotgal consumption status were obtained, and an appropriate probability distribution was established. The simulation models responding to the scenario were analyzed using the @RISK program. The IC of V. parahaemolyticus was estimated using beta distribution [Beta (1, 91)]. The cell counts during transportation were estimated using Weibull and polynomial models [δ = 1 / (0.0718 – 0.0097 × T + 0.0005 × T2)], while the probability distributions for time and temperature were estimated using Pert, Weibull, Uniform, and LogLogistic distributions. Daily average consumption amounts were assessed using the Pareto distribution [0.60284,1.32,Risk Truncate(0,155)]. The results indicated that the risk of V. parahaemolyticus infection through Jeotgal consumption is low in South Korea.

Risk assessment for norovirus foodborne illness by raw oyster (Ostreidae) consumption and economic burden in Korea

The objective of this study was to evaluate the probability of norovirus foodborne illness by raw oyster consumption. One hundred fifty-six oyster samples were collected to examine the norovirus prevalence. The oyster samples were inoculated with murine norovirus and stored at 4°C-25°C. A plaque assay determined norovirus titers. The norovirus titers were fitted with the Baranyi model to calculate shoulder period (h) and death rate (Log PFU/g/h). These kinetic parameters were fitted to a polynomial model as a function of temperature. Distribution temperature and time were surveyed, and consumption data were surveyed. A dose-response model was also searched through literature. The simulation model was prepared with these data in @RISK to estimate the probability of norovirus foodborne. One sample of 156 samples was norovirus positive. Thus, the initial contamination level was estimated by the Beta distribution (2, 156), and the level was .5.3 Log PFU/g. The developed predictive models showed that the norovirus titers decreased in oysters under the storage conditions simulated with the Uniform distribution (0.325, 1.643) for time and the Pert distribution (10, 18, 25) for temperature. Consumption ratio of raw oyster was 0.98%, and average consumption amount was 1.82 g, calculated by the Pert distribution [Pert {1.8200, 1.8200, 335.30, Truncate (0, 236.8)}]. 1F1 hypergeometric dose-response model [1 -(1 + 2.55 × 10−3 × dose)-0.086] was appropriate to evaluate dose-response. The simulation showed that the probability of norovirus foodborne illness by raw oyster consumption was 5.90 × 10−10 per person per day. The annual socioeconomic cost of consuming raw oysters contaminated with norovirus was not very high.

Monte Carlo Simulation in a Peruvian Highway

Highways and roads are important for nations' development and life quality. This is not different for Peru. A highway project called Daniel Alcides Carrión is expected to provide a solution to the over-employed Carretera Central road. This is a multimillionaire and important infrastructure project. Hence, it is important to evaluate the possible sustainability risks. In consequence, this study employed the Monte Carlo simulation for such a purpose. First, variables have been chosen and segregated into input and output. Variables like the initial investment, recurrent maintenance, periodical maintenance, savings in the operative cost of vehicles, and time savings employed the triangular distribution. Traffic growth and inflation rate employed the Pert distribution. The project's Net Present Value and Internal Rate of Return have been selected as output variables. Crystal Ball software has been employed to perform the Monte Carlo analysis. Consequently, this research found a high probability that the highway can become a profitable project due to its Net Present Value and Internal Rate of Return. Moreover, savings in operative costs of vehicles and traffic growth rate had positive impacts on the project's Net Present Value. However, the initial investment had a negative relationship with the output variable. Hence, the new highway should take prevision policies to maintain traffic flux. Thus, avoid closures that can have both human and natural sources. This study is the first in the Peruvian academic literature regarding highways risk analysis. Moreover, this study provides researchers, state officials, future highway managers, and users' valuable information to elaborate preventive measures to maintain the highway's social sustainability and increase its benefits.

Estimation of Parameters of Pert Distribution by Using Method of Moments

Abstract: The method of moments has been widely used for estimating the parameters of a distribution. Usually lower order moments are wont to find the parameter estimates as they're known to possess less sampling variability. The method of moments may be a technique for estimating the parameters of a statistical model. It works by finding values of the parameters that end in a match between the sample moments and therefore the population moments (as implied by the model). the Method of moment Estimator is used to find out Estimates the parameters of PERT Distribution. We also compare equispaced and unequispaced Optimally Constructed Grouped data by the method of an Asymptotically Relative Efficiency. We also computed Average Estimate (AE), Variance (VAR), Standard Deviation (STD), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Simulated Error (SE) and Relative Absolute Bias (RAB) for both the parameters under grouped sample supported 1000 simulations to assess the performance of the estimators. Keywords: Method of Moments, PERT Distribution, equispaced and unequipped Optimal Grouped sample

Evaluation of the Impact of Skewness, Clustering, and Probe Sampling Plan on Aflatoxin Detection in Corn.

Probe sampling plans for aflatoxin in corn attempt to reliably estimate concentrations in bulk corn given complications like skewed contamination distribution and hotspots. To evaluate and improve sampling plans, three sampling strategies (simple random sampling, stratified random sampling, systematic sampling with U.S. GIPSA sampling schemes), three numbers of probes (5, 10, 100, the last a proxy for autosampling), four clustering levels (1, 10, 100, 1,000 kernels/cluster source), and six aflatoxin concentrations (5, 10, 20, 40, 80, 100 ppb) were assessed by Monte‐Carlo simulation. Aflatoxin distribution was approximated by PERT and Gamma distributions of experimental aflatoxin data for uncontaminated and naturally contaminated single kernels. The model was validated against published data repeatedly sampling 18 grain lots contaminated with 5.8–680 ppb aflatoxin. All empirical acceptance probabilities fell within the range of simulated acceptance probabilities. Sensitivity analysis with partial rank correlation coefficients found acceptance probability more sensitive to aflatoxin concentration (−0.87) and clustering level (0.28) than number of probes (−0.09) and sampling strategy (0.04). Comparison of operating characteristic curves indicate all sampling strategies have similar average performance at the 20 ppb threshold (0.8–3.5% absolute marginal change), but systematic sampling has larger variability at clustering levels above 100. Taking extra probes improves detection (1.8% increase in absolute marginal change) when aflatoxin is spatially clustered at 1,000 kernels/cluster, but not when contaminated grains are homogenously distributed. Therefore, taking many small samples, for example, autosampling, may increase sampling plan reliability. The simulation is provided as an R Shiny web app for stakeholder use evaluating grain sampling plans.

Assessment of the environmental impacts of Xylella fastidiosa subsp. pauca in Puglia

The introduction of Xylella fastidiosa subsp. pauca in Puglia (Italy) has disturbed the flow of ecosystem services (ES). We assessed the short- and long term impacts of this disease and the control measures against it on ES provisioning and biodiversity in the olive-producing agroecosystem of Puglia at a landscape level by using an ES framework in combination with expert knowledge elicitation. Experts estimated the minimum, maximum and most likely relative reductions in ES delivery and biodiversity components in the affected area. The estimates were used to fit a PERT distribution. Results show that the loss of olive trees (the main woody plant in the region) in general, and of centenarian olive trees in particular, has affected ES delivery. The delivery of ES is estimated to diminish by 34% and 30% in the short- and long term, respectively, mainly due to the impact of the disease on food (olive) production, ornamental resources, soil erosion regulation, primary production and cultural heritage. Biodiversity components are estimated to diminish by 28%, mainly due to the impact of the disease on genetic diversity and habitats of high conservation value. The study provides a first assessment of the wider environmental impacts of Xylella fastdiosa subsp. pauca, which can serve as a basis for formulating hypotheses for further investigation.

PROJECT SIMULATION USING EXCEL WITHOUT ADD-INS

ABSTRACT This paper demonstrates a simulation application using Excel without add-ins in calculating PERT and CPM of a project with random activity times. PERT/CPM and PERT distribution are reviewed and PERT-distribution random number generation is discussed. The development of the simulation model is presented in detail. We take advantage of the capabilities of one-variable data table to run the simulation of the project. Our approach avoids the add-ins with little additional efforts to execute the simulation. Keywords PERT/CPM; PERT distribution; simulation

Burden of Disease of Guillain-Barré Syndrome in Brazil before and during the Zika virus epidemic 2014-2016.

To estimate the burden of disease of Guillain-Barré syndrome (GBS) in Brazil in 2014, 1year before the Zika virus epidemic, and in 2015 and 2016 during the epidemic. The burden of disease of GBS was estimated using the summary measure of population health: Disability Adjusted Life Years (DALY), that combines both mortality (Years of Life Lost YLLs) and morbidity (Years Lived with Disability) components. The study population was composed of GBS hospitalised cases and deaths from the information systems of the Brazilian Unified Health System. The GBS incidence rate in 2014, 2015 and 2016 was 0.74, 0.96, 1.02/100000 respectively, and the mortality rate in the same period was 0.08, 0.009 and 0.11/100000 habitants. The DALYs calculated using the point estimate of GBS disability weight and its values of the confidence interval (0.198 and 0.414) were 5725.90 (5711.79-5742.89) in 2014, 6054.61 (6035.57-6077.54) in 2015 and 7588.49 (7570.20-7610.51) in 2016. The DALYs were high among the male population and in age groups between 20 and 50years. The increase in DALYs in the years 2015 and 2016 compared to 2014 probably resulted from the introduction of ZIKV in Brazil, reinforcing the importance of investments in the prevention of ZIKV infection and in the care of GBS patients.

Effects of Housing System on Dairy Heifer Replacement Cost From Birth to Calving: Evaluating Costs of Confinement, Dry-Lot, and Pasture-Based Systems and Their Impact on Total Rearing Investment.

Replacement heifer rearing is critical for the future of dairy operations, to improve genetic merit and maintain herd size. A myriad of options exist on how to manage, feed, and ultimately raise replacement heifers. Pasture is perceived to offer optimal welfare and an economical housing system for replacement animals, but confinement systems are gaining popularity. This study investigates the costs associated with replacement heifer management decisions from birth to calving, considering the factors of housing systems, labor, feed, and health. The objective of this study was to develop an economic model to determine the cost of raising a replacement heifer managed in confinement, dry-lot, and pasture-based scenarios post-weaning. We accounted for variation in feed, labor, and health inputs and quantified the impact of these individual management decisions. An economic simulation with 10,000 iterations were completed for each situation using @Risk and PrecisionTree add-ons (Palisade Corporation, Ithaca, NY) where health incidence, commodity prices, and management variables were made stochastic. Published literature or sample farm data created parameters used in Pert distributions. Costs and biological responses were reflective of published surveys, literature, and market conditions. Management decision inputs had 3 main factors: housing type, ration composition, and labor utilization. Housing systems were calculated separately for confinement, dry-lot, and pasture scenarios. The mean total cost (min, max) to raise a replacement heifer from birth to calving, assuming the same pre-weaning strategy of group housing with an automatic calf feeder, was found to be $1,919.02 ($1,777.25, $2,100.57), $1,593.57 ($1,490.30, $1,737.26), and $1,335.84 ($1,266.69, $1,423.94) for confinement, dry-lot, and pasture, respectively. Total housing cost per replacement heifer was $423.05, $117.96, and $207.96 for confinement, dry-lot, and pasture management systems, respectively. When compared to total cost, housing contributed 21% for confinement, 7% for dry-lot, and 15% for pasture. Upon analysis of all scenarios, utilizing pasture to raise heifers resulted in a lower overall cost when compared to confinement housing options. Percentage breakdowns of feed, labor, housing, and fixed and variable costs provided more information on efficiency rather than total cost, which makes each situation different in relation to on-farm cost. This cost analysis is critical to assisting farms in making decisions in the utilization of their resources for replacement dairy heifers.

27: Costs of delayed vs intraoperative recognition of lower urinary tract injury at the time of laparoscopic hysterectomy

To evaluate the direct medical costs associated with delayed versus immediate recognition of lower urinary tract injury (LUTI) at the time of laparoscopic hysterectomy and estimate the relative costs and savings associated with universal cystoscopy (UC) as compared to selective cystoscopy (SC). A decision tree model was used to estimate the costs associated with delayed vs intraoperative recognition of LUTI at the time of laparoscopic hysterectomy for benign indications. Probability distributions of injury and its associated complications were drawn from published literature. Where multiple published probabilities were available, a sample-size-weighted average was used with a PERT distribution. Healthcare payer costs were drawn from 2016 Centers for Medicare and Medicaid Services national reimbursement schedules and included estimated physician fees, anesthesia fees, facility fees, imaging fees and pathology fees, using Current Procedural Terminology or Diagnosis Related Group codes as appropriate. The payer cost of cystoscopy was estimated using bundled outpatient reimbursement rates, as it is not separately billed at the time of hysterectomy. Total costs were estimated under both SC and UC. SC assumed an average 25% cystoscopy rate based on published literature. A three-way threshold analysis was performed to determine the proportion of LUTI with delayed diagnosis that would need to be diagnosed intraoperatively to favor UC under three scenarios: low injury rates (0.5%; estimated using pooled retrospective and prospective data), high injury rates (4%; estimated using prospective data with universal screening), and moderate injury rates (2.2%). Probabilistic sensitivity analysis was used to assess the robustness of the findings. Given insufficient data, no other healthcare system costs, such as malpractice liability, differential operating room time, or out-of-pocket expenses were included. The SC estimated cost of laparoscopic hysterectomy, inclusive of LUTI complications, ranged from $8,807 to $9,132, while the UC cost ranged from $8,962 to $9,034. Assuming high LUTI rates, UC can be cost saving with ureteral injury detection rates above 50% or bladder injury detection rates above 96%; maximal estimated costs savings were $98 per hysterectomy. UC does not achieve cost savings to the payer assuming low injury rates with a median incremental cost of $143 per hysterectomy (incremental cost to prevent one delayed diagnosis $113,186). Moderate injury rates were estimated to be cost neutral. Estimated UC costs vary based on LUTI rates, and UC policy has the potential to be cost saving through increased intraoperative diagnosis of LUTI. When not cost saving, the incremental costs of UC are modest and may still be outweighed by other health system and malpractice liability costs not accounted for within this model.

Risk Analysis of Project Scheduling Using Microsoft Excel

Scheduling plays a very important role for the successful implementation of construction projects. One of the biggest risks in scheduling is in terms of project costs and duration uncertainty. To anticipate these uncertainties, scheduling methods have been developed using probabilistic duration, including PERT and Monte Carlo Simulation methods. In this research the simulation process was carried out using Microsoft Excel which was integrated with @Risk's add-ins. From the results of the analysis, the project duration obtained from the scheduling model with the CPM method, conventional PERT and Monte Carlo simulation produces an average value that is close to the same. The difference occurs in the standard deviation value, the conventional PERT method produces the smallest standard deviation, followed by a Monte Carlo simulation using the PERT distribution and the last is Monte Carlo simulation using the TRIANGULAR distribution. From the results of the sensitivity analysis, it was found that the dominant input variables that affect the duration of the project are the duration of activities that most often enter the critical path or those that have a great critical index. Meanwhile, project costs are influenced by a combination of the duration of activities on the critical path and activities that have a great direct cost.

The scenario tree epidemiological model in estimation effects of B. melitensis Rev 1 vaccination on disease prevalence

minant brucellosis. The stochastic scenario tree model was used to simulate vaccination and surveillance implementation during 8 years. Outputs were annual proportions of true positive, true negative, false positive, and false negative flocks. Vaccination coverage was described by the Pert distribution with average 70% and min and max 50% and 80%, respectively. Effects of risk factors (high prevalence-areas and transhumance) were considered. The model was separately simulated for three levels of initial average flock prevalence (2%, 5%, and 10%). In the following years, flock prevalence arising from the fitted distribution of the false negative flocks from the previous year increased by the estimated reproductive number. Average within flock prevalence was provided separately for the vaccinated and unvaccinated flocks, indirectly accounting for the vaccine efficacy. Specificity of the diagnostic tests (Rose Bengal plate and Complement fixation) was reduced by 5% for the vaccinated flocks to represent the increased occurrence of false positive results. Each simulation was iterated 1000 times using @Risk, providing average prediction and 5th and 95th percentile of outputs. According to our estimates, only consistent vaccination combined with systematic removal of diseased animals can result in significant reduction of disease prevalence.

Stress Testing the Effect of Income Tax Scale on a Full Time Agricultural Income in Greece After the New Tax Legislation

The main purpose of this paper is to identify and point out changes in the tax burden on farm income given the new tax legislation of 2016 according to which the farm income has the same tax treatment as the rest of the professionals. The paper initially presents the key determinants of tax burden through the latest tax legislation, like the new tax scales, and the relative tax rates of each scale. Furthermore, it presents a theoretical model regarding an indicative Gross Revenue and a Cost Index of a full-time farmer. By choosing to apply Monte Carlo Simulation with the Pert Distribution as analyzed below; we use @ Risk software to stress the tax parameter of Tax Scales, with intervals of ±10%, to identify how the parameter, has influenced the tax burden of farm income. From the findings we can conclude that tax scale has an abnormal effect to the tax burden of the Farm Income as the second and not the third scale has the most significant effect. As tax legislation evolves every one or two years it is important to point out that regarding farmers income the tax scale has a non-gradual – non-linear effect, which must be corrected to be more equal distributed along the income tax scales.

Assessing the social sustainability contribution of an infrastructure project under conditions of uncertainty

Assessing the viability of a public infrastructure includes economic, technical and environmental aspects; however, on many occasions, the social aspects are not always adequately considered. This article proposes a procedure to estimate the social sustainability of infrastructure projects under conditions of uncertainty, based on a multicriteria deterministic method. The variability of the method inputs is contributed by the decision-makers. Uncertain inputs are treated through uniform and beta PERT distributions. The Monte Carlo method is used to propagate uncertainty in the method. A case study of a road infrastructure improvement in El Salvador is used to illustrate this treatment. The main results determine the variability of the short and long-term social improvement indices by infrastructure and the probability of the position in the prioritization of the alternatives. The proposed mechanism improves the reliability of the decision making early in infrastructure projects, taking their social contribution into account. The results can complement environmental and economic sustainability assessments.