Combined Effect of Temperature and Relative Humidity on the Survival of Salmonella Isolates on Stainless Steel Coupons

The survival on stainless steel of ten Salmonella isolates from food factory, clinical and veterinary sources was investigated. Stainless steel coupons inoculated with Salmonella were dried and stored at a range of temperatures and relative humidity (RH) levels representing factory conditions. Viability was determined from 1 to 22 days. Survival curves obtained for most isolates and storage conditions displayed exponential inactivation described by a log-linear model. Survival was affected by environmental temperatures and RH with decimal reduction times (DRTs) ranging from <1 day to 18 days. At 25 °C/15% RH, all isolates survived at levels of 103 to 105 cfu for >22 days. Furthermore, temperatures and RH independently influenced survival on stainless steel; increasing temperatures between 10 °C and 37 °C and increasing RH levels from 30–70% both decreased the DRT values. Survival curves displaying a shoulder followed by exponential death were obtained for three isolates at 10 °C/70% RH. Inactivation kinetics for these were described by modified Weibull models, suggesting that cumulative injury occurs before cellular inactivation. This study highlights the need to control temperature and RH to limit microbial persistence in the food manufacturing environment, particularly during the factory shut-down period for cleaning when higher temperature/humidity levels could be introduced.

Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China

Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.

The The Beta Reduced Modified Weibull Distribution with Applications to Reliability Data

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Prediction of rail defect development using parametric bootstrapping modified Weibull equations

The railroad industry has historically used the 2-Parameter Weibull equation to determine the rate of rail fatigue defect occurrences and to forecast the fatigue life of railroad rail. However, the 2-Parameter Weibull equation has significant limitations to include inability to analyze segments of track with limited number of rail defects. These limitations are addressed through modification of the traditional 2-Parameter Weibull equation with a novel approach developed from Parametric Bootstrapping. The result is a Parametric Bootstrapping modified Weibull (PBW) forecasting approach. This methodology is applied to rail segments with insufficient numbers of defects to allow for appropriate defect forecasting analysis. Thus, the PBW method provides reasonable estimates of the rate of defects for track segments that have little or no prior defect history. This approach allows for more track to be analyzed and forecasts the probability of rail defect occurrence as a function of key parameters such as cumulative traffic over the rail. A validation of the proposed methodology was performed. Comparison of the output results of over 300,000 track segments with over 200,000 rail defects showed a major improvement in percentage of segments with reasonable Weibull parameters (alpha and beta). This percentage increased from 11% of segments using traditional Weibull analysis to 77% of segments using Parametric Bootstrap modified Weibull approach. These results show that the PBW Analysis approach introduced here offers a more accurate and effective approach to determining the probability of developing future rail defects. This provides a benefit to railroads in planning maintenance of their expensive rail assets.