Lipoprotein Profile in Populations from Regions of the Russian Federation: ESSE-RF Study

This study aimed to describe the dyslipidemia prevalence and pattern among adult populations from different regions (n = 13) of the Russian Federation (RF). Randomly selected samples (n = 22,258, aged 25–64) were studied according to the ESSE-RF protocol. Lipoprotein parameters were estimated by routine methods. Statistical analyses were performed using R software (v.3.5.1). The overall dyslipidemia prevalence was 76.1% (76.9/75.3% for men/women). In women, total cholesterol (TC) and low-density lipoprotein (LDL)-C levels gradually increased with age (from 4.72 to 5.93 and from 2.76 to 3.79 mmol/L, respectively); in men, they reached a maximum by 45–54 (5.55 and 3.55 mmol/L, respectively) and then decreased. No differences in high-density lipoprotein (HDL)-C in men of different ages were found, but slight decreases in HDL-C and apo AI were observed in women by 55–64 years. No pronounced associations between education and lipid levels in men were observed; higher-educated women showed significantly better lipoprotein profiles. Similar associations between lipids and income level were detected. Women from rural areas had higher TC and triglycerides than urban residents. Regardless of sex, rural residents had higher HDL-C and apo AI, and reduced apo B/apo AI. Conclusion: Information on the peculiarities of dyslipidemia prevalence and lipoprotein profile depending on sex, age, residential place, and socioeconomic status is useful for assessing the global ASCVD risk, and for risk modeling based on national data.

A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model’s validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

A statistics and physics-based tropical cyclone full track model for catastrophe risk modeling

Catastrophe (CAT) risk modeling of perils such as typhoon and earthquake has become a prevailing practice in the insurance and reinsurance industry. The event generation model is the key component of the CAT modeling. In this paper, a physics-based tropical cyclone (TC) full track model is introduced to model typhoons events in the western North Pacific basin. At the same time, a comprehensive test of the model is presented from the perspective of CAT risk modeling for insurance and reinsurance applications. The full track model includes the genesis, track, intensity, and landing models. Driven by the global environmental circulations, the model employs the advection and beta drift theory in atmospheric dynamics to model the track of typhoons. The proposed model is novel in the way of modeling the genesis of TCs with three-dimension kernel distributions in space and time. This enables the simulation of seasonal characteristics of TCs. By generating 10,000-year TC events, we comprehensively test the model from the standpoint of CAT insurance and reinsurance applications. The typhoon hazard model and the generated events can serve as the inputs for assessing the typhoon risk and insured loss caused by winds, rains, floods, and storm surges.

Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.