Revisiting physical distancing threshold in indoor environment using infection-risk-based modeling

Physical distancing has been an important policy to mitigate the spread of the novel coronavirus disease 2019 (COVID-19) in public settings. However, the current 1-2m physical distancing rule is based on the physics of droplet transport and could not directly translate into infection risk. We therefore revisit the 2-m physical distancing rule by developing an infection-risk-based model for human speaking. The key modeling framework components include viral load, droplets dispersion and evaporation, deposition efficiency, viral dose-response rate and infection risk. The results suggest that the one-size-fits-all 2-m physical distancing rule derived from the pure droplet-physics-based model is not applicable under some realistic indoor settings, and may rather increase transmission probability of diseases. Especially, in thermally stratified environments, the infection risk could exhibit multiple peaks for a long distance beyond 2 meters. With Sobol’s sensitivity analysis, most variance of the risk is found to be significantly attributable to the variability in temperature gradient, exposure time and breathing height difference. Our study suggests there is no such magic 2m physical distancing rule for all environments, but it needs to be used alongside other strategies, such as using face cover, reducing exposure time, and controlling the thermal stratification of indoor environment

TOWARDS A HOLISTIC RISK MODEL FOR SAFEGUARDING THE PHARMACEUTICAL SUPPLY CHAIN: CAPTURING THE HUMAN-INDUCED RISK TO DRUG QUALITY

Counterfeit, adulterated, and misbranded medicines in the pharmaceutical supply chain (PSC) are a critical problem. Regulators charged with safeguarding the supply chain are facing shrinking resources for inspections while concurrently facing increasing demands posed by new drug products being manufactured at more sites in the US and abroad. To mitigate risk, the University of Kentucky (UK) Central Pharmacy Drug Quality Study (DQS) tests injectable drugs dispensed within the UK hospital. Using FT-NIR spectrometry coupled with machine learning techniques the team identifies and flags potentially contaminated drugs for further testing and possible removal from the pharmacy. Teams like the DQS are always working with limited equipment, time, and staffing resources. Scanning every vial immediately before use is infeasible and drugs must be prioritized for analysis. A risk scoring system coupled with batch sampling techniques is currently used in the DQS. However, a risk scoring system only allows the team to know about the risks to the PSC today. It doesn’t let us predict what the risks will be in the future. To begin bridging this gap in predictive modeling capabilities the authors assert that models must incorporate the human element. A sister project to the DQS, the Drug Quality Game (DGC), enables humans and all of their unpredictability to be inserted into a virtual PSC. The DQG approach was adopted as a means of capturing human creativity, imagination, and problem-solving skills. Current methods of prioritizing drug scans rely heavily on drug cost, sole-source status, warning letters, equipment and material specifications. However, humans, not machines, commit fraud. Given that even one defective drug product could have catastrophic consequences this project will improve risk-based modeling by equipping future models to identify and incorporate human-induced risks, expanding the overall landscape of risk-based modeling. This exploratory study tested the following hypotheses (1) a useful game system able to simulate real-life humans and their actions in a pharmaceutical manufacturing process can be designed and deployed, (2) there are variables in the game that are predictive of human-induced risks to the PSC, and (3) the game can identify ways in which bad actors can “game the system” (GTS) to produce counterfeit, adulterated, and misbranded drugs. A commercial-off-the-shelf (COTS) game, BigPharma, was used as the basis of a game system able to simulate the human subjects and their actions in a pharmaceutical manufacturing process. BigPharma was selected as it provides a low-cost, time-efficient virtual environment that captures the major elements of a pharmaceutical business- research, marketing, and manufacturing/processing. Running Big Pharma with a Python shell enables researchers to implement specific GxP-related tasks (Good x Practice, where x=Manufacturing, Clinical, Research, etc.) not provided in the COTS BigPharma game. Results from players\u27 interaction with the Python shell/Big Pharma environment suggest that the game can identify both variables predictive of human-induced risks to the PSC and ways in which bad actors may GTS. For example, company profitability emerged as one variable predictive of successful GTS. Player\u27s unethical in-game techniques matched well with observations seen within the DQS

System Safety Modeling of Alternative Geofencing Configurations for small UAS

As is well known, the integration of small Unmanned Aircraft Systems (sUAS) or “drones” into the National Airspace System (NAS) has captured significant industry, academic, regulatory and media attention. For sUAS that typically fly low and slow, the possibility of a mid-air collision with a nearby general aviation aircraft needs to be studied from a system safety perspective to identify possible hazards and to assess mitigations. The Aviation System Risk Model (ASRM) is a first-generation socio-technical model that uses a Bayesian Belief Network (BBN) methodology to integrate possible hazards to assess a non-linear safety risk metric. Using inductive logic, the ASRM may be used to evaluate underlying causal factors linked to the air vehicle and/or to the systems and procedures that lead to the unsafe state and the probabilistic interactions among these factors that contribute to the safety risk. The ASRM can also assess the projected impact of mitigations. Recently, the ASRM has been updated with the use of the Hazard Classification and Analysis System (HCAS) that provides an analytic structure for categorizing hazards related to the UAS, Airmen, Operations and the Environment. In this paper, the ASRM, together with the HCAS, is demonstrated with a notional scenario that involves a sUAS being used for aerial surveillance in the siting of a wind turbine farm near the Yukon River in Alaska. It is conjectured that the sUAS interacts with a general aviation aircraft flying in the nearby vicinity from a local airport. The sUAS being used is a fixed wing-type where there is a failure of the separation assurance function since the sUAS leaves its Area of Operation (AO) due to a Ground Control Station (GCS) transmission disruption (from faulty maintenance) and by the waypoints being incorrectly programmed. In the modeling approach, the time-dependent effects of wind velocity, wind sensor faults, and wind sensor accuracy are also included. In particular, the system safety study focuses on investigating the mitigating efficacy of alternative geofencing configurations

Risk-Based Project Development

Risk management is integral to highway project development. Managing risk entails identifying uncertainties which could influence project activities, understanding how they can be mitigated or eliminated, and monitoring risk during project development. Many state transportation agencies have introduced methods for identifying risks, determining whether risks are high impact or low impact, and generating response strategies. These methods are often qualitative or semi-quantitative in nature due to the challenge of quantifying the likelihood of a risk and its effects. These approaches are nonetheless valuable for helping designers and project development teams remain mindful of negative risks which could pose significant hurdles. Building on recent work for the Kentucky Transportation Cabinet (KYTC) on risk-based construction inspection, this report discusses the creation of an Excel tool for managing risk on highway projects. Leveraging information gathered via interviews with KYTC stakeholders, subject-matter experts, and consultants, the tool identifies risks associated with key decision points and key execution points for four project types: new road and expansion, road rehabilitation and resurfacing, new or replacement bridge, and bridge rehabilitation. Embedded in the tool are high-level discussions of risks often confronted when completing different activities as well as best practices for mitigating or eliminating those risks. The tool has been designed to accommodate periodic updates, which can ensure material reflects the most up-to-date thinking about risk management and recent agency experiences

Karakterizacija šljake i pepela odloženog u Kaštel Gomilici

The objective of this study was to determine the chemical, radiological and leaching properties of slag and ash produced by a thermoelectric unit of a former factory Adriavinil and deposited in the area of Kaštel Gomilica near Split, Croatia. A total of 33 samples were analysed. The bioavailable fraction of the slag and ash was estimated using different leaching tests. The waste material was characterized by a high activity of naturally occurring radionuclides 238U, 235U and 226Ra and by elevated concentrations of heavy metals. The concentrations of most heavy metals were three to four times as high as in the common soil. Uranium slag and ash concentration was almost 40 times higher than in control soil. More than 37 % of the total U could be removed from the slag and ash with the sea water.U radu su kemijski i radiološki okarakterizirani uzorci šljake i pepela. Otpadni materijal je nastao radom termoelektričnog postrojenja bivše tvornice “Adriavinil”, a odložen je u Kaštel Gomilici u Hrvatskoj. Ukupno su analizirana 33 uzorka. Biodostupnost frakcija šljake i pepela određena je različitim testovima za izluživanje. U otpadnome materijalu određena je velika aktivnost radionuklida iz prirodnog niza, 238U, 235U i 226Ra, i povišene koncentracije teških metala. Koncentracije veæine teških metala 3 do 4 puta su veæe nego u kontrolnim uzorcima, dok je koncentracija urana veæa 40 puta. Utvrđeno je da se više od 37 % ukupnog urana iz uzorka može ukloniti izluživanjem u morskoj vodi

A methodological framework to operationalize Climate Risk Management: Managing sovereign climate-related extreme event risk in Austria

Despite considerable uncertainties regarding the exact contribution of anthropogenic climate change to disaster risk, rising losses from extreme events have highlighted the need to comprehensively address climate-related risk. This requires linking climate adaptation to disaster risk management (DRM), leading to what has been broadly referred to as climate risk management (CRM). While this concept has received attention in debate, important gaps remain in terms of operationalizing it with applicable methods and tools for specific risks and decision-contexts. By developing and applying a methodological approach to CRM in the decision context of sovereign risk (flooding) in Austria we test the usefulness of CRM, and based on these insights, inform applications in other decision contexts. Our methodological approach builds on multiple lines of evidence and methods. These comprise of a broad stakeholder engagement process, empirical analysis of public budgets, and risk-focused economic modelling. We find that a CRM framework is able to inform instrumental as well as reflexive and participatory debate in practice. Due to the complex interaction of social-ecological systems with climate risks, and taking into account the likelihood of future contingent climate-related fiscal liabilities increasing substantially as a result of socioeconomic developments and climate change, we identify the need for advanced learning processes and iterative updates of CRM management plans. We suggest that strategies comprising a portfolio of policy measures to reduce and manage climate-related risks are particularly effective if they tailor individual instruments to the specific requirements of different risk layers. (authors' abstract

Understanding Potential GS Risk: A Multi-Disciplinary Framework to Foster Responsible Stewardship

AbstractGeologic sequestration (GS) holds promise as a safe and effective approach for addressing climate change. However, concern about potential “liability” associated with GS often is cited as a significant barrier to project deployment. However, the authors contend that the term “liability” is poorly defined and conflates concerns about the uncertainty in the timing and magnitude of potential damages with the call for long-term stewardship of certified closed sites. This paper offers an analytic framework predicated on the use of risk-based probabilistic modeling to assist stakeholders in evaluating the potential environmental, human health and financial consequences of GS projects. Use of this framework will inform siting decisions for specific GS projects and provide maximum loss values and probabilistic estimates of expected loss values that can inform policy discussions addressing the “liability” issue