A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data

Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the “Lassosum” penalty used to fit polygenic risk scores and integrated risk models using either summary statistics or raw data. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimer’s disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm (for the datasets under consideration), allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.

Risk Management in Company Providing Services on the International Market

Effective and integrated risk management requires integrating the risk management process into the enterprise management process. Each enterprise takes risks to achieve the planned results. The market economy creates both opportunities to achieve the planned profits and the risk of losses as a result of unfavorable changes in the company's environment and errors within the organization. At the time of making a decision, it is never certain how the conditions for the implementation of the planned project will develop in the future. Accounting, which is the most important element of the system, plays an important role in the risk management process information business unit. Nowadays, all business decisions are burdened with risk, which is why organizations more and more often decide to implement a risk management system.

Can machine learning improve carbon storage? Synergies of deep learning, uncertainty quantification and intelligent process control

As we transition from fossil fuel to renewable energy, negative emission technologies, such ascarbon capture and storage (CCS), can help us reduce CO2 emissions. Effective CO2 storage requires: (1) detailed site characterization, (2) regular, integrated risk assessment, and (3) flexible design and operation. We believe that recent advances in machine learning coupled with uncertainty quantification and intelligent process control help us with these task and thus im-prove the efficiency and safety of subsurface CO2 storage.

Assessing the introduction risk of vector-borne animal diseases for the Netherlands using MINTRISK: A Model for INTegrated RISK assessment

To evaluate and compare the risk of emerging vector-borne diseases (VBDs), a Model for INTegrated RISK assessment, MINTRISK, was developed to assess the introduction risk of VBDs for new regions in an objective, transparent and repeatable manner. MINTRISK is a web-based calculation tool, that provides semi-quantitative risk scores that can be used for prioritization purposes. Input into MINTRISK is entered by answering questions regarding entry, transmission, establishment, spread, persistence and impact of a selected VBD. Answers can be chosen from qualitative answer categories with accompanying quantitative explanation to ensure consistent answering. The quantitative information is subsequently used as input for the model calculations to estimate the risk for each individual step in the model and for the summarizing output values (rate of introduction; epidemic size; overall risk). The risk assessor can indicate his uncertainty on each answer, and this is accounted for by Monte Carlo simulation. MINTRISK was used to assess the risk of four VBDs (African horse sickness, epizootic haemorrhagic disease, Rift Valley fever, and West Nile fever) for the Netherlands with the aim to prioritise these diseases for preparedness. Results indicated that the overall risk estimate was very high for all evaluated diseases but epizootic haemorrhagic disease. Uncertainty intervals were, however, wide limiting the options for ranking of the diseases. Risk profiles of the VBDs differed. Whereas all diseases were estimated to have a very high economic impact once introduced, the estimated introduction rates differed from low for Rift Valley fever and epizootic haemorrhagic disease to moderate for African horse sickness and very high for West Nile fever. Entry of infected mosquitoes on board of aircraft was deemed the most likely route of introduction for West Nile fever into the Netherlands, followed by entry of infected migratory birds.

AIRPORT ALoSP AND INTEGRATED RISK APPROACH FOR SSPs

This work proposed the development of a methodology for risk management in airports for ICAO’s State Safety Programmes (SSP). To do so we investigated the nature of the airport safety events occurred in busiest Brazilian airports and proposed a risk index capable to provide the Acceptable Level of Safety Performance (ALoSP) ICAO demands to their signatory States. The approach allowed us to rank airports per their risk in relation to the ALoSP calculated and proposed State optimized regulatory actions to where the risk is. Research found the high concentration on risk recurrence among 31 airports. Only 4 concentrated 30% of all the recurrence of risk, 8 were responsible for 50% and 17 represented approximately 80% of the total risk load in Brazilian airport operations. Five groups of airports are proposed as per their safety performance (Safety+2, Safety+1, Neutral, Safety-1 and Safety-2). Their measured performance showed statistically significant differences. Safety+1 group presented a safety performance 1.49 times better than ALoSP and 3.52 times better than worst group of airports (Safety-2). The Safety+2 group is 3.76 times safer than ALoSP and 8.88 times safer than Safety-2 group. Safety-1 group presented a risk level 1.51 times higher than ALoSP and Safety-2 performed 2.35 times riskier than the ALoSP. This matrix way of problem solving brings more effectiveness and rationality to the SSP’s, amplifying their effectiveness.