scholarly journals Optimal Operation of Transient Gas Transport Networks

2021 ◽  
Author(s):  
Kai Hoppmann-Baum ◽  
Felix Hennings ◽  
Ralf Lenz ◽  
Uwe Gotzes ◽  
Nina Heinecke ◽  
...  
Keyword(s):  
Real World ◽  
Gas Transport ◽  
Optimal Operation ◽  
Control Measures ◽  
Transport Networks ◽  
Real World Data ◽  
Time Step ◽  
Industrial Setting ◽  
Technical Control ◽  
Subsequent Step

AbstractIn this paper, we describe an algorithmic framework for the optimal operation of transient gas transport networks consisting of a hierarchical MILP formulation together with a sequential linear programming inspired post-processing routine. Its implementation is part of the KOMPASS decision support system, which is currently used in an industrial setting. Real-world gas transport networks are controlled by operating complex pipeline intersection areas, which comprise multiple compressor units, regulators, and valves. In the following, we introduce the concept of network stations to model them. Thereby, we represent the technical capabilities of a station by hand-tailored artificial arcs and add them to network. Furthermore, we choose from a predefined set of flow directions for each network station and time step, which determines where the gas enters and leaves the station. Additionally, we have to select a supported simple state, which consists of two subsets of artificial arcs: Arcs that must and arcs that cannot be used. The goal is to determine a stable control of the network satisfying all supplies and demands. The pipeline intersections, that are represented by the network stations, were initially built centuries ago. Subsequently, due to updates, changes, and extensions, they evolved into highly complex and involved topologies. To extract their basic properties and to model them using computer-readable and optimizable descriptions took several years of effort. To support the dispatchers in controlling the network, we need to compute a continuously updated list of recommended measures. Our motivation for the model presented here is to make fast decisions on important transient global control parameters, i.e., how to route the flow and where to compress the gas. Detailed continuous and discrete technical control measures realizing them, which take all hardware details into account, are determined in a subsequent step. In this paper, we present computational results from the KOMPASS project using detailed real-world data.

scholarly journals COVID-19 Breakthrough Infections and Transmission Risk: Real-World Data Analyses from Germany’s Largest Public Health Department (Cologne)

Vaccines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1267
Author(s):  
Lea Hsu ◽  
Barbara Grüne ◽  
Michael Buess ◽  
Christine Joisten ◽  
Jan Klobucnik ◽  
...  
Keyword(s):  
Real World ◽  
Threshold Value ◽  
Health Department ◽  
Control Measures ◽  
Transmission Risk ◽  
Control Group ◽  
Public Health Department ◽  
Real World Data ◽  
World Data ◽  
Protection Against Infection

Background and Methods: Vaccination is currently considered the most successful strategy for combating the SARS-CoV-2 virus. According to short-term clinical trials, protection against infection is estimated to reach up to 95% after complete vaccination (≥14 days after receipt of all recommended COVID-19 vaccine doses). Nevertheless, infections despite vaccination, so-called breakthrough infections, are documented. Even though they are more likely to have a milder or even asymptomatic course, the assessment of further transmission is highly relevant for successful containment. Therefore, we calculated the real-world transmission risk from fully vaccinated patients (vaccination group, VG) to their close contacts (CP) compared with the risk from unvaccinated reference persons matched according to age, sex, and virus type (control group = CG) utilizing data from Cologne’s health department. Results: A total of 357 breakthrough infections occurred among Cologne residents between 27 December 2020 (the date of the first vaccination in Cologne) and 6 August 2021. Of the 979 CPs in VG, 99 (10.1%) became infected. In CG, 303 of 802 CPs (37.8%) became infected. Factors promoting transmission included non-vaccinated status (β = 0.237; p < 0.001), male sex (β = 0.079; p = 0.049), the presence of symptoms (β = −0.125; p = 0.005), and lower cycle threshold value (β = −0.125; p = 0.032). This model explained 14.0% of the variance (corr. R2). Conclusion: The number of transmissions from unvaccinated controls was three times higher than from fully vaccinated patients. These real-world data underscore the importance of vaccination in enabling the relaxation of stringent and restrictive general pandemic control measures.

scholarly journals Border control and SARS-CoV-2: an opportunity for generating highly policy-relevant, real-world evidence

2021 ◽  
Author(s):  
Jacob Burns ◽  
Ani Movsisyan ◽  
Eva A Rehfuess ◽  
Jan M Stratil
Keyword(s):  
Real World ◽  
Evidence Base ◽  
Control Measures ◽  
Border Control ◽  
Combined Effects ◽  
Repeated Testing ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence ◽  
Study Type

We propose a study type that would contribute to the evidence base related to border control measures. Over a study period during which arriving travellers are quarantined, repeated testing and/or screening at regular intervals would provide real-world data on the relative and combined effects of various screening and testing measures.

scholarly journals Dynamics of individual adherence to mass drug administration in a conditional probability model

2020 ◽  
Author(s):  
Robert J. Hardwick ◽  
James E. Truscott ◽  
William E. Oswald ◽  
Marleen Werkman ◽  
Katherine E. Halliday ◽  
...  
Keyword(s):  
Drug Administration ◽  
Mass Drug Administration ◽  
Real World ◽  
Disease Transmission ◽  
Neglected Tropical Diseases ◽  
Probability Model ◽  
Control Measures ◽  
Real World Data ◽  
Adherence Behaviour ◽  
The Impact

AbstractWe present a comprehensive framework which describes the systematic (binary) choice of individuals to either take treatment, or not for any reason, over the course of multiple rounds of mass drug administration (MDA) — which we here here refer to as ‘adherence’ and ‘non-adherence’. This methodology can be fitted to (or informed by) program data as well as manipulated to reproduce the same adherence behaviours of past analyses, and can go beyond past analyses to describe new behaviours that have yet to be considered in the literature. Our model also has a straightforward interpretation and implementation in simulations of mass drug trials for disease transmission studies and forecasts for control through MDA. We demonstrate how our analysis may be implemented to statistically infer adherence behaviour from a dataset by applying our approach to the recent adherence data from the TUMIKIA project, a recent trial of deworming strategies in Kenya. We stratify our analysis according to age and sex, though the framework which we introduce here may be readily adapted to accomodate other categories. Our findings include the detection of past behaviour dependent non-adherence in all age groups to varying degrees of severity and particularly strong non-adherent behaviour of men of ages 30+. We then demonstrate the use of our model in stochastic individual-based simulations by running two example forecasts for elimination in TUMIKIA with the learned adherence behaviour implemented. Our results demonstrate the impact and utility of including non-adherence from real world datasets in simulations.Author summaryMass drug administration (MDA) is an important tool in prevention of morbidity from various neglected tropical diseases (NTDs). Due to a variety of social and medical reasons, many people will either not be offered or refuse such treatment, and if this behaviour is recurring then control measures may face a challenge to achieving their stated goals. Learning the patterns of individual adherence or non-adherence to MDA control measures for NTDs from real world data followed by their implementation in simulated scenarios is a relatively recent development in the study of NTDs. Past analyses assessing individual adherence have informed the approach we take in this work. However, we have sought to provide a framework which encapsulates as many types of adherence behaviour as possible so that their implementation in modern simulations is streamlined effectively. Our example application to the TUMIKIA data highlights the importance of such a general framework as we find past behaviour dependence that may have been missed by other methods.

scholarly journals Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2340 ◽  
Author(s):  
Teresa Pamuła ◽  
Wiesław Pamuła
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Real World ◽  
Public Transport ◽  
Urban Transport ◽  
Transport Networks ◽  
Real World Data ◽  
World Data ◽  
Study Results ◽  
Bus Stops

The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data

Safety and efficacy of direct acting oral anticoagulants and vitamin K antagonists in nonvalvular atrial fibrillation – a network meta-analysis of real-world data

VASA ◽  
2019 ◽  
Vol 48 (2) ◽  
pp. 134-147 ◽  
Author(s):  
Mirko Hirschl ◽  
Michael Kundi
Keyword(s):  
Real World ◽  
Meta Analysis ◽  
Vitamin K Antagonists ◽  
Oral Anticoagulants ◽  
Efficacy And Safety ◽  
Nonvalvular Atrial Fibrillation ◽  
Real World Data ◽  
Hazard Ratios ◽  
Direct Acting ◽  
Event Rates

Abstract. Background: In randomized controlled trials (RCTs) direct acting oral anticoagulants (DOACs) showed a superior risk-benefit profile in comparison to vitamin K antagonists (VKAs) for patients with nonvalvular atrial fibrillation. Patients enrolled in such studies do not necessarily reflect the whole target population treated in real-world practice. Materials and methods: By a systematic literature search, 88 studies including 3,351,628 patients providing over 2.9 million patient-years of follow-up were identified. Hazard ratios and event-rates for the main efficacy and safety outcomes were extracted and the results for DOACs and VKAs combined by network meta-analysis. In addition, meta-regression was performed to identify factors responsible for heterogeneity across studies. Results: For stroke and systemic embolism as well as for major bleeding and intracranial bleeding real-world studies gave virtually the same result as RCTs with higher efficacy and lower major bleeding risk (for dabigatran and apixaban) and lower risk of intracranial bleeding (all DOACs) compared to VKAs. Results for gastrointestinal bleeding were consistently better for DOACs and hazard ratios of myocardial infarction were significantly lower in real-world for dabigatran and apixaban compared to RCTs. By a ranking analysis we found that apixaban is the safest anticoagulant drug, while rivaroxaban closely followed by dabigatran are the most efficacious. Risk of bias and heterogeneity was assessed and had little impact on the overall results. Analysis of effect modification could guide the clinical decision as no single DOAC was superior/inferior to the others under all conditions. Conclusions: DOACs were at least as efficacious as VKAs. In terms of safety endpoints, DOACs performed better under real-world conditions than in RCTs. The current real-world data showed that differences in efficacy and safety, despite generally low event rates, exist between DOACs. Knowledge about these differences in performance can contribute to a more personalized medicine.

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