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This report contains a description of new methods of fabrication, new equipment, and special tools, with a view to supplying data for design from economical points of view, as well as to stimulate the interest of other factories in improved shop methods.
This paper describes two linked technology development projects to support Space Shuttle ground operations personnel, both during mission preparation analysis and related analyses in missions. The Space Propulsion Robust Analysis Tool (SPRAT) will provide intelligent support and automation for mission analysis setup, interpretation, reporting and documentation. SPRAT models the actions taken by flight support personnel during mission preparation and uses this model to generate an action plan. CONFIG will provide intelligent automation for procedure analyses and failure impact analyses, by simulating the interactions between operations and systems with embedded failures. CONFIG models the actions taken by crew during space vehicle malfunctions and simulates how the planned action sequences in procedures affect a device model. Jointly the SPRAT and CONFIG projects provide an opportunity to investigate how the nature of a task affects the representation of actions, and to determine a more general action representation supporting a broad range of tasks. This paper describes the problems in representing actions for mission preparation and their relation to planning and scheduling.
Since aircraft configuration plays an important role in aerodynamic performance and sonic boom shape, the configuration of the next generation supersonic civil transport has to be tailored to meet high aerodynamic performance and low sonic boom requirements. Computational fluid dynamics (CFD) can be used to design airplanes to meet these dual objectives. The work and results in this report are used to support NASA's High Speed Research Program (HSRP). CFD tools and techniques have been developed for general usages of sonic boom propagation study and aerodynamic design. Parallel to the research effort on sonic boom extrapolation, CFD flow solvers have been coupled with a numeric optimization tool to form a design package for aircraft configuration. This CFD optimization package has been applied to configuration design on a low-boom concept and an oblique all-wing concept. A nonlinear unconstrained optimizer for Parallel Virtual Machine has been developed for aerodynamic design and study.
As advanced composite materials have gained wider usage. the need for analytical models and computer codes to predict the thermomechanical deformation response of these materials has increased significantly. Recently, a micromechanics technique called the generalized method of cells (GMC) has been developed, which has the capability to fulfill this -oal. Tc provide a framework for GMC, the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) has been developed. As MAC/GMC has been updated, significant improvements have been made to the post-processing capabilities of the code. Through the MACPOST program, which operates directly within the MSC/PATRAN graphical pre- and post-processing package, a direct link between the analysis capabilities of MAC/GMC and the post-processing capabilities of MSC/PATRAN has been established. MACPOST has simplified the production, printing. and exportation of results for unit cells analyzed by MAC/GMC. MACPOST allows different micro-level quantities to be plotted quickly and easily in contour plots. In addition, meaningful data for X-Y plots can be examined. MACPOST thus serves as an important analysis and visualization tool for the macro- and micro-level data generated by MAC/GMC. This report serves as the user's manual for the MACPOST program.
This report presents the results of a study, funded by the Federal Aviation Administration (FAA), of transport airplane crash-resistant fuel system (CRFS). The report covers the historical studies related to aircraft crash fires and fuel containment concepts undertaken by the FAA, NASA, and the U.S. Army, which ultimately led to the current state of the art in CRFS technology. It describes the basic research, testing, field investigations and production efforts which have led to the highly successful military CRFS, which has saved many lives and reduced costs of accidents. Current CRFS technology used in transport category airplanes is defined and compared to the available state-of-the-art technology. The report provides information to the FAA and other government organizations which can help them plan their efforts to improve the state of crash fire protection in the transport airplane fleet. The report provides guidance to designers looking for information about CRFS design problems, analysis tools to use for product improvement, and a summary of current and proposed regulations for transport category airplane fuel systems.
The NASA USM3D unstructured flow solver is undergoing extensions to address dynamic flow problems in support of NASA and NAVAIR efforts to study the applicability of Computational Fluid Dynamics tools for the prediction of aircraft stability and control characteristics. The initial extensions reported herein include two second-order time stepping schemes, Detached-Eddy Simulation, and grid motion. This paper reports the initial code verification and validation assessment of the dynamic flow capabilities of USM3D. The cases considered are the classic inviscid shock-tube problem, low Reynolds number wake shedding from a NACA 0012 airfoil, high Reynolds number DES-based wake shedding from a 4-to-1 length-to-diameter cylinder, and forced pitch oscillation of a NACA 0012 airfoil with inviscid and turbulent flow.
This paper will present an overview of efforts to investigate the application of Raman spectroscopy for the characterization of Kevlar materials. Raman spectroscopy is a laser technique that is sensitive to molecular interactions in materials such as Kevlar, graphite and carbon used in composite materials. The overall goal of this research reported here is to evaluate Raman spectroscopy as a potential nondestructive evaluation (NDE) tool for the detection of stress rupture in Kevlar composite over-wrapped pressure vessels (COPVs). Characterization of the Raman spectra of Kevlar yarn and strands will be presented and compared with analytical models provided in the literature. Results of testing to investigate the effects of creep and high-temperature aging on the Raman spectra will be presented.
Spacecraft are subject to micro-meteoroid and orbital debris (MMOD) impact damage which have the potential to degrade performance, shorten the mission, or result in catastrophic loss of the vehicle. Specific MMOD protection requirements are established by NASA for each spacecraft early in the program/project life, to ensure the spacecraft meets desired safety and mission success goals. Both the design and operations influences spacecraft survivability in the MMOD environment, and NASA considers both in meeting MMOD protection requirements. The purpose of this handbook is to provide spacecraft designers and operations personnel with knowledge gained by NASA in implementing effective MMOD protection for the International Space Station, Space Shuttle, and various science spacecraft. It has been drawn from a number of previous publications [10-14], as well as new work. This handbook documents design and operational methods to reduce MMOD risk. In addition, this handbook describes tools and equations needed to design proper MMOD protection. It is a living report, in that it will be updated and re-released periodically in future with additional information. Providing effective and efficient MMOD protection is essential for ensuring safe and successful operations of spacecraft and satellites. A variety of shields protect crew modules, external pressurized vessels and critical equipment from MMOD on the International Space Station (ISS). Certain Space Shuttle Orbiter vehicle systems are hardened from MMOD impact, and operational rules are established to reduce the risk from MMOD (i.e., flight attitudes are selected and late inspection of sensitive thermal protection surfaces are conducted to reduce MMOD impacts). Science spacecraft include specific provisions to meet MMOD protection requirements in their design (for example, Stardust & GLAST). Commercial satellites such as Iridium and Bigelow Aerospace Genesis spacecraft incorporate MMOD protection. The development of low-weight, effective MMOD protection has enabled these spacecraft missions to be performed successfully. This handbook describes these shielding techniques. For future exploration activities to the Moon and Mars, implementing high-performance MMOD shielding will be necessary to meet protection requirements with minimum mass penalty. A current area of technology development in MMOD shielding is the incorporation of sensors to detect and locate MMOD impact damage. Depending on the type of sensor the signals from the sensor can be processed to infer the location of the impact and the extent of damage. The objective of the sensors is to locate critical damage that would endanger the spacecraft or crew immediately or during reentry (such as an air leak from crew module or critical damage to thermal protection system of reentry vehicles). The information from the sensors can then be used with repair kits, patch kits, hatch closure or other appropriate remedial techniques to reduce MMOD risk.
Modeling guidelines constitute one of the fundamental cornerstones for Model Based Development. Their relevance is essential when dealing with code generation in the safety-critical domain. This article presents the experience of a railway signaling systems manufacturer on this issue. Introduction of Model-Based Development (MBD) and code generation in the industrial safety-critical sector created a crucial paradigm shift in the development process of dependable systems. While traditional software development focuses on the code, with MBD practices the focus shifts to model abstractions. The change has fundamental implications for safety-critical systems, which still need to guarantee a high degree of confidence also at code level. Usage of the Simulink/Stateflow platform for modeling, which is a de facto standard in control software development, does not ensure by itself production of high-quality dependable code. This issue has been addressed by companies through the definition of modeling rules imposing restrictions on the usage of design tools components, in order to enable production of qualified code. The MAAB Control Algorithm Modeling Guidelines (MathWorks Automotive Advisory Board)[3] is a well established set of publicly available rules for modeling with Simulink/Stateflow. This set of recommendations has been developed by a group of OEMs and suppliers of the automotive sector with the objective of enforcing and easing the usage of the MathWorks tools within the automotive industry. The guidelines have been published in 2001 and afterwords revisited in 2007 in order to integrate some additional rules developed by the Japanese division of MAAB [5]. The scope of the current edition of the guidelines ranges from model maintainability and readability to code generation issues. The rules are conceived as a reference baseline and therefore they need to be tailored to comply with the characteristics of each industrial context. Customization of these recommendations has been performed for the automotive control systems domain in order to enforce code generation [7]. The MAAB guidelines have been found profitable also in the aerospace/avionics sector [1] and they have been adopted by the MathWorks Aerospace Leadership Council (MALC). General Electric Transportation Systems (GETS) is a well known railway signaling systems manufacturer leading in Automatic Train Protection (ATP) systems technology. Inside an effort of adopting formal methods within its own development process, GETS decided to introduce system modeling by means of the MathWorks tools [2], and in 2008 chose to move to code generation. This article reports the experience performed by GETS in developing its own modeling standard through customizing the MAAB rules for the railway signaling domain and shows the result of this experience with a successful product development story.
Blazars are a subset of active galactic nuclei (AGN) with jets that are oriented along our line of sight. Variability and spectral energy distribution (SED) studies are crucial tools for understanding the physical processes responsible for observed AGN emission. Aims. We report peculiar behavior in the bright gamma-ray blazar PKS 1424-418 and use its strong variability to reveal information about the particle acceleration and interactions in the jet. Methods. Correlation analysis of the extensive optical coverage by the ATOM telescope and nearly continuous -ray coverage by the Fermi Large Area Telescope is combined with broadband, time-dependent modeling of the SED incorporating supplemental information from radio and X-ray observations of this blazar. Results. We analyse in detail four bright phases at optical-GeV energies. These flares of PKS 1424-418 show high correlation between these energy ranges, with the exception of one large optical flare that coincides with relatively low -ray activity. Although the optical/gamma-ray behavior of PKS 1424-418 shows variety, the multiwavelength modeling indicates that these differences can largely be explained by changes in the flux and energy spectrum of the electrons in the jet that are radiating. We find that for all flares the SED is adequately represented by a leptonic model that includes inverse Compton emission from external radiation fields with similar parameters. Conclusions. Detailed studies of individual blazars like PKS 1424-418 during periods of enhanced activity in different wavebands are helping us identify underlying patterns in the physical parameters in this class of AGN.
Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.
To help State Energy Officials and Public Utility Commissions engaging investor-and consumer-owned utilities and the solar industry in topical discussions about their cybersecurity practices and priorities in general, and for interconnected solar systems, specifically, this report outlines discussions prompts for these discussions. By using the questions in this tool states can explore aspects of utilities’ cybersecurity risk management programs that target solar devices, systems, and communication pathways. Such discussions help states identify cybersecurity gaps and assist in emergency response planning, as well as general risk awareness.
NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.
NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.
Integrated energy systems (IESs) are essential for decarbonizing electricity and industrial sectors and fully exploiting these systems requires sophisticated planning, scheduling, and dispatching tools to maximize their socio-economic benefits. The Framework for Optimization of Resources and Economics (FORCE) is a tool suite developed at Idaho National Laboratory and is dedicated to making robust analyses of IESs easier for analysts and researchers. The report summarizes the improvements and updates to FORCE during the most recent development cycle. Particularly, we demonstrate improvements to user accessibility, stochastic time-series analysis, and vertical integration with the software tools provided in FORCE.
Researchers from three NNSA labs plus CEA recently met for a half-day workshop on scientific and engineering workflows in March of 2021. Tools, projects and use cases from each institution were described. A wide range of unique capabilities and requirements were represented. The workshop highlights the fact that the CEA/NNSA workflows space mirrors the broader open science workflows community, with multiple technologies under development in a number of science domains and under a number of funding streams, with differing capabilities and focuses. Despite the number of tools, presentations and discussions have shown that these tools cover specific mission spaces at the four labs, each with distinctive capabilities that do not completely overlap with each other. We believe there is a strong interest in the short term for sharing lessons learned and collaborating on benchmarks and site evaluation of projects. This report, prepared by the NNSA/CEA Workflows Working Group, briefly summarizes the presentations in the areas of domain specific workflows, end user environments, data management, job and resource management, and infrastructure, and then identifies six broad areas for potential collaboration. A key finding is that users could benefit from greater interoperability, compatibility, and composability of the workflow technologies under development and that point-to-point collaboration opportunities should be identified to explore these aspects.
INTRODUCTION Back pain during spaceflight has often been attributed to the lengthening of the spinal column due to the absence of gravity during both short and long-duration missions. Upon landing and re-adaptation to gravity, the spinal column reverts back to its original length thereby causing some individuals to experience pain and muscular spasms, while others experience no ill effects. With International Space Station (ISS) missions, cases of back pain and injury are more common post-flight, but little is known about the potential risk factors. Thus, the purpose of this project was to perform an initial evaluation of reported post-flight back pain and injury cases to relevant spaceflight risk factors in United States astronauts that have completed an ISS mission. METHODS All US astronauts who completed an ISS mission between Expeditions (EXP) 1 and 41 (2000-2015) were included in this evaluation. Forty-five astronauts (36 males and 9 females) completed 50 ISS missions during the study time period, as 5 astronauts completed 2 ISS missions. Researchers queried medical records of the 45 astronauts for occurrences of back pain and injury. A case was defined as any reported event of back pain or injury to the cervical, thoracic, lumbar, sacral, or coccyx spine regions. Data sources for the cases included the Flight Medicine Clinic's electronic medical record; Astronaut Strength, Conditioning and Rehabilitation electronic documentation; the Private Medical Conference tool; and the Space Medicine Operations Team records. Post-flight cases were classified as an early case if reported within 45 days of landing (R + 45) or a late case if reported from R + 46 to R + 365 days after landing (R + 1y). Risk factors in the astronaut population for back pain include age, sex, prior military service, and prior history of back pain. Additionally, spaceflight specific risk factors such as type of landing vehicle and onboard exercise countermeasures were included to evaluate their contribution to post-flight cases. Prior history of back pain included back pain recorded in the medical record within 3 years prior to launch. Landing vehicle was included in the model to discern if more astronauts experienced back pain or injury following a Shuttle or Soyuz landing. Onboard exercise countermeasures were noted for those astronauts who had a mission following 2009 deployment of the Advanced Resistive Exercise Device (aRED) (EXP 19 to 41). T-test and chi-squared tests were performed to evaluate the association between each individual risk factor and post-flight case. Logistic regression was used to evaluate the combined contribution of all the risk factors on post-flight cases. Separate models were calculated for cases reported by R + 45 and R + 1y. RESULTS During the study time period, there were 13 post-flight cases reported by R + 45 and an additional 5 reported by R + 1y. Most of these cases have been reported since EXP 19 with 10 cases by R + 45 and 4 by R + 1y. Individual risk factors of age, sex, landing vehicle, and prior military service were not significantly associated with post-flight cases identified at R + 45 or R + 1y (p greater than 0.05). Having back pain or injury within 3 years prior to launch significantly increased the likelihood of becoming a case by R + 1y (p = 0.041), but not at R+45 (p=0.204). Additionally, astronauts who experienced onboard exercise countermeasures that included aRED had a significantly increased risk of becoming a case at R + 45 (p = 0.024) and R + 1y (p=0.003). Multiple logistic regression evaluating all the risk factors for cases identified no significant risk factors at either the R + 45 or R + 1y time period (p greater than 0.05). Overall model fit was poor for both the R + 45 (R(exp 2) = 0.132) and R + 1y (R(exp 2) = 0.186) cases showing that there are risk factors not represented in our model. CONCLUSIONS Regardless of cause, post-flight cases are reported more often since aRED was deployed in 2009. This may reflect improved documentation or unidentified risk factors. No spaceflight risk factor explains the data fully. Post-flight cases are probably due to multi-faceted factors that are not easily elucidated in the medical data.
Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).