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The increasing digitization of the healthcare system is leading to a growing volume of health data. Leveraging this data beyond its initial collection purpose for secondary use can provide valuable insights into diagnostics, treatment processes, and the quality of care. The Health Data Lab (HDL) will provide infrastructure for this purpose. Both the protection of patient privacy and optimal analytical capabilities are of central importance in this context, and artificial intelligence (AI) provides two opportunities. First, it enables the analysis of large volumes of data with flexible models, which means that hidden correlations and patterns can be discovered. Second, synthetic - that is, artificial - data generated by AI can protect privacy.This paper describes the KI-FDZ project, which aims to investigate innovative technologies that can support the secure provision of health data for secondary research purposes. A multi-layered approach is investigated in which data-level measures can be combined in different ways with processing in secure environments. To this end, anonymization and synthetization methods, among others, are evaluated based on two concrete application examples. Moreover, it is examined how the creation of machine learning pipelines and the execution of AI algorithms can be supported in secure processing environments. Preliminary results indicate that this approach can achieve a high level of protection while maintaining data validity. The approach investigated in the project can be an important building block in the secure secondary use of health data., (© 2024. The Author(s).)

Recent research has shown an indirect effect of generativity on fear of death through ego-integrity in older adults. The present paper aims at demonstrating that the indirect effect is valid even when controlling for social desirability. For that purpose, participants (N = 260 German adults) in study 1 provided self-reports on generativity, ego-integrity, fear of death, and social desirability. Analyses confirmed the indirect effect when the tendency for socially desirable responding was statistically controlled. In study 2, participants (N = 133 German adults) also reported on their generativity and ego-integrity. Fear of death, however, was assessed with a reaction time-based measure (i.e., the Implicit Associations Test). Again, the indirect effect could be confirmed. Taken together, the studies lend further credibility to the extant findings on the indirect effect of generativity on fear of death through ego-integrity. [ABSTRACT FROM AUTHOR]

The article presents a study which investigates the development of an algorithm to retrieve atmospheric water vapour column from observations in Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS). The study has applied the algorithm to observations at the Mainz, Germany-based Max Planck Institute for Chemistry from March to August 2011. Study reveals that the retrieval of vertical column density (VCDs) of water in cloudy conditions serves as the algorithm's important advantage.

Assessing the impact of dysarthria on a patient's ability to communicate should be an integral part of patient management. However, due to the high demands on reliable quantification of communication limitations, hardly any formal clinical tests with approved psychometric properties have been developed so far. This study investigates a web-based assessment of communication impairment in dysarthria, named KommPaS. The test comprises measures of intelligibility, naturalness, perceived listener effort and communication efficiency, as well as a total score that integrates these parameters. The approach is characterized by a quasi-random access to a large inventory of test materials and to a large group of naïve listeners, recruited via crowdsourcing. As part of a larger research program to establish the clinical applicability of this new approach, the present paper focuses on two psychometric issues, namely specificity and sensitivity (study 1) and retest-reliability (study 2). Study 1: KommPaS was administered to 54 healthy adults and 100 adult persons with dysarthria (PWD). Non-parametric criterion-based norms (specificity: 0.95) were used to derive a standard metric for each of the four component variables, and corresponding sensitivity values for the presence of dysarthria were identified. Overall classification accuracy of the total score was determined using a ROC analysis. The resulting cutscores showed a high accuracy in the separation of PWD from healthy speakers for the naturalness and the total score. Study 2: A sub-group of 20 PWD enrolled in study 1 were administered a second KommPaS examination. ICC analyses revealed good to excellent retest reliabilities for all parameters. [ABSTRACT FROM AUTHOR]

Background: Previous research has shown that teachers hold misconceptions about multimedia learning (e.g., multimedia instruction needs to be adapted to students' learning styles), which may be at odds with evidence‐based teaching. Objectives: Refutation texts are a classical method to reduce misconceptions and thus to stimulate conceptual change. We wanted to know whether making use of a computer algorithm to personalize refutation texts would best initiate teachers' conceptual change. Methods: We designed an online experiment, in which N = 129 in‐service teachers read either (1) expository texts (without direct refutation), (2) common refutation texts, or (3) personalized refutation texts. The teachers filled in a misconception questionnaire pre and post to assess their conceptual change. Results and Conclusions: Statistical analyses revealed that personalized refutation texts initiated the strongest conceptual change, which was driven by increased feelings of guilt and shame. Common refutation texts did not foster teachers' conceptual change as compared to expository texts. These findings indicate that refutation texts should be personalized for experienced practitioners such as teachers. Takeaways: Personalized refutation seems to be promising in the context of online teacher training programs. Further research should test to which extent the present findings also apply to other groups of experienced learners or practitioners. Lay Description: What is already known about this topic?: Teachers hold misconceptions about multimedia learning (e.g., learning materials should be adapted to students' individual learning styles, such as visualizers or verbalizers).Refutation texts, naming a commonly held misconception, disproving it and introducing a scientific explanation, are a common means to reduce misconceptions.Personalization fosters learning by drawing the learner's attention toward the discrepancy between their own beliefs and the learning material, further creating an impasse experience.Said impasse experience may trigger teachers' conceptual change, as, for teachers' conceptual change, a certain degree of discomfort is required. Yet, anger, caused by lessoning teachers on their topic may cause repulse and hamper learning. What this paper adds?: With a computer algorithm, we can efficiently personalize refutation texts by automatically matching them to teachers' answers in a pre‐test. Such a personalized refutation instruction may especially foster conceptual change.Within a randomized experiment, the personalized refutation instruction worked best compared to common refutation texts and expository texts.Feelings of guilt and shame moderated the effect of a personalized refutation, as teachers felt more addressed in their misconceptions and thus experienced the required impasse experience.Feelings of anger did not play an important role within our experiment. The implications of study findings for practitioners: Computer algorithms enable efficient personalization of instruction to better deal with heterogeneous groups of learners (e.g., with big differences in prior knowledge or experience, such as in the case of in‐service teachers).Refutation texts work better for teachers when they are personalized. Common refutation texts do not work better than expository texts.An advantage of digital instruction is the use of algorithms to efficiently personalize instructions even for larger groups. [ABSTRACT FROM AUTHOR]

During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge. [ABSTRACT FROM AUTHOR]

As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks' layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision.

Classification of outdoor point clouds is an intensely studied topic, particularly with respect to the separation of vegetation from the terrain and manmade structures. In the presence of many overhanging and vertical structures, the (relative) height is no longer a reliable criterion for such a separation. An alternative would be to apply supervised classification; however, thousands of examples are typically required for appropriate training. In this paper, an unsupervised and rotation-invariant method is presented and evaluated for three datasets with very different characteristics. The method allows us to detect planar patches by filtering and clustering so-called superpoints, whereby the well-known but suitably modified random sampling and consensus (RANSAC) approach plays a key role for plane estimation in outlier-rich data. The performance of our method is compared to that produced by supervised classifiers common for remote sensing settings: random forest as learner and feature sets for point cloud processing, like covariance-based features or point descriptors. It is shown that for point clouds resulting from airborne laser scans, the detection accuracy of the proposed method is over 96% and, as such, higher than that of standard supervised classification approaches. Because of artifacts caused by interpolation during 3D stereo matching, the overall accuracy was lower for photogrammetric point clouds (74-77%). However, using additional salient features, such as the normalized green-red difference index, the results became more accurate and less dependent on the data source.

Management of reservoir systems is a complicated process involving many uncertainties regarding future events and the diversity of purposes these reservoirs serve; therefore, an effective management of these systems could help improve resource utilization and avoid stakeholder disputes. The aim of this paper was to build an optimization-simulation framework based on implicit stochastic optimization (ISO), genetic algorithms (GA), and recurrent neural network (RNN) for addressing the issue of reservoir operation. Inflow scenarios were generated synthetically based on a monthly scale to be used as an input to a multi-objective genetic programming model to construct an optimal operating rules database. Such database was subsequently used simultaneously with the output of the inflow forecasting model to simulate monthly reservoir hedging rules using RNN. Our results demonstrate the effectiveness of the GA-ISO-RNN model for simulating and predicting optimal reservoir release with consistent accuracy. Results from both the training and testing phases clearly proved the usefulness of RNN in predicting optimal reservoir release with relatively higher values of the Nash-Sutcliffe model efficiency coefficient, correlation coefficient, and lower values of root mean squared error and mean absolute deviation. Furthermore, by comparing the historical releases and the output of the proposed model, the results show that the proposed model was less vulnerable than standard operating rules. The proposed methodology was applied to the Bigge reservoir in Germany, as it features an extensive management infrastructure, but this methodology can also be easily adopted in other similar cases.

Grammatical knowledge has often been investigated in wug tests, in which participants inflect pseudo-words. It was argued that in inflecting these pseudo-words, speakers apply their knowledge of word formation. However, it remains unclear what exactly this knowledge is and how it is learned. According to one theory, the knowledge is best characterised as abstractions that specify how units are combined. Another theory maintains that it is best characterised by memory-based analogy. In both cases the knowledge is learned by association based on positive evidence alone. In this paper, we model the classification of pseudo-words to Maltese plurals using a shallow neural network trained with an error-driven learning algorithm. We demonstrate that the classifications mirror those of Maltese speakers in a wug test. Our results indicate that speakers rely on gradient knowledge of a relation between the phonetics of whole words and plural classes, which is learned in an error-driven way. [ABSTRACT FROM AUTHOR]

In Part I of this two-part paper a new method of predicting the total lightning flash rate in thunderstorms was introduced. In this paper, the implementation of this method into the convection-permitting Consortium for Small Scale Modeling (COSMO) model is presented. The new approach is based on a simple theoretical model that consists of a dipole charge structure, which is maintained by a generator current and discharged by lightning and, to a small extent, by a leakage current. This approach yields a set of four predictor variables, which are not amenable to direct observations and consequently need to be parameterized (Part I). Using an algorithm that identifies thunderstorm cells and their properties, this approach is applied to determine the flash frequency of every thunderstorm cell in the model domain. With this information, the number of flashes that are accumulated by each cell and during the interval between the activation of the lightning scheme can be calculated. These flashes are then randomly distributed in time and beneath each cell. The output contains the longitude, the latitude, and the time of occurrence of each simulated discharge. Simulations of real-world scenarios are presented, which are compared to measurements with the lightning detection network, LINET. These comparisons are done on the cloud scale as well as in a mesoscale region composing southern Germany (two cases each). The flash rates of individual cumulonimbus clouds at the extreme ends of the intensity spectrum are realistically simulated. The simulated overall lightning activity over southern Germany is dominated by spatiotemporal displacements of the modeled convective clouds, although the scheme generally reproduces realistic patterns such as coherent lightning swaths. [ABSTRACT FROM AUTHOR]

The impact of algorithms on everyday life is ever increasing. Medicine and public health are not excluded from this development – algorithms in medicine do not only challenge, change and inform research (methods) but also clinical situations. Given this development, questions arise concerning the competency level of prospective physicians, thus medical students, on algorithm related topics. This paper, based on a master's thesis in library and information science written at Humboldt‐Universität zu Berlin, gives an insight into this topic by presenting and analysing the results of a knowledge test conducted among medical students in Germany. F. J. [ABSTRACT FROM AUTHOR]

The paper explores the possibilities offered by evolutionary multi-objective algorithms in structural design. The developed procedure includes the parameterization of a 3D structure, the automatic generation of a finite element (FE) model, and the optimum design. The search of the optimum solution is dealt with using two commercial software packages: Straus7, for FE analysis, and modeFrontier, for optimal search, interlinked by a program purposely developed (SCU, system control unit). The optimum design of a structure needs a different approach with respect to the traditional design. The paper presents a possible approach and describes more in detail the data pre and post processing including the choice of the solutions on the Pareto's front. A case study, a steel-glass vault roof built in Badenweiler (Germany), is used to demonstrate the effectiveness of the proposed approach. A significant economical saving can be achieved when the optimum design solution is used. The proposed algorithm can then be employed as a useful tool to help the designer in the complex design process. [ABSTRACT FROM AUTHOR]

Presents information on the Symposium on Geometry Processing in Aachen, Germany on June 23 to 25, 2003. Information on the symposium's Web site; Details on algorithms presented by speakers S. Oudot and J.-D. Boissonat; Papers on mesh compression featured in the event.

Background: Low back pain is a common problem for primary care providers, outpatient clinics and A&E departments. The predominant symptoms are those of so-called "unspecific back pain", but serious pathologies can be concealed by the clinical signs. Especially less experienced colleagues have problems in treating these patients, as - despite the multitude of recommendations and guidelines - there is no generally accepted algorithm., Methods: After a literature search (Medline/Cochrane), 158 articles were selected from 15,000 papers and classified according to their level of evidence. These were attuned to the clinical guidelines of the orthopaedic and pain-physician associations in Europe, North America and overseas and the experience of specialists at LMU Munich, in order to achieve consistency with literature recommendations, as well as feasibility in everyday clinical work and optimised with practical relevance., Results: An algorithm was formed to provide the crucial differential diagnosis of lumbar back pain according to its clinical relevance and to provide a plan of action offering reasonable diagnostic and therapeutic steps. As a consequence of distinct binary decisions, low back patients should be treated at any given time according to the guidelines, with emergencies detected, unnecessary diagnostic testing and interventions averted and reasonable treatment initiated pursuant to the underlying pathology., Conclusion: In the context of the available evidence, a clinical algorithm has been developed that translates the complex diagnostic testing of acute low back pain into a transparent, structured and systematic guideline., Competing Interests: Die Autoren geben an, dass kein Interessenkonflikt besteht., (Georg Thieme Verlag KG Stuttgart · New York.)

Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. Implications for research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system. [ABSTRACT FROM AUTHOR]

One major criticism against the use of synthetic data has been that the efforts necessary to generate useful synthetic data are so intense that many statistical agencies cannot afford them. We argue many lessons in this evolving field have been learned in the early years of synthetic data generation, and can be used in the development of new synthetic data products, considerably reducing the required investments. The final goal of the project described in this paper will be to evaluate whether synthetic data algorithms developed in the U.S. to generate a synthetic version of the Longitudinal Business Database (LBD) can easily be transferred to generate a similar data product for other countries. We construct a German data product with information comparable to the LBD - the German Longitudinal Business Database (GLBD) - that is generated from different administrative sources at the Institute for Employment Research, Germany. In a future step, the algorithms developed for the synthesis of the LBD will be applied to the GLBD. Extensive evaluations will illustrate whether the algorithms provide useful synthetic data without further adjustment. The ultimate goal of the project is to provide access to multiple synthetic datasets similar to the SynLBD at Cornell to enable comparative studies between countries. The Synthetic GLBD is a first step towards that goal. [ABSTRACT FROM AUTHOR]

Background: Staffing ratios in nursing homes vary among the federal states of Germany, but there are no rational grounds for these variations. In a previous study, a new instrument for the standardized calculation of staffing requirements in nursing homes was developed (Algorithm1.0). The development was based on a new empirical data collection method that derives actual and target values for the time and number of care interventions provided. Algorithm1.0 found an increased requirement of 36% of staff in German nursing homes. Based on these results, the German legislature has commissioned a model program to trial and evaluate a complex intervention comprising increased staffing combined with strategies for organizational development. Methods: The mixed-methods study consists of (i) developing a concept for restructuring the work organization, (ii) the application of this concept combined with increased staffing in 10 nursing homes (complex intervention), and the further development of the concept using a participatory and iterative formal evaluation process. The intervention consists of (a) quantitative measures of increased staffing based on a calculation using Algorithm1.0 and (b) qualitative measures regarding organizational development. The intervention will be conducted over one year. The effects of the intervention on job satisfaction and quality of care will be evaluated in (iii) a comprehensive prospective, controlled summative evaluation. The results will be compared with ten matched nursing homes as a control group. Finally, (iv) prototypical concepts for qualification-oriented work organization, a strategy for the national rollout, and the further development of Algorithm1.0 into Algorithm 2.0 will be derived. Discussion: In Germany, there is an ongoing dynamic legislation process regarding further developing the long-term care sector. The study, which is the subject of the study protocol presented here, generates an evidence-based strategy for the staffing requirements for nursing homes. Ethics and dissemination. This study was approved by the Ethics Committee of the German Association of Nursing Science (Deutsche Gesellschaft für Pflegewissenschaft) on 02.08.2023 (amended on 20.09.2023). Research findings are disseminated through presentations at national and international conferences and publications in peer-reviewed scientific journals. Trial registration number: German Clinical Trails Register DRKS00031773 (Date of registration 09.11.2023). [ABSTRACT FROM AUTHOR]

Background: Early recognition and management of trauma related coagulopathy improves the outcome. Trauma facilities should implement an algorithm to identify the bleeding trauma patient with coagulopathy. Objective: The scope of the paper is to identify the indicators of early coagulopathy and to optimize the indications for thromboelastometry and coagulation support. Design: Cohort study based on data from trauma registry. Setting: Data of 493 major trauma patients treated in GH Celje from 2006 to 2014 were included into The TraumaRegister DGU (TR-DGU). Patients: Patients were selected for inclusion into TR-DGU according to the following criteria: polytraumatized patients with Injury severity score (ISS) ≥ 18, patients with injuries to single region with AIS 5, patients with major injuries to a single region and abnormal vital signs. All patients that were dead on arrival to hospital, patients presented to hospital >24 h after the injury, and head injuries that occurred with a low energy mechanism in patients on anticoagulation drugs were excluded. Measurements: Two groups were formed (with or without coagulopathy). Mortality, morbidity, length of mechanical ventilation, ICU and hospital stay were used as outcome and compared between the groups. A coagulopathy prediction model (CPM) was developed to identify the patients who were at high risk of coagulopathy. Results: Coagulopathy was present in 51 % of patients. Severe injuries to the torso and limbs, infusion of >1000 ml of fluids in the prehospital settings, and hypotension were included into CPM. If all three criteria were present, the sensitivity of the model to predict coagulopathy was 93 %. By adding the blood gas analysis (BE ≤ −5), the specificity increased to 81.7 %. Limitations: Shortcomings of our analysis are mainly related to the quality of data in the registry that may not be comparable to a clinical trial where data are collected specifically to address a given issue. Conclusions: The Criteria for activation of coagulation support treatment remain centre dependent. In our settings the CPM is the tool to select patients for ROTEM analysis. By adding data from blood gas analysis, treatment of coagulopathy is justifiable before complete test results are available. [ABSTRACT FROM AUTHOR]

This paper introduces an approach for generating scenarios of sea port container terminals. The scenarios can be used as input data for simulation models. Furthermore, they can be employed as test data for algorithms to solve optimization problems in container terminal logistics such as berth planning and crane scheduling. A scenario consists of arrivals of deep sea vessels, feeder ships, trains, and trucks together with lists of containers to be loaded and unloaded. Moreover, container attributes such as size, empty, reefer, weight. and destination are included. The generator is based on a large number of parameters that allow the user to produce realistic scenarios of any size. The purpose of this paper is to outline the parameters that are important to produce realistic scenarios of high practical relevance and to propose an algorithm that computes scenarios on the basis of these parameters. The generator discussed here has been developed within the simulation project at the HHLA Container-Terminal Altenwerder in Hamburg, Germany. Nevertheless, its structure is general enough to be applied to any other terminal as well. [ABSTRACT FROM AUTHOR]

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field. [ABSTRACT FROM AUTHOR]

Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF. [ABSTRACT FROM AUTHOR]

Clearance monitor systems are used for gross gamma measurements of waste potentially contaminated with radioactivity. These measurements are to make sure that legal requirements, e.g. clearance criteria according to the german radiation protection ordinance, are met. This means that measurement results may overestimate, but must not underestimate the true values. This paper describes a pragmatic way using a calibrated Cs-137 point source to generate a conservative calibration for the clearance monitor system used in the Medizinische Hochschule Hannover (MHH). The most important nuclides used in nuclear medicine are considered. The measurement result reliably overestimates the true value of the activity present in the waste. The calibration is compliant with the demands for conservativity and traceability to national standards., (Copyright © 2014. Published by Elsevier GmbH.)

The German healthcare system offers comprehensive coverage for people with mental illness including inpatient, day hospital and outpatient services. These services are primarily financed through the statutory health and pension insurances. According to legal regulations, providers are required to base their services on current scientific evidence and to continuously assure the quality of their services. This paper gives an overview of recent initiatives to develop, evaluate and disseminate routine outcome measurement (ROM) in service settings in Germany. A large number of projects have shown outcome monitoring to be feasible, and that feedback of outcome may enhance routine care through an improved allocation of treatment resources. However, none of these initiatives have been integrated into routine care on a nationwide or trans-sectoral level, and their sustainability has been limited. This is due to various barriers in a fragmented mental health service system and to the lack of coordinated national or state-level service planning. The time is ripe for a concerted effort including policy-makers to pick up on these initiatives and move them towards wide-spread implementation in routine care accompanied by practice-oriented research including service user involvement. [ABSTRACT FROM AUTHOR]

Sarcoidosis is a multisystemic granulomatous disorder which affects the respiratory system in the majority of the cases. Cardiac manifestations are found in up to 10 % of the affected cohort and show a large heterogeneity based on the ethnic background. Cardiac sarcoidosis are not only found in patients with rhythmogenic heart disease such as atrial and ventricular fibrillation but also in all phenotypes of cardiomyopathies. The overall morbidity and mortality caused by cardiac sarcoidois in Germany is unclear and no large prospective international studies are published on this topic. This consensus paper on diagnostic and therapeutic algorithms in cardiac sarcoidosis is based on a current literature search and forms a expert opinion statement under the hospices of the "Deutsche Gesellschaft für Pneumologie" and "Deutsche Gesellschaft für Kardiologie". It is the rationale of this statement to offer algorithms to facilitate clinical decision-making based on the individual case., (© Georg Thieme Verlag KG Stuttgart · New York.)

Simulation studies are conducted to assess the performance of current and novel statistical models in pre-defined scenarios. It is often desirable that chosen simulation scenarios accurately reflect a biologically plausible underlying distribution. This is particularly important in the framework of survival analysis, where simulated distributions are chosen for both the event time and the censoring time. This paper develops methods for using complex distributions when generating survival times to assess methods in practice. We describe a general algorithm involving numerical integration and root-finding techniques to generate survival times from a variety of complex parametric distributions, incorporating any combination of time-dependent effects, time-varying covariates, delayed entry, random effects and covariates measured with error. User-friendly Stata software is provided., (Copyright © 2013 John Wiley & Sons, Ltd.)

In March 1964, the cover page of a popular German weekly magazine entitled Der Spiegel painted a frightening picture: An anthropomorphic robot with six mechanical arms commands an assembly line while a displaced human worker floats aimlessly in the foreground. Ejected from his station, the worker throws up his hands in despair next to a headline that reads, "Automation in Germany, the arrival of robots." Over fifty years later, a cover page from the same magazine evoked similar themes: A giant robot arm yanks an office worker away from his computer under the headline, "You're fired! How computers and robots steal our jobs - and which jobs will be safe." The more things change, the more they stay the same. [ABSTRACT FROM AUTHOR]

Within the framework of restructuring for the certification to a regional trauma centre of the DGU (German Society for Casualty Surgery), a uniform algorithm for multiple trauma was developed in the medical centre of Wolfsburg. The Wolfsburg multiple trauma algorithm is based on ATLS (advanced trauma life support) with integration of FAST (focused assessment with sonography for trauma), as well as the white paper of the DGU and regional-specific features. Thus structural, instrumental, organizational and personnel conditions were created to improve the care of multiply traumatized patients even further. The conditions for transition to a regional trauma centre of the DGU were confirmed by a successful audit.

Online-monitoring systems in intensive care are affected by a high rate of false threshold alarms. These are caused by irrelevant noise and outliers in the measured time series data. The high false alarm rates can be lowered by separating relevant signals from noise and outliers online, in such a way that signal estimations, instead of raw measurements, are compared to the alarm limits. This paper presents a clinical validation study for two recently developed online signal filters. The filters are based on robust repeated median regression in moving windows of varying width. Validation is done offline using a large annotated reference database. The performance criteria are sensitivity and the proportion of false alarms suppressed by the signal filters.

In the framework of an epidemiological study, dosemeters were used for the assessment of radio frequency electromagnetic field exposure. To check the correct dosemeter's performance in terms of consistency of recorded field values over the entire study period, a quality control strategy was developed. In this paper, the concept of quality control and its results is described. From the 20 dosemeters used, 19 were very stable and reproducible, with deviations of a maximum of +/-1 dB compared with their initial state. One device was found to be faulty and its measurement data had to be excluded from the analysis. As a result of continuous quality control procedures, the confidence in the measurements obtained during the field work was strengthened significantly.

Introduction: Multidisciplinary medical management of osteoporosis and osteoporosis-related fractures is still an important treatment issue today. In view of ethiopathology of osteoporosis and the future demographic development an increasing socioeconomic burden has to be estimated. A prerequisite for an effective secondary prophylaxis of osteoporotic fractures is the implementation of a treatment network, with inclusion of all partners involved in patient's care. Therefore, special attention should be paid to formation and establishment of centres with multidisciplinary and integrated treatment concepts. This paper outlines the concept of a clinical centre for diagnosis and therapy of osteoporosis established 4 years ago. Furthermore, a concept of integrated care of osteoporosis-related fractures is introduced and the obtained data of a 2-year follow-up analysis will be presented., Methods: The establishment of an osteoporosis centre at a university teaching hospital as well as certification according to the Dachverband Osteologie (DVO) guidelines were necessary. Recruitment of contract partners on both sides, health insurances and outpatient general practitioners as well as specialist doctors, was also essential. The implementation of an osteoporosis coordinator was a step to put the treatment concept into practice., Results and Discussion: Based on the recommendations of DVO guidelines, all diagnostic and therapeutic requirements of osteoporosis can be met by the team of consultant specialists at a clinical osteoporosis centre. In the described treatment concept of integrated care, 44 patients suffering of osteoporosis with a consecutive fracture could be included. Mean age was 77. Inclusion criteria were spinal fractures (61%), proximal femoral fractures (27%) and peripheral fractures (12%). Fifty percent of patients included into the contract had not received previous osteoporosis medication. Sixty-eight patients who met the inclusion criteria could not be included due to the lack of compliance (42%), patients' disapproval (34%) or incomplete treatment and documentation algorithm (24%). Special attention should focus on the completion of standardised diagnosis and documentation. The high amount of time and personnel required has proven the importance of the introduction of an osteoporosis coordinator to be essential.

Objectives: There have been many developments and applications of mathematical methods in the context of record linkage as one area of interdisciplinary research efforts. However, comparative evaluations of record linkage methods are still underrepresented. In this paper improvements of the Fellegi-Sunter model are compared with other elaborated classification methods in order to direct further research endeavors to the most promising methodologies., Methods: The task of linking records can be viewed as a special form of object identification. We consider several non-stochastic methods and procedures for the record linkage task in addition to the Fellegi-Sunter model and perform an empirical evaluation on artificial and real data in the context of iterative insertions. This evaluation provides a deeper insight into empirical similarities and differences between different modelling frames of the record linkage problem. In addition, the effects of using string comparators on the performance of different matching algorithms are evaluated., Results: Our central results show that stochastic record linkage based on the principle of the EM algorithm exhibits best classification results when calibrating data are structurally different to validation data. Bagging, boosting together with support vector machines are best classification methods when calibrating and validation data have no major structural differences., Conclusions: The most promising methodologies for record linkage in environments similar to the one considered in this paper seem to be stochastic ones.

In this paper, a novel geometric calibration method for C-arm cone-beam scanners is presented which allows the calibration of the circle-plus-arc trajectory. The main idea is the separation of the trajectory into two circular segments (circle segment and arc segment) which are calibrated independently. This separation makes it possible to reuse a calibration phantom which has been successfully applied in clinical environments to calibrate numerous routinely used C-arm systems. For each trajectory segment, the phantom is placed in an optimal position. The two calibration results are then combined by computing the transformation the phantom underwent between the independent calibration runs. This combination can be done in a post-processing step by using standard linear algebra. The method is not limited to circle-plus-arc trajectories and works for any calibration procedure in which the phantom has a preferred orientation with respect to a trajectory segment. Results are presented for both simulated as well as real data acquired with a C-arm system. We also present the first image reconstruction results for the circle-plus-arc trajectory using real C-arm data.

In this paper, we propose an optimization concept for a rotation therapy technique which is referred to as arc-modulated cone beam therapy (AMCBT). The aim is a reduction of the treatment time while achieving a treatment plan quality equal to or better than that of IMRT. Therefore, the complete dose is delivered in one single gantry rotation and the beam is modulated by a multileaf collimator. The degrees of freedom are the field shapes and weights for a predefined number of beam directions. In the new optimization loop, the beam weights are determined by a gradient algorithm and the field shapes by a tabu search algorithm. We present treatment plans for AMCBT for two clinical cases. In comparison to step-and-shoot IMRT treatment plans, it was possible by AMCBT to achieve dose distributions with a better dose conformity to the target and a lower mean dose for the most relevant organ at risk. Furthermore, the number of applied monitor units was reduced for AMCBT in comparison to IMRT treatment plans.

Objectives: In this paper we are concerned with elastic image registration. Usually, elastic approaches assume constant material parameters and result in a smooth displacement field. However, a constant choice has its shortcomings for images with varying elastic properties, like bones and soft tissue. The proposed method allows for spatially varying material properties., Methods: The proposed variational registration scheme is based on a segmentation of the template image. Individual material properties can be assigned to each segmented region. The proposed variable elastic regulariser leads to a displacement field which is adapted to the locally chosen material properties., Results: The capability of this approach is demonstrated by a synthetic and by real-life examples in two dimensions. For all examples the proposed method is compared to a conventional scheme where the material parameters are constants in the entire image domain., Conclusions: A method for non-parametric registration which supports spatially varying elastic properties such as (in)compressibility or Young's modulus in certain image regions is proposed. It allows for registration results to be more realistic compared to conventional approaches. Also, for a particular structure, an approximated preservation of volume or shape can be achieved.

Until now few analyses of routine data relating to the health of migrants have been conducted in Germany. A major obstacle is that most data sources do not provide reliable information on the origin of migrants. While some sources contain the nationality of persons registered, this information does not allow one to identify migrants who have taken up German citizenship, i.e., a substantial part of second-generation migrants. In this paper we demonstrate how a computer-aided, name-based algorithm can be used to identify persons of Turkish origin in the German Childhood Cancer Registry in Mainz, Germany. The performance of the algorithm, as assessed against the gold standard of assessing names manually, was very good (sensitivity and specificity > or = 0.975). In total, we identified 1774 of the 37,259 cases in the registry as being of Turkish origin. The name algorithm proved to be a useful tool to identify Turkish migrants in routine data sources, thus avoiding potential bias due to changes in citizenship. This approach aims at improving migrant-sensitive health reporting and research in Germany. In future, additional information on migrant status should be obtained already during primary data collection so that health data for all migrant groups can be provided.

The assessment of extra-, intracellular and total body water (ECW, ICW, TBW) is important in many clinical situations. Bioimpedance spectroscopy (BIS) has advantages over dilution methods in terms of usability and reproducibility, but a careful analysis reveals systematic deviations in extremes of body composition and morbid states. Recent publications stress the need to set up and validate BIS equations in a wide variety of healthy subjects and patients with fluid imbalance. This paper presents two new equations for determination of ECW and ICW (referred to as body composition spectroscopy, BCS) based on Hanai mixture theory but corrected for body mass index (BMI). The equations were set up by means of cross validation using data of 152 subjects (120 healthy subjects, 32 dialysis patients) from three different centers. Validation was performed against bromide/deuterium dilution (NaBr, D2O) for ECW/TBW and total body potassium (TBK) for ICW. Agreement between BCS and the references (all subjects) was -0.4 +/- 1.4 L (mean +/- SD) for ECW, 0.2 +/- 2.0 L for ICW and -0.2 +/- 2.3 L for TBW. The ECW agreement between three independent reference methods (NaBr versus D2O-TBK) was -0.1 +/- 1.8 L for 74 subjects from two centers. Comparing the new BCS equations with the standard Hanai approach revealed an improvement in SEE for ICW and TBW by 0.6 L (24%) for all subjects, and by 1.2 L (48%) for 24 subjects with extreme BMIs (<20 and >30). BCS may be an appropriate method for body fluid volume determination over a wide range of body compositions in different states of health and disease.

The three-isotope tracer Delta(17)O is increasingly used in atmospheric chemistry and other research areas. Thanks to the development of isotope-ratio mass spectrometry (IRMS), delta(17)O and delta(18)O can be determined with a precision of a few 0.01 per thousand, and values for Delta(17)O may be calculated with similar precision. However, interpreting small and precisely determined Delta(17)O values as a deviation from an expected mass-dependent fractionation process is not straightforward. Several aspects are of high importance. In the present paper we review existing definitions, formulas and some other aspects of Delta(17)O reporting. One of the most confusing aspects is a variance of definitions and corresponding formulas. While Delta(17)O is traditionally defined to characterise a data point, i.e. Delta(17)O is considered as a deviation from an expected mass-fractionation line, the recently introduced definition (Miller MF. Geochim. Cosmochim. Acta 2002; 66: 188) characterises a fractionation line itself, in terms of its ordinate intercept. The formulas corresponding to this definition gives a characteristic for a specific process. When the 'traditionally defined' Delta(17)O is in use, an expected fractionation processes--the key point for Delta(17)O reporting--should be defined and parameterised with the same accuracy as intended for reporting Delta(17)O. When Delta(17)O is reported for a data point, not only a value for lambda but an ordinate intercept of a reference fractionation line should be given with high accuracy. We note that defining a single fractionation process is hardly possible for many natural compounds. For such compounds we propose to use a phenomenological reference line, namely an isotope composition range of natural sources. Next, aspects of Delta(17)O comparison and mass-balance calculations are considered. All the aspects considered for Delta(17)O may be relevant for others three-isotope tracers, e.g. Delta(33)S.

Soft tissue simulation with the finite element method is based on discretizations of anatomical objects. The structure of organs is mostly heterogeneous. To compute the deformation of composite materials correctly borders between adjacent tissues must be preserved. In this paper a new meshing algorithm is presented that fulfill this requirement. Additionally it works automatically in a straight forward manner to produce high quality 3d meshes from input triangle surfaces. The algorithm shows robust behavior and allows even to generate mesh from the most complex geometries. The processing time depends directly on the size of the input mesh and not on topology. This makes this algorithm calculable hence useful for time dependent cases, for example to adapt intraoperatively to modified operation status.

In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data as an input. In this paper, a comparative qualitative study was conducted using the iterative partitioning and hierarchical clustering based mechanisms and full waveform ALS data as an input to extract the individual trees/tree crowns in their most appropriate shape. The full waveform LIght Detection And Ranging (LIDAR) data was collected from the Waldkirch black forest area in the south-western part of Germany in August 2005 with density of 4-5 points/m². Both the clustering algorithms were used in their original and modified form for a comparative qualitative analysis of the results obtained in the form of individual clusters containing 3-D points for each tree/tree crown. A total of 378 trees were found in all the 1.2 ha area with height ranging from 15 m to 50.9 m. The forest contains mainly older trees with deciduous, coniferous and mixed stands. The findings showed that among the three kind of clustering methods applied (normal k-means, modified k-means and hierarchical clustering), the modified k-means algorithm using external seed points and scaling down the height for initialization of the clustering process was the most promising method for the extraction of clusters of individual trees/tree crowns. A 3-D reconstruction of extracted individual clusters was carried out using QHull algorithm. In this study, the result was not possible to validate quantitatively due to lack of the field inventory data. [ABSTRACT FROM AUTHOR]

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In this paper we investigate a vehicle routing problem motivated by a real-world application in cooperation with the German Automobile Association (ADAC). The general task is to assign service requests to service units and to plan tours for the units such as to minimize the overall cost. The characteristics of this large-scale problem due to the data volume involve strict real-time requirements. We show that the problem of finding a feasible dispatch for service units starting at their current position and serving at most k requests is NP-complete for each fixed k ≥ 2. We also present a polynomial time (2 k − 1)-approximation algorithm, where again k denotes the maximal number of requests served by a single service unit. For the boundary case when k equals the total number | E| of requests (and thus there are no limitations on the tour length), we provide a $${\left(2-\frac{1}{|E|} \right)}$$ -approximation. Finally, we extend our approximation results to include linear and quadratic lateness costs. [ABSTRACT FROM AUTHOR]

The SF-36 is a widely used instrument to measure health-related quality of life that provides a profile of eight scales [Physical Functioning (PF), Role-Physical (RP), Bodily Pain (BP), General Health (GH), Vitality (VT), Social Functioning (SF), Role-Emotional (RE) and Mental Health (MH)]. To reduce the dimension of the profile, a principal component analysis with the data of the US SF-36 Survey was undertaken. As a result, the Physical Component Summary (PCS) and the Mental Component Summary (MCS) were gained by weighting and summing up of the original scales of the SF-36. The weights were assumed to be country specific. Comparing the weights from the representative samples of nine European countries with those from the USA it has been suggested that the weights gained from the US sample could be applied to all datasets for purposes of comparability and simplicity. In this paper, for a new representative German population sample completed in 1998, the calculation results are compared with those for the older German population sample from 1994. PCS and MCS are calculated using the weights from the new German population sample as well as using the American weights. In this paper, the calculations for the 1998 representative population sample were repeated based on the US weights developed in 1994. The German weights were also calculated based on the 1998 sample. The results were compared and the pro and cons of using the American weights are discussed. In essence, the American weights continue to be acceptable for international comparisons. The separate evaluation of physical and mental health for men and women allows additional insight into gender-specific changes. In this paper, the summary scales for the 1998 German normative population are calculated gender specific. The weights to be used in future studies are also published. With respect to gender mainstreaming, this approach should be taken into consideration in quality of life research. Regarding the methodological and factorial difficulties, the questions arise if the summary scales should be applied instead of the original scales of the SF-36 questionnaire.

In late 2001, the Object Management Group issued a Request for Proposal to develop a testing profile for UML 2.0. In June 2003, the work on the UML 2.0 Testing Profile was finally adopted by the OMG. Since March 2004, it has become an official standard of the OMG. The UML 2.0 Testing Profile provides support for UML based model-driven testing. This paper introduces a methodology on how to use the testing profile in order to modify and extend an existing UML design model for test issues. The application of the methodology will be explained by applying it to an existing UML Model for a Bluetooth device.

In risk analysis there is a spatial mismatch of hazard data that are commonly modelled on an explicit raster level and exposure data that are often only available for aggregated units, e.g. communities. Dasymetric mapping techniques that use ancillary information to disaggregate data within a spatial unit help to bridge this gap. This paper presents dasymetric maps showing the population density and a unit value of residential assets for whole Germany. A dasymetric mapping approach, which uses land cover data (CORINE Land Cover) as ancillary variable, was adapted and applied to regionalize aggregated census data that are provided for all communities in Germany. The results were validated by two approaches. First, it was ascertained whether population data disaggregated at the community level can be used to estimate population in postcodes. Secondly, disaggregated population and asset data were used for a loss evaluation of two flood events that occurred in 1999 and 2002, respectively. It must be concluded that the algorithm tends to underestimate the population in urban areas and to overestimate population in other land cover classes. Nevertheless, flood loss evaluations demonstrate that the approach is capable of providing realistic estimates of the number of exposed people and assets. Thus, the maps are sufficient for applications in large-scale risk assessments such as the estimation of population and assets exposed to natural and man-made hazards. [ABSTRACT FROM AUTHOR]

This paper illustrates a method based on local likelihood (LL) for detecting disease clusters. The approach is based on estimating a lasso distance for each region: within which regions are considered to be clustered. An important advantage in implementing this approach is that it does not require any special Monte Carlo Markov Chain (MCMC) algorithm, e.g., reversible jump MCMC, which is essential in hidden Markov model approach. Another advantage is that extending the model to incorporate covariates is straightforward. We illustrate three ways of doing this by using Eastern Germany lip cancer data. By using simulated data, we have made a comparison with the BYM model [Besag et al. (1991) Annals of the Institute of Statistical Mathematics, 43, 1–59] and the mixture model [Lawson and Clark (2002) Disease Mapping and Risk Assessment for Public Health, Chapman and Hall]. We also did a limited examination of the ability of the LL model to recover true relative risk under different priors for lasso parameter. In order to check the edge effects, which has been overlooked in many spatial clustering models for disease mapping but deserves special attention as it lacks observable neighbors, we have adapted here a simple approach to observe the changes in relative risks when the edge regions are omitted. [ABSTRACT FROM AUTHOR]

The management of moving objects has been intensively studied in recent years. A wide and increasing range of database applications has to deal with spatial objects whose position changes continuously over time, called moving objects. The main interest of these applications is to efficiently store and query the positions of these continuously moving objects. To achieve this goal, index structures are required. The main proposals of index structures for moving objects deal with unconstrained 2-dimensional movement. Constrained movement is a special and a very important case of object movement. For example, cars move in roads and trains in railroads. In this paper we propose a new index structure for moving objects on networks, the MON-Tree. We describe two network models that can be indexed by the MON-Tree. The first model is edge oriented, i.e., the network consists of nodes and edges and there is a polyline associated to each edge. The second one is more suitable for transportation networks and is route oriented, i.e., the network consists of routes and junctions. In this model, a polyline also serves as a representation of the routes. We propose the index in terms of the basic algorithms for insertion and querying. We test our proposal in an extensive experimental evaluation with generated data sets using as underlying networks the roads of Germany. In our tests, the MON-Tree shows good scalabiliy and outperforms the competing index structures in updating (index creation) as well as in querying. [ABSTRACT FROM AUTHOR]

The Bat optimizing algorithm (BOA) is one of the metaheuristic algorithms and applied here to interpret self-potential (SP) data. The BOA is depending upon a bat echolocation behavior for global optimization, which the global optimum solution reached at the suggested minimum value of the objective function. The best interpretive source parameters for the subsurface structures occurred at the minimal the objective function value (global best solution). The BOA is applied to 2D SP anomaly data to estimate the characteristic source parameters (i.e., the depth to center, amplitude coefficient, origin location, geometric shape factor, and polarization and inclination angle of the causative buried structure). The BOA can be applied to single and multiple source structures in the restricted class of simple geometric shapes, which these bodies help in the validation of the subsurface ore and mineral targets. The stability and efficiency of the proposed BOA have been examined by several synthetic examples. In addition, three different real field examples from Germany and Indonesia have been successfully applied to ore and mineral investigation and geological structure studies. In general, the achieved results are in good agreement with the available borehole data and results mentioned in the literature. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]