| 1. |
- Bender, Jennifer K., et al.
(author)
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Analysis of Asymptomatic and Presymptomatic Transmission in SARS-CoV-2 Outbreak, Germany, 2020
- 2021
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In: Emerging Infectious Diseases. - : Centers for Disease Control and Prevention (CDC). - 1080-6040 .- 1080-6059. ; 27:4, s. 1159-1163
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Journal article (peer-reviewed)abstract
- We determined secondary attack rates (SAR) among close contacts of 59 asymptomatic and symptomatic coronavirus disease case-patients by presymptomatic and symptomatic exposure. We observed no transmission from asymptomatic case-patients and highest SAR through presymptomatic exposure. Rapid quarantine of close contacts with or without symptoms is needed to prevent presymptomatic transmission.
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| 2. |
- Höhle, Michael, 1975-, et al.
(author)
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Generation and Assessment of Urban Land Cover Maps Using High-Resolution Multispectral Aerial Cameras
- 2013
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In: International Journal on Advances in Software. - 1942-2628. ; 6:3-4, s. 272-282
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Journal article (peer-reviewed)abstract
- New aerial cameras and new advanced geoprocessingtools improve the generation of urban land covermaps. Elevations can be derived from stereo pairs with highdensity, positional accuracy, and efficiency. The combinationof multispectral high-resolution imagery and high-densityelevations enable a unique method for the automaticgeneration of urban land cover maps. In the present paper,imagery of a new medium-format aerial camera and advancedgeoprocessing software are applied to derive normalizeddigital surface models and vegetation maps. These twointermediate products then become input to a tree structuredclassifier, which automatically derives land cover maps in 2Dor 3D. We investigate the thematic accuracy of the producedland cover map by a class-wise stratified design and provide amethod for deriving necessary sample sizes. Correspondingsurvey adjusted accuracy measures and their associatedconfidence intervals are used to adequately reflect uncertaintyin the assessment based on the chosen sample size. Proof ofconcept for the method is given for an urban area inSwitzerland. Here, the produced land cover map with sixclasses (building, wall and carport, road and parking lot, hedgeand bush, grass) has an overall accuracy of 86% (95%confidence interval: 83-88%) and a kappa coefficient of 0.82(95% confidence interval: 0.78-0.85). The classification ofbuildings is correct with 99% and of road and parking lot with95%. To possibly improve the classification further,classification tree learning based on recursive partitioning isinvestigated. We conclude that the open source software “R”provides all the tools needed for performing statistical prudentclassification and accuracy evaluations of urban land covermaps.
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| 3. |
- Allévius, Benjamin, et al.
(author)
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An unconditional space–time scan statistic for ZIP‐distributed data
- 2019
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In: Scandinavian Journal of Statistics. - : Wiley. - 0303-6898 .- 1467-9469. ; 46:1, s. 142-159
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Journal article (peer-reviewed)abstract
- A scan statistic is proposed for the prospective monitoring of spatiotemporal count data with an excess of zeros. The method that is based on an outbreak model for the zero‐inflated Poisson distribution is shown to be superior to traditional scan statistics based on the Poisson distribution in the presence of structural zeros. The spatial accuracy and the detection timeliness of the proposed scan statistic are investigated by means of simulation, and an application on the weekly cases of Campylobacteriosis in Germany illustrates how the scan statistic could be used to detect emerging disease outbreaks. An implementation of the method is provided in the open‐source R package scanstatistics available on the Comprehensive R Archive Network.
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| 4. |
- Allévius, Benjamin
(author)
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Scan Statistics for Space-Time Cluster Detection
- 2018
-
Licentiate thesis (other academic/artistic)abstract
- Scan statistics are used by public health agencies to detect and localize disease outbreaks. This thesis provides an overview of scan statistics in the context of prospective disease surveillance and outbreak detection, presents a novel scan statistic to deal with the type of zero-abundant data that is often encountered in these settings, and—perhaps most importantly—implements this and other scan statistics in a freely available and open source R package. Additionally, Markov processes and time series methods are frequently used in many disease surveillance methods. The last part of this thesis presents some computationally efficient methods for density evaluation and simulation of irregularly sampled AR(1) processes, that may be useful when implementing surveillance methods based on these types of processes.
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| 5. |
- Bergström, Fanny, 1988-, et al.
(author)
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Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden
- 2022
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 18:12
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Journal article (peer-reviewed)abstract
- The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.
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| 6. |
- Bernard, Helen, et al.
(author)
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Estimating the under-reporting of norovirus illness in Germany utilizing enhanced awareness of diarrhoea during a large outbreak of Shiga toxin-producing E. coli O104:H4 in 2011 - a time series analysis
- 2014
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In: BMC Infectious Diseases. - : Springer Science and Business Media LLC. - 1471-2334. ; 14
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Journal article (peer-reviewed)abstract
- Background: Laboratory- confirmed norovirus illness is reportable in Germany since 2001. Reported case numbers are known to be undercounts, and a valid estimate of the actual incidence in Germany does not exist. An increase of reported norovirus illness was observed simultaneously to a large outbreak of Shiga toxin-producing E. coli O104: H4 in Germany in 2011 - likely due to enhanced (but not complete) awareness of diarrhoea at that time. We aimed at estimating age- and sex-specific factors of that excess, which should be interpretable as (minimal) under-reporting factors of norovirus illness in Germany. Methods: We used national reporting data on laboratory-confirmed norovirus illness in Germany from calendar week 31 in 2003 through calendar week 30 in 2012. A negative binomial time series regression model was used to describe the weekly counts in 8.2 age- sex strata while adjusting for secular trend and seasonality. Overall as well as age- and sex- specific factors for the excess were estimated by including additional terms (either an O104: H4 outbreak period indicator or a triple interaction term between outbreak period, age and sex) in the model. Results: We estimated the overall under- reporting factor to be 1.76 (95% Cl 1.28- 2.41) for the first three weeks of the outbreak before the outbreak vehicle was publicly communicated. Highest under-reporting factors were here estimated for 20- 29 year-old males (2.88, 95% Cl 2.01- 4.11) and females (2.67, 95% Cl 1.87- 3.79). Under-reporting was substantially lower in persons aged < 10 years and 70 years or older. Conclusions: These are the first estimates of (minimal) under- reporting factors for norovirus illness in Germany. They provide a starting point for a more detailed investigation of the relationship between actual incidence and reporting incidence of norovirus illness in Germany.
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| 7. |
- Ekvall, Markus, et al.
(author)
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Parallelized calculation of permutation tests
- 2020
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:22-23, s. 5392-5397
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Journal article (peer-reviewed)abstract
- Motivation: Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naive implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Results: Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up-by orders of magnitude-is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test.
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| 8. |
- Espinosa, Laura, et al.
(author)
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Epitweetr : Early warning of public health threats using Twitter data
- 2022
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In: Eurosurveillance. - 1025-496X .- 1560-7917. ; 27:39
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Journal article (peer-reviewed)abstract
- Background: The European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geotocates and aggregates tweets generating signals and email alerts. Aim: This study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats. Methods: We calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and accuracy and performance of tweet geolocation and signal detection algorithms obtained from epitweetr and the manual monitoring of 1,200 tweets were compared. Results: The epitweetr geolocation algorithm had an accuracy of 30.1% at national, and 25.9% at subnational levels. The signal detection algorithm had 3.0% general PPV and 74.6% specific PPV. Compared to manual monitoring, epitweetr had greater sensitivity (47.9% and 78.6%, respectively), and reduced PPV (97.9% and 74.6%, respectively). Median validation time difference between 16 common events detected by epitweetr and manual monitoring was -48.6 hours (IQR: -102.8 to -23.7). Conclusion: Epitweetr has shown sufficient performance as an early warning toot for public health threats using Twitter data. Since epitweetr is a free, open-source tool with configurable settings and a strong automated component, it is expected to increase in usability and usefulness to public health experts.
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| 9. |
- Fenske, Nora, et al.
(author)
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Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data
- 2013
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In: The International Journal of Biostatistics. - : Walter de Gruyter GmbH. - 1557-4679 .- 2194-573X. ; 9:1, s. 1-18
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Journal article (peer-reviewed)abstract
- Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.
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| 10. |
- Günther, Felix, et al.
(author)
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Nowcasting the COVID-19 pandemic in Bavaria
- 2021
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In: Biometrical Journal. - : Wiley. - 0323-3847 .- 1521-4036. ; 63:3, s. 490-502
-
Journal article (peer-reviewed)abstract
- To assess the current dynamics of an epidemic, it is central to collect information on the daily number of newly diseased cases. This is especially important in real-time surveillance, where the aim is to gain situational awareness, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred-but-not-yet-reported events. Here, we present a novel application of nowcasting to data on the current COVID-19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time-varying case reproduction number Re(t) based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID-19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current pandemic, and evaluate the model based on synthetic and retrospective data on COVID-19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.
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