scholarly journals Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

2021 ◽  
Vol 149 ◽  
Author(s):  
Helmut Küchenhoff ◽  
Felix Günther ◽  
Michael Höhle ◽  
Andreas Bender
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Disease Onset ◽  
Major Change ◽  
Change Points ◽  
Federal State ◽  
Strong Increase ◽  
Epidemic Curve ◽  
Projection Techniques ◽  
Reporting Delay

Abstract We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

scholarly journals Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points

2020 ◽  
Author(s):  
Helmut Küchenhoff ◽  
Felix Günther ◽  
Michael Höhle ◽  
Andreas Bender
Keyword(s):  
Time Series ◽  
Disease Onset ◽  
Major Change ◽  
Change Points ◽  
Federal State ◽  
Strong Increase ◽  
Robert Koch Institute ◽  
Epidemic Curve ◽  
Projection Techniques ◽  
Reporting Delay

AbstractWe analyze the Covid-19 epidemic curve from March to end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analyzed by a Poisson trend regression model with change points. The change points are estimated directly from the data without further assumptions. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between March 9th and 13th for the time series of infections: from a strong increase to a stagnation or a slight decrease. Another change was found between March 24th and March 31st, where the decline intensified. These two major changes can be related to different governmental measures. On March, 11th, Chancellor Merkel appealed for social distancing in a press conference with the Robert Koch Institute (RKI) and a ban on major events with more than 1000 visitors (March 10th) was issued. The other change point at the end of March could be related to the shutdown in Germany.Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

scholarly journals Changes of China’s agri-food exports to Germany caused by its accession to WTO and the 2008 financial crisis

2015 ◽  
Vol 7 (2) ◽  
pp. 262-279 ◽  
Author(s):  
Zhichao Guo ◽  
Yuanhua Feng ◽  
Thomas Gries
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Regression Models ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Middle Part ◽  
Change Points ◽  
Content Type ◽  
The World ◽  
Before And After

Purpose – The purpose of this paper is to investigate changes of China’s agri-food exports to Germany caused by China’s accession to WTO and the global financial crisis in a quantitative way. The paper aims to detect structural breaks and compare differences before and after the change points. Design/methodology/approach – The structural breaks detection procedures in this paper can be applied to find out two different types of change points, i.e. in the middle and at the end of one time series. Then time series and regression models are used to compare differences of trade relationship before and after the detected change points. The methods can be employed in any economic series and work well in practice. Findings – The results indicate that structural breaks in 2002 and 2009 are caused by China’s accession to WTO and the financial crisis. Time series and regression models show that the development of China’s exports to Germany in agri-food products has different features in different sub-periods. Before 1999, there is no significant relationship between China’s exports to Germany and Germany’s imports from the world. Between 2002 and 2008 the former depends on the latter very strongly, and China’s exports to Germany developed quickly and stably. It decreased, however suddenly in 2009, caused by the great reduction of Germany’s imports from the world in that year. But China’s market share in Germany still had a small gain. Analysis of two categories in agri-food trade also leads to similar conclusions. Comparing the two events we see rather different patterns even if they both indicate structural breaks in the development of China’s agri-food exports to Germany. Originality/value – This paper partly originally proposes two statistical algorithms for detecting different kinds of structural breaks in the middle part and at the end of a short-time series, respectively.

scholarly journals Nowcasting the COVID-19 pandemic in Bavaria

2020 ◽  
Author(s):  
Felix Günther ◽  
Andreas Bender ◽  
Katharina Katz ◽  
Helmut Küchenhoff ◽  
Michael Höhle
Keyword(s):  
Bayesian Model ◽  
Reproduction Number ◽  
Disease Onset ◽  
Daily Basis ◽  
Time Dependent ◽  
Disease Spread ◽  
Hierarchical Bayesian Model ◽  
Epidemic Curve ◽  
Reporting Delays ◽  
Reporting Delay

AbstractTo assess the current dynamic 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, when one aims at evaluating the effects of interventions on disease spread. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamic of an epidemic 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 associated with the week and weekday of reporting and assumes a smooth epidemic curve. Furthermore, we present a way to estimate the time-dependent case reproduction number R(t) based on predictions of the nowcast. We provide methodological details of the developed approach, illustrate results based on data of the current epidemic, discuss limitations and alternative estimation strategies, and provide code for reproduction or adaption of the nowcasting to data from different regions. Results of the nowcasting approach are reported to the Bavarian health authority and published on a webpage on a daily basis.

scholarly journals Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 480
Author(s):  
Rania Kousovista ◽  
Christos Athanasiou ◽  
Konstantinos Liaskonis ◽  
Olga Ivopoulou ◽  
George Ismailos ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Acinetobacter Baumannii ◽  
Regression Models ◽  
Tertiary Care ◽  
Antimicrobial Use ◽  
Care Hospital ◽  
Arima Models ◽  
Series Analysis ◽  
Resistance Rates

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R2 > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

scholarly journals Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tanja Charles ◽  
Matthias Eckardt ◽  
Basel Karo ◽  
Walter Haas ◽  
Stefan Kröger
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Disease Onset ◽  
Age Groups ◽  
Age Group ◽  
Pulmonary Tb ◽  
Series Analysis ◽  
Disease Site ◽  
Extra Pulmonary

Abstract Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals Gender-Specific Risk Factors for the Development of Retinal Changes in Children with Type 1 Diabetes

2021 ◽  
Vol 11 (6) ◽  
pp. 588
Author(s):  
Marta Wysocka-Mincewicz ◽  
Joanna Gołębiewska ◽  
Marta Baszyńska-Wilk ◽  
Andrzej Olechowski
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Uric Acid ◽  
Serum Creatinine ◽  
Regression Models ◽  
Retinal Thickness ◽  
Disease Onset ◽  
Specific Risk ◽  
Gender Specific

The aim of the study was to determine gender-specific risk factor sets which could influence optical coherence tomography (OCT) results in children with type 1 diabetes (T1D). Material and Methods: 175 children with T1D without symptoms of diabetic retinopathy were enrolled, but 330 eyes were used for the final analysis (168 children, mean age 12.81 ± 3.63 years, diabetes duration 4.59 ± 3.71 years). The multivariate regression models for retinal thickness (foveal FT, and parafoveal PFT) and vascular densities (superficial and deep) were carried out separately for both genders using all metabolic and demographic parameters. Results: In the statistically significant multiple regression models for all analyzed OCT parameters for both genders, pH at the onset of diabetes were in existence, as well as for retinal thickness current HbA1c. Duration of continuous insulin infusion (CSII) was an important factor in all parameters, except PFT. For the girls, the most significant factors were daily insulin dose, uric acid, and triglycerides, but for the boys, it was serum creatinine, systolic pressure, and free thyroxine level. Conclusions: We detected significant risk factors set for development of OCT parameters changes, and they were not identical for both genders. Current metabolic control, diabetic ketoacidosis at the disease onset, serum creatinine and longer use of CSII are the most important factors for retinal thickness and vessel densities in both genders in children with type 1 diabetes. For the girls, elements of metabolic syndrome (uric acid and triglycerides) and parameters of insulin amount were more pronounced.

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