| 1. |
- 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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| 2. |
- Höhle, Michael, et al.
(author)
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Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 2011
- 2014
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In: Biometrics. - : Wiley. - 0006-341X .- 1541-0420. ; 70:4, s. 993-1002
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Journal article (peer-reviewed)abstract
- A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends.
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| 3. |
- Jombart, Thibaut, et al.
(author)
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OutbreakTools : A new platform for disease outbreak analysis using the R software
- 2014
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In: Epidemics. - : Elsevier BV. - 1755-4365 .- 1878-0067. ; 7, s. 28-34
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Journal article (peer-reviewed)abstract
- The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks.
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| 4. |
- Weidemann, Felix, et al.
(author)
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Bayesian parameter inference for dynamic infectious disease modelling : Rotavirus in Germany
- 2014
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 33:9, s. 1580-1599
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Journal article (peer-reviewed)abstract
- Understanding infectious disease dynamics using epidemic models based on ordinary differential equations requires the calibration of model parameters from data. A commonly used approach in practice to simplify this task is to fix many parameters on the basis of expert or literature information. However, this not only leaves the corresponding uncertainty unexamined but often also leads to biased inference for the remaining parameters because of dependence structures inherent in any given model. In the present work, we develop a Bayesian inference framework that lessens the reliance on such external parameter quantifications by pursuing a more data-driven calibration approach. This includes a novel focus on residual autocorrelation combined with model averaging techniques in order to reduce these estimates’ dependence on the underlying model structure. We applied our methods to the modelling of age-stratified weekly rotavirus incidence data in Germany from 2001 to 2008 using a complex susceptible–infectious–susceptible-type model complemented by the stochastic reporting of new cases. As a result, we found the detection rate in the eastern federal states to be more than four times higher compared with that of the western federal states (19.0% vs 4.3%), and also the infectiousness of symptomatically infected individuals was estimated to be more than 10 times higher than that of asymptomatically infected individuals (95% credibility interval: 8.1–19.6). Not only do these findings give valuable epidemiological insight into the transmission processes, we were also able to examine the considerable impact on the model-predicted transmission dynamics when fixing parameters beforehand.
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| 5. |
- Weidemann, Felix, et al.
(author)
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Modelling the epidemiological impact of rotavirus vaccination in Germany - A Bayesian approach
- 2014
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In: Vaccine. - : Elsevier BV. - 0264-410X .- 1873-2518. ; 32:40, s. 5250-5257
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Journal article (peer-reviewed)abstract
- Background: Rotavirus (RV) infection is the primary cause of severe gastroenteritis in children aged <5 years in Germany and worldwide. In 2013 the German Standing Committee on Vaccination (STIKO) developed a national recommendation for routine RV-immunization of infants. To support informed decision-making we predicted the epidemiological impact of routine RV-vaccination in Germany using statistical modelling. Methods: We developed a population-based model for the dynamic transmission of RV-infection in a vaccination setting. Using data from the communicable disease reporting system and survey records on the vaccination coverage from the eastern federal states, where the vaccine was widely used before recommended at national level, we first estimated RV vaccine effectiveness (VE) within a Bayesian framework utilizing adaptive Markov Chain Monte Carlo inference. The calibrated model was then used to compute the predictive distribution of RV-incidence after achieving high vaccination coverage with the introduction of routine vaccination. Results: Our model estimated that RV-vaccination provides high protection against symptomatic RV-infection (VE=96%; 95% credibility interval (CI): 91-99%) that remains at its maximum level for three years (95% CI: 1.43-5.80 years) and is fully waned after twelve years. At population level, routine vaccination at 90% coverage is predicted to reduce symptomatic RV-incidence among children aged <5 years by 84% (95% prediction interval (PI): 71-90%) including a 2.5% decrease due to herd protection. Ten years after vaccine introduction an increase in RV incidences of 12% (95% PI: -16 to 85%) among persons aged 5-59 years and 14% (95% PI: -6 to 109%) within the age-group >60 years was predicted. Conclusion: Routine infant RV-vaccination is predicted to considerably reduce RV-incidence in Germany among children <5 years. Outwork generated estimates of RV VE in the field and predicted the population-level impact, while adequately addressing the role of model and prediction uncertainty when making statements about the future.
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