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  • 1.
    Allévius, Benjamin
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    An unconditional space–time scan statistic for ZIP‐distributed data2019Inngår i: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 46, nr 1, s. 142-159Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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.

  • 2. Bernard, H.
    et al.
    Faber, M.
    Wilking, H.
    Haller, S.
    Höhle, Michael
    Robert Koch Institute, Germany.
    Schielke, A.
    Ducomble, T.
    Siffczyk, C.
    Merbecks, S. S.
    Fricke, G.
    Hamouda, O.
    Stark, K.
    Werber, D.
    Large multistate outbreak of norovirus gastroenteritis associated with frozen strawberries, East Germany, 20122014Inngår i: Eurosurveillance, ISSN 1025-496X, E-ISSN 1560-7917, Vol. 19, nr 8, artikkel-id pii=20719Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    From 20 September through 5 October 2012, the largest recorded food-borne outbreak in Germany occurred. Norovirus was identified as the causative agent. We conducted four analytical epidemiological studies, two case–control studies and two surveys (in total 150 cases) in secondary schools in three different federal states. Overall, 390 institutions in five federal states reported nearly 11,000 cases of gastroenteritis. They were predominantly schools and childcare facilities and were supplied almost exclusively by one large catering company. The analytical epidemiological studies consistently identified dishes containing strawberries as the most likely vehicle, with estimated odds ratios ranging from 2.6 to 45.4. The dishes had been prepared in different regional kitchens of the catering company and were served in the schools two days before the peaks of the respective outbreaks. All affected institutions had received strawberries of one lot, imported frozen from China. The outbreak vehicle was identified within a week, which led to a timely recall and prevented more than half of the lot from reaching the consumer. This outbreak exemplifies the risk of large outbreaks in the era of global food trade. It underlines the importance of timely surveillance and epidemiological outbreak investigations for food safety.

  • 3. Bernard, Helen
    et al.
    Werber, Dirk
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Robert Koch Institute, Germany.
    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 analysis2014Inngår i: BMC Infectious Diseases, ISSN 1471-2334, E-ISSN 1471-2334, Vol. 14, artikkel-id 116Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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.

  • 4. Fenske, Nora
    et al.
    Fahrmeir, Ludwig
    Rzehak, Peter
    Hothorn, Torsten
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data2013Inngår i: The International Journal of Biostatistics, ISSN 1557-4679, E-ISSN 1557-4679, Vol. 9, nr 1, s. 1-18Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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.

  • 5.
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Rank uncertainty: why the “most popular” baby names might not be the most popular2017Inngår i: Significance, ISSN 1740-9705, E-ISSN 1740-9713, Vol. 14, nr 3, s. 30-33Artikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    Soon‐to‐be parents, souvenir mug producers and onomatologists are all equally fascinated by baby name statistics. But end‐of‐year lists of the most popular names fail to account for uncertainty says Michael Höhle. Can we trust the rankings?

  • 6.
    Höhle, Michael
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Robert Koch Institute, Germany.
    an der Heiden, Matthias
    Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 20112014Inngår i: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 70, nr 4, s. 993-1002Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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.

  • 7.
    Höhle, Michael
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Höhle, Joachim
    Aalborg University.
    Generation and Assessment of Urban Land Cover Maps Using High-Resolution Multispectral Aerial Cameras2013Inngår i: International Journal On Advances in Software, ISSN 1942-2628, E-ISSN 1942-2628, Vol. 6, nr 3-4, s. 272-282Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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.

  • 8. Jacobs, Rianne
    et al.
    Lesaffre, Emmanuel
    Teunis, Peter F. M.
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    van de Kassteele, Jan
    Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection2019Inngår i: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 28, nr 4, s. 1126-1140Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.

  • 9. Jombart, Thibaut
    et al.
    Aanensen, David M.
    Baguelin, Marc
    Birrell, Paul
    Cauchemez, Simon
    Camacho, Anton
    Colijn, Caroline
    Collins, Caitlin
    Cori, Anne
    Didelot, Xavier
    Fraser, Christophe
    Frost, Simon
    Hens, Niel
    Hugues, Joseph
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Opatowski, Lulla
    Rambautm, Andrew
    Ratmann, Oliver
    Soubeyrand, Samuel
    Suchard, Marc A.
    Wallinga, Jacco
    Ypma, Rolf
    Ferguso, Neil
    OutbreakTools: A new platform for disease outbreak analysis using the R software2014Inngår i: Epidemics, ISSN 1755-4365, E-ISSN 1878-0067, Vol. 7, s. 28-34Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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.

  • 10. Lantos, Paul M.
    et al.
    Hoffman, Kate
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Anderson, Benjamin
    Gray, Gregory C.
    Are People Living Near Modern Swine Production Facilities at Increased Risk of Influenza Virus Infection?2016Inngår i: Clinical Infectious Diseases, ISSN 1058-4838, E-ISSN 1537-6591, Vol. 63, nr 12, s. 1558-1563Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background

    Swine can harbor influenza viruses that are pathogenic to humans. Previous studies support an increased risk of human influenza cases among individuals with swine contact. North Carolina has the second-largest swine industry in the United States.

    Methods

    We investigated the spatiotemporal association between influenza-like illnesses (ILIs) and licensed swine operations from 2008 to 2012 in North Carolina. We determined the week in which ILI cases peaked and statistically estimated their week of onset. This was performed for all 100 North Carolina counties for 4 consecutive influenza seasons. We used linear models to correlate the number of permitted swine operations per county with the weeks of onset and peak ILI activity.

    Results

    We found that during the 2009–2010 and 2010–2011 influenza seasons, both seasons in which the pandemic 2009 H1N1 influenza A virus circulated, ILI peaked earlier in counties with a higher number of licensed swine operations. We did not observe this in 2008–2009 or 2011–2012, nor did we observe a relationship between ILI onset week and number of swine operations.

    Conclusions

    Our findings suggest that concentrated swine feeding operations amplified transmission of influenza during years in which H1N1 was circulating. This has implications for vaccine strategies targeting swine workers, as well as virologic surveillance in areas with large concentrations of swine.

  • 11. Meyer, Sebastian
    et al.
    Held, Leonhard
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance2017Inngår i: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 77, nr 11Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports) in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.

  • 12. Salmon, M.
    et al.
    Schumacher, D.
    Burmann, H.
    Frank, C.
    Claus, H.
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Robert Koch Institute, Germany.
    A system for automated outbreak detection of communicable diseases in Germany2016Inngår i: Eurosurveillance, ISSN 1025-496X, E-ISSN 1560-7917, Vol. 21, nr 13, s. 47-53Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute.

  • 13. Salmon, Maelle
    et al.
    Schumacher, Dirk
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance2016Inngår i: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 70, nr 10, s. 1-35Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Public health surveillance aims at lessening disease burden by, e.g., timely recognizing emerging outbreaks in case of infectious diseases. Seen from a statistical perspective, this implies the use of appropriate methods for monitoring time series of aggregated case reports. This paper presents the tools for such automatic aberration detection offered by the R package surveillance. We introduce the functionalities for the visualization, modeling and monitoring of surveillance time series. With respect to modeling we focus on univariate time series modeling based on generalized linear models (GLMs), multivariate GLMs, generalized additive models and generalized additive models for location, shape and scale. Applications of such modeling include illustrating implementational improvements and extensions of the well-known Farrington algorithm, e.g., by spline-modeling or by treating it in a Bayesian context. Furthermore, we look at categorical time series and address overdispersion using beta-binomial or Dirichlet-multinomial modeling. With respect to monitoring we consider detectors based on either a Shewhart-like single timepoint comparison between the observed count and the predictive distribution or by likelihood-ratio based cumulative sum methods. Finally, we illustrate how surveillance can support aberration detection in practice by integrating it into the monitoring workflow of a public health institution. Altogether, the present article shows how well surveillance can support automatic aberration detection in a public health surveillance context.

  • 14. Salmon, Maelle
    et al.
    Schumacher, Dirk
    Stark, Klaus
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Bayesian outbreak detection in the presence of reporting delays2015Inngår i: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 57, nr 6, s. 1051-1067Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    One use of infectious disease surveillance systems is the statistical aberration detection performed on time series of counts resulting from the aggregation of individual case reports. However, inherent reporting delays in such surveillance systems make the considered time series incomplete, which can be an impediment to the timely detection and thus to the containment of emerging outbreaks. In this work, we synthesize the outbreak detection algorithms of Noufaily etal.(2013) and Manitz and Hohle(2013) while additionally addressing right truncation caused by reporting delays. We do so by considering the resulting time series as an incomplete two-way contingency table which we model using negative binomial regression. Our approach is defined in a Bayesian setting allowing a direct inclusion of all sources of uncertainty in the derivation of whether an observed case count is to be considered an aberration. The proposed algorithm is evaluated both on simulated data and on the time series of Salmonella Newport cases in Germany in 2011. Altogether, our method aims at allowing timely aberration detection in the presence of reporting delays and hence underlines the need for statistical modeling to address complications of reporting systems. An implementation of the proposed method is made available in the R package surveillance as the function bodaDelay.

  • 15.
    Stocks, Theresa
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Britton, Tom
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany2018Inngår i: Biostatistics, ISSN 1465-4644, E-ISSN 1468-4357Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Despite the wide application of dynamic models in infectious disease epidemiology, the particular modeling of variability in the different model components is often subjective rather than the result of a thorough model selection process. This is in part because inference for a stochastic transmission model can be difficult since the likelihood is often intractable due to partial observability. In this work, we address the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models. For this, we perform inference for six partially observed Markov process models, which assume the same underlying transmission dynamics, but differ with respect to the amount of variability they allow for. The inference framework for the stochastic transmission models is provided by iterated filtering methods, which are readily implemented in the R package pomp by King and others (2016, Statistical inference for partially observed Markov processes via the R package pomp. Journal of Statistical Software 69, 1–43). We illustrate our approach on German rotavirus surveillance data from 2001 to 2008, discuss practical difficulties of the methods used and calculate a model based estimate for the basic reproduction number R0 using these data.

  • 16.
    Stocks, Theresa
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Britton, Tom
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Pomp-astic inference methods for epidemic models illustrated on German rotavirus surveillance dataManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Infectious disease surveillance data often provides only partial information about the progression of the disease in the individual while disease transmission is often modelled using complex mathematical models for large scale data, where variability only enters through a stochastic observation process. In this work it is shown that a rather simplistic, but truly stochastic transmission model, is competitive with respect to model fit when compared with more detailed deterministic transmission models and even preferable because the role of each parameter and its identifiability is clearly understood in the simpler model. The inference framework for the stochastic model is provided by iterated filtering methods which are readily implemented in the R package pomp available from the comprehensive R archive network (CRAN). We illustrate our findings on German rotavirus surveillance data from 2001 to 2008 and calculate a model based estimate for the reproduction number R0 using these data.

  • 17. Weidemann, Felix
    et al.
    Dehnert, Manuel
    Koch, Judith
    Wichmann, Ole
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Robert Koch Institute, Germany.
    Bayesian parameter inference for dynamic infectious disease modelling: Rotavirus in Germany2014Inngår i: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 33, nr 9, s. 1580-1599Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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.

  • 18. Weidemann, Felix
    et al.
    Dehnert, Manuel
    Koch, Judith
    Wichmann, Ole
    Höhle, Michael
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Modelling the epidemiological impact of rotavirus vaccination in Germany - A Bayesian approach2014Inngår i: Vaccine, ISSN 0264-410X, E-ISSN 1873-2518, Vol. 32, nr 40, s. 5250-5257Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    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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