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.
We assessed hepatitis E virus (HEV) antibody seroprevalence in a sample of the adult population in Germany. Overall HEV IgG prevalence was 16.8% (95% CI 15.6%-17.9%) and increased with age, leveling ...off at >60 years of age. HEV is endemic in Germany, and the lifetime risk for exposure is high.
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.
The clinical spectrum following infection with Shiga toxin-producing Escherichia coli (STEC) is wide ranging and includes hemorrhagic colitis and life-threatening hemolytic uremic syndrome (HUS). ...Severity of STEC illness depends on patients' age and strongly on the infecting strains' virulence. Serogroup O157 is often assumed to be more virulent than others. Age-adjusted population-based data supporting this view are lacking thus far. We conducted a large retrospective cohort study among patients of community-acquired gastroenteritis or HUS diagnosed with STEC infection, reported in Germany January 2004 through December 2011. Age-adjusted risks for reported hospitalization and death, as proxies for disease severity, were estimated for STEC serogroups separately, and compared with STEC O157 (reference group) using Poisson regression models with robust error estimation. A total of 8,400 case-patients were included in the analysis; for 2,454 (29%) and 30 (0.4%) hospitalization and death was reported, respectively. Highest risks for hospitalization, adjusted for age and region of residence, were estimated for STEC O104 (68%; risk ratio RR, 1.33; 95% confidence interval CI, 1.19-1.45), followed by STEC O157 (46%). Hospitalization risks for the most prevalent non-O157 serogroups (O26, O103, O91, O145, O128, O111) were consistently and markedly lower than for O157, with the highest RR for O145 (0.54; 95% CI, 0.41-0.70) and the lowest for O103 (0.27; 95% CI, 0.20-0.35). Mortality risk of O104 was similar to O157 (1.2% each), but the group of all other non-O157 STEC had only 1/10 the risk (RR, 0.09; 95% CI, 0.02-0.32) compared to O157. The study provides population-based and age-adjusted evidence for the exceptional high virulence of STEC O157 in relation to non-O157 STEC other than O104. Timely diagnosis and surveillance of STEC infections should prioritize HUS-associated E. coli, of which STEC O157 is the most important serogroup.
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.
As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of ...transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package
. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
In order to detect levels of pre-existing cross-reactive antibodies in different age groups and to measure age-specific infection rates of the influenza A (H1N1) 2009 pandemic in Germany, we ...conducted a seroprevalence study based on samples from an ongoing nationwide representative health survey.
We analysed 845 pre-pandemic samples collected between 25 Nov 2008 and 28 Apr 2009 and 757 post-pandemic samples collected between 12 Jan 2010 and 24 Apr 2010. Reactive antibodies against 2009 pandemic influenza A (H1N1) virus (pH1N1) were detected using a haemagglutination inhibition test (antigen A/California/7/2009). Proportions of samples with antibodies at titre ≥ 40 and geometric mean of the titres (GMT) were calculated and compared among 6 age groups (18-29, 30-39, 40-49, 50-59, 60-69, ≥ 70 years). The highest proportions of cross-reactive antibodies at titre ≥ 40 before the pandemic were observed among 18-29 year olds, 12.5% (95% CI 7.3-19.5%). The highest increase in seroprevalence between pre- and post-pandemic was also observed among 18-29 year olds, 29.9% (95% CI 16.7-43.2%). Effects of sampling period (pre- and post-pandemic), age, sex, and prior influenza immunization on titre were investigated with Tobit regression analysis using three birth cohorts (after 1976, between 1957 and 1976, and before 1957). The GMT increased between the pre- and post-pandemic period by a factor of 10.2 (95% CI 5.0-20.7) in the birth cohort born after 1976, 6.3 (95% CI 3.3-11.9) in those born between 1957 and 1976 and 2.4 (95% CI 1.3-4.3) in those born before 1957.
We demonstrate that infection rates differed among age groups and that the measured pre-pandemic level of cross-reactive antibodies towards pH1N1 did not add information in relation to protection and prediction of the most affected age groups among adults in the pandemic.
Measures for the accuracy assessment of Digital Elevation Models (DEMs) are discussed and characteristics of DEMs derived from laser scanning and automated photogrammetry are presented. Such DEMs are ...very dense and relatively accurate in open terrain. Built-up and wooded areas, however, need automated filtering and classification in order to generate terrain (bare earth) data when Digital Terrain Models (DTMs) have to be produced. Automated processing of the raw data is not always successful. Systematic errors and many outliers at both methods (laser scanning and digital photogrammetry) may therefore be present in the data sets. We discuss requirements for the reference data with respect to accuracy and propose robust statistical methods as accuracy measures. Their use is illustrated by application at four practical examples. It is concluded that measures such as median, normalized median absolute deviation, and sample quantiles should be used in the accuracy assessment of such DEMs. Furthermore, the question is discussed how large a sample size is needed in order to obtain sufficiently precise estimates of the new accuracy measures and relevant formulae are presented.
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 Software69, 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 $R_0$ using these data.
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