Modeling latent spatio-temporal disease incidence using penalized composite link models

Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, which may be of interest to researchers and public health officials, are overlooked. We propose to use the penalized composite link model (Eilers PCH (2007)), combined with spatio-temporal P-splines methodology (Lee D.-J., Durban M (2011)) to estimate the underlying trend within data that have been aggregated not only in space, but also in time. Model estimation is carried out within a generalized linear mixed model framework, and sophisticated algorithms are used to speed up computations that otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands.Grant No. MTM2014-52184-P awarded to MD, and DA, and by Agencia Estatal de Investigació

EU Ringonderzoek voedsel-I (2006) Bacteriologische detectie van Salmonella in rundergehakt

De Europese Nationale Referentie Laboratoria (NRLs) voor Salmonella hebben in een ringonderzoek hoge en lage concentraties Salmonella aangetoond in rundergehakt. Hiermee hebben ze laten zien dat ze voldoen aan de gestelde eisen. De Modified Semi-solid Rappaport Vassiliadis (MSRV), een analysemethode die veel gebruikt wordt voor Salmonella in dierenmest, bleek de beste methode voor het aantonen van Salmonella in rundergehakt. Vijfentwintig referentielaboratoria deden in september 2006 mee aan een ringonderzoek van het Communautair Referentie Laboratorium (CRL) voor Salmonella. Doel was in eerste instantie om na te gaan of de laboratoria Salmonella in gehakt goed konden aantonen. In tweede instantie werd ook onderzocht wat de beste analysemethode was voor het aantonen van Salmonella in rundergehakt. Ieder laboratorium kreeg een pakket toegestuurd met rundergehakt en 35 gelatine capsules met melkpoeder van verschillende besmettingsniveaus Salmonella. De laboratoria moesten volgens voorschrift gehakt en capsules samenvoegen en onderzoeken op de aanwezigheid van Salmonella. Voor het onderzoek gebruikten de laboratoria drie methoden: Rappaport Vassiliadis Soya broth (RVS), Mueller Kauffmann Tetrathionaat met novobiocine (MKTTn) en Modified Semi-solid Rappaport Vassiliadis (MSRV). De eerste twee methoden (RVS en MKKTn) staan bekend als internationaal voorgeschreven voor de analyse van Salmonella in levensmiddelen. De derde methode (MSRV) wordt gebruikt om Salmonella in dierlijke mest aan te tonen. Met een van de levensmiddelenmethoden (MKTTn) vonden alle laboratoria in slechts 88% van de monsters Salmonella. De methode voor dierlijke mest (MSRV) bleek de beste resultaten te geven. Hiermee vonden alle laboratoria in 99% van de besmette monsters Salmonella. De levensmiddelenmethode MKTTn blijkt dus niet de meest optimale methode te zijn voor het aantonen van Salmonella in rundergehakt.The European National Reference Laboratories (NRLs) for Salmonella were able to detect high and low levels of Salmonella in a ring trial using minced beef as matrix, thereby reaching the level of good performance. The Modified Semi-solid Rappaport Vassiliadis (MSRV), a method often used for the detection of Salmonella in animal faeces, turned out to be the best method for minced beef. This was one outcome of the inter-laboratory comparison study organized by the Community Reference Laboratory (CRL) for Salmonella on the detection of Salmonella spp. in a food matrix in September 2006. The first, and most important goal, was to see if the 25 participating laboratories in this study could detect Salmonella in minced beef. The second goal was to compare the different analysis methods for the detection of Salmonella in minced beef. Each laboratory received a package containing minced beef and 35 gelatin capsules containing different levels of Salmonella. According to the instructions, the laboratories spiked the meat with the capsules and tested those samples for the presence of Salmonella. The laboratories used three methods for running this test: Rappaport Vassiliadis Soya broth (RVS), Mueller Kauffmann Tetrathionate novobiocin broth (MKTTn) and Modified Semi-solid Rappaport Vassiliadis (MSRV). The first two methods are internationally prescribed for the detection of Salmonella in food, while the third (MSRV) is prescribed for the detection of Salmonella in veterinary faeces. All laboratories found Salmonella in just 88% of the samples using one of the food methods (MKTTn). The method for the veterinary samples, MSRV, gave the best results, with 99% of all laboratories detecting Salmonella in the spiked samples. The MKTTn food method is therefore not the optimal medium for the detection of Salmonella in minced beef.European CommissionLegislation Veterinaire et Zootechniqu

Model-based Geostatistical Interpolation of the annual number of ozone exceedance days in the Netherlands

This paper discusses two model-based geostatistical methods for spatial interpolation of the number of days that ground level ozone exceeds a threshold level. The first method assumes counts to approximately follow a Poisson distribution, while the second method assumes a log-Normal distribution. First, these methods were compared using an extensive data set covering the Netherlands, Belgium and Germany. Second, the focus was placed on only the Netherlands, where only a small data set was used. Bayesian techniques were used for parameter estimation and interpolation. Parameter estimates are comparable due to the log-link in both models. Incorporating data from adjacent countries improves parameter estimation. The Poisson model predicts more accurately (maximum kriging standard deviation of 2.16 compared to 2.69) but shows smoother surfaces than the log-Normal model. The log-Normal approach ensures a better representation of the observations and gives more realistic patterns (an RMSE of 2.26 compared to 2.44). Model-based geostatistical procedures are useful to interpolate limited data sets of counts of ozone exceedance days. Spatial risk estimates using existing prior information can be made relating health effects to environmental threshold

Determinants of Rotavirus Transmission A Lag Nonlinear Time Series Analysis

Development and application of statistical models for medical scientific researc

Determinants of Rotavirus Transmission A Lag Nonlinear Time Series Analysis

Development and application of statistical models for medical scientific researc

Infectious reactivation of cytomegalovirus explaining age- and sex-specific patterns of seroprevalence.

Human cytomegalovirus (CMV) is a herpes virus with poorly understood transmission dynamics. Person-to-person transmission is thought to occur primarily through transfer of saliva or urine, but no quantitative estimates are available for the contribution of different infection routes. Using data from a large population-based serological study (n = 5,179), we provide quantitative estimates of key epidemiological parameters, including the transmissibility of primary infection, reactivation, and re-infection. Mixture models are fitted to age- and sex-specific antibody response data from the Netherlands, showing that the data can be described by a model with three distributions of antibody measurements, i.e. uninfected, infected, and infected with increased antibody concentration. Estimates of seroprevalence increase gradually with age, such that at 80 years 73% (95%CrI: 64%-78%) of females and 62% (95%CrI: 55%-68%) of males are infected, while 57% (95%CrI: 47%-67%) of females and 37% (95%CrI: 28%-46%) of males have increased antibody concentration. Merging the statistical analyses with transmission models, we find that models with infectious reactivation (i.e. reactivation that can lead to the virus being transmitted to a novel host) fit the data significantly better than models without infectious reactivation. Estimated reactivation rates increase from low values in children to 2%-4% per year in women older than 50 years. The results advance a hypothesis in which transmission from adults after infectious reactivation is a key driver of transmission. We discuss the implications for control strategies aimed at reducing CMV infection in vulnerable groups

Statistical air quality mapping

This thesis handles statistical mapping of air quality data. Policy makers require more and more detailed air quality information to take measures to improve air quality. Besides, researchers need detailed air quality information to assess health effects. Accurate and spatially highly resolved maps of air pollution levels form a basis. Since policy makers and researchers tend to focus more and more on uncertainties as well, the question is how precise these concentration maps are.To base concentration maps on measurements of air quality only, every km2 should be monitored. Measurements, however, are only taken at a limited number of locations, so between the monitoring locations relevant information will be missing or can only be predicted, i.e. interpolated, leading to uncertainty in the map. Furthermore, no information about the physical and chemical processes about the concerned component is taken into account. On the other hand, concentration maps can also be based on physical and chemical processes modeling of components only. This model output covers the full domain on a fine-mazed grid. All dispersion models are imperfect however, which may lead to biased output and uncertainties.A combination of the two approaches always results into more detailed and more accurate maps. In this thesis this is done by means of a geostatistical approach: kriging with external drift (KED). KED allows mapping of a primary variable that is accurate and precise but only available at a limited number of locations, and a secondary variable that covers the full domain on a fine-mazed grid but is less accurate.First, we focus on the use of atmospheric dispersion model output as secondary information source to compensate for the loss of spatial precision caused by a reduction in the Dutch air quality monitoring network in the mid-nineteen eighties. We compare KED with universal kriging. The impact of several parameter estimation and spatial interpolation methods, the number of observations and configuration of the network on uncertainty are quantified by cross-validation. With KED, more accurate and precise predictions are obtained where observations were sparse. However, the dispersion model output in this context was considered to be deterministic, i.e. without uncertainties, so the geostatistical model must be extended.We present a method, error-in-variable KED, which combines uncertain air quality measurements with uncertain secondary information from the atmospheric dispersion model. The new method combines KED and a measurement error model, and uses Bayesian techniques for inference. The method is flexible for assigning different error variances to both the primary information and secondary information at each location. We address actual NO2 data collected at an urban and a rural site in the Netherlands. Uncertainty assessments in terms of exceeding air quality standards are given.The error-in-variable KED procedure is further extended with a time component to assess future local NO2 concentrations near Rotterdam for the year 2010, focusing on uncertainties and exceedances of European air quality standards. The background concentration is determined by the extended error-in-variable KED. A local traffic contribution is added based on a local generic dispersion model with use of an emission scenario for 2010. This results in maps showing local NO2 concentrations, upper and lower limits, and probabilities of exceeding the air quality standard. The probabilistic measures are calculated in numbers and translated into words for easier communication to policy makers.Finally, the use of two secondary information sources is explored to map particulate matter (PM10) over Western Europe. It is almost impossible to get a consistent overview of PM10 concentrations based solely on ground based measurements because of differences between countries regarding monitoring methods used and monitoring station surroundings. We illustrate the use of statistical techniques to standardize the ground based measurements of PM10 and interpolate these standardized concentrations by combining them uncertain secondary information from a chemical transport model and from MODIS satellite observations of aerosol optical thickness. The secondary variables contain different information and a combination of both gives the most accurate and precise predictions and should therefore be preferred

Uncertainty assessment of local NO2 concentrations derived from error-in-variable external drift kriging and its relationship to the 2010 air quality standard

Local NO2 concentrations near Rotterdam (Netherlands) were assessed for the year 2010, focusing on the uncertainties and the changes in exceedance of European air quality standards. In the first step of the 2-step assessment method, the background contribution was determined by error-in-variable external drift kriging, where measurements and dispersion model output in the 1987-2003 period were combined. The result was subsequently extrapolated using dispersion model output and an emission scenario for 2010. In the second step, the local traffic contribution was added on the basis of a local generic dispersion model with use of an emission scenario for 2010. This resulted in maps showing local NO2 concentrations, upper and lower limits, and probabilities of exceeding the 40 mu g m(-3) air quality standard. The probabilistic measures were calculated in numbers and translated into words for easier communication. Using this method and scenario we found that within about 100m from the highways near Rotterdam the mean NO2 concentrations are likely to exceed the standard in 2010. The chance of exceeding the standard is unlikely up to 1 km from the highways, where the mean is expected to be below the standard in 2010

A model for external drift kriging with uncertain covariates applied to air quality measurements and dispersion model output

We present a method that combines uncertain air quality measurements with uncertain secondary information from an atmospheric dispersion model. The method combines external drift kriging and a measurement error (ME) model, and uses Bayesian techniques for inference. An illustration with simulated data shows what can theoretically be expected. The method is flexible for assigning different error variances to both the primary information and secondary information at each location. Next, we address actual NO2 data collected at an urban and a rural site in the Netherlands. Uncertainty assessments in terms of exceeding air quality standards are given. The study shows that biased uncertain secondary information can be used successfully in a spatial interpolation study at the national scal