Two basic formulas, for the mean and variance of the number of effects in an epidemiological cohort, are derived. The formula for variance shows “extra-binomial variation” or “overdispersion” when there is correlated uncertainty of the probability of an effect. The formulas were validated by a numerical Monte Carlo study. The method of including “epistemic” uncertainty discussed by Hofer (E. Ho...

Two cestode species, Fimbriaria fasciolaris (Pallas, 1781) Frölich, 1802 Cloacotaenia megalops (Nitzsch in Creplin, 1829) Wolffhügel, 1938 collected from Anas bahamensis Linné, 1758 and Amazonetta brasiliensis (Gmelin, 1758) in lagoons of the Maricá District, State of Rio de Janeiro, Brazil, are described. This is the first record of F. fasciolaris parasitizing A. bahamensis. The prevalence, in...

The likelihood ratio spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection applications. In order to better understand cluster mechanisms, an equivalent model-based approach is proposed to the spatial scan statistic that unifies currently loosely coupled methods for including ecological covariates in the spatial scan test. In addition, the uti...

We describe a stochastic model based on a branching process for analyzing surveillance data of infectious diseases that allows to make forecasts of the future development of the epidemic. The model is based on a Poisson branching process with immigration with additional adjustment for possible overdispersion. An extension to a longitudinal model for the multivariate case is described. The model...

Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis....

The article analyses the question whether women and men differ in their tastes for location factors. The question is answered by quantifying the impact of location characteristics on interregional migration flows across Germany. The analysis is based on a grouped conditional logit approach. We augment the framework by controlling for violation of the independence of irrelevant alternatives assu...

Program MARK provides a number of sophisticated analyses beyond just providing parameter estimates and their estimated conditional measures of precision. Advanced features include graphs of parameter estimates from 1 or more models, models constructed with the design matrix to estimate the mean of a set of real parameters, quasi-likelihood procedures to correct for overdispersion of the data, m...

This paper presents a new approach to identify and estimate the dispersion parameters for bivariate, trivariate and multivariate correlated binary data, not only with scalar value but also with matrix values. For this direction, we present some recent studies indicating the impact of overdispersion on the univariate data analysis and comparing a new approach with these studies. Following the pr...

We describe a mixed-effects hurdle model for zero-inflated longitudinal count data, where a baseline variable is included in the model specification. Association between the count data process and the endogenous baseline variable is modeled through a latent structure, assumed to be dependent across equations. We show how model parameters can be estimated in a finite mixture context, allowing fo...

We show how the rootogram – a graphical tool associated with the work of J. W. Tukey and originally used for assessing goodness of fit of univariate distributions – can help to diagnose and treat issues such as overdispersion and/or excess zeros in regression models for count data. Two empirical illustrations, from ethology and from public health, are included. The former employs a negative bin...