Publications by authors named "Ronald Meester"

The interaction between biological tissue and electromagnetic fields (EMF) is a topic of increasing interest due to the rising prevalence of background EMF in the past decades. Previous studies have attempted to measure the effects of EMF on brainwaves using EEG recordings, but are typically hampered by experimental and environmental factors. In this study, we present a framework for measuring the impact of EMF on EEG while controlling for these factors.

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The purpose of this paper is to investigate how to interpret the outcome of a test-negative design study. We does so by systematically reviewing the properties of the design in relation to its potential applications. We first argue that the application of the design does not depend on certain assumptions (as is sometimes articulated in the literature), which may open new opportunities for using the design.

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We comment on the contributions of Dahlman and of Fenton et al., who both suggested a Bayesian approach to analyze the Simonshaven case. We argue that analyzing a full case with a Bayesian approach is not feasible, and that there are serious problems with assigning actual numbers to probabilities and priors.

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This paper offers a response to the 2015 article of Lucas and Henneberg "Are human faces unique?" (Forensic Sci. Int. 257, 514.

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In de Zoete et al. (2015) a framework for the evaluation of evidence when an individual is a suspect of two separate offenses (based on Evett et al., 2006) is implemented using a Bayesian network.

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We study the spread of stochastic SIR (Susceptible [Formula: see text] Infectious [Formula: see text] Recovered) epidemics in two types of structured populations, both consisting of schools and households. In each of the types, every individual is part of one school and one household. In the independent partition model, the partitions of the population into schools and households are independent of each other.

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Recently, p-values have been suggested to explain the strength of a likelihood ratio that evaluates DNA evidence. It has been argued that likelihood ratios would be difficult to explain in court and that p-values would offer an alternative that is easily explained. In this article, we argue that p-values should not be used in this context.

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Familial searching is the process of finding potential relatives of the donor of a crime scene profile in a DNA database. Several authors have proposed strategies for generating candidate lists of potential relatives. This paper reviews four strategies and investigates theoretical properties as well as empirical behavior, using a comprehensive simulation study on mock databases.

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When a Y-chromosomal and a (partial) autosomal DNA profile are obtained from one crime sample, and both profiles match the suspect's profiles, we would like to know the combined evidential value. To calculate the likelihood ratio of observing the autosomal and Y-chromosomal DNA profiles combined, we need to know the conditional random match probability of the observed autosomal DNA profile, given the Y-chromosomal match. We examine this conditional probability in two ways: (1) with a database containing data of 2,085 men and (2) using a simulation model.

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William Dembski (No free lunch: why specified complexity cannot be purchased without intelligence, 2002) claimed that the NFL theorems from optimization theory render darwinian biological evolution impossible. Häggström (Biology and Philosophy 22:217-230, 2007) argued that the NFL theorems are not relevant for biological evolution at all, since the assumptions of the NFL theorems are not met. Although I agree with Häggström (Biology and Philosophy 22:217-230, 2007), in this article I argue that the NFL theorems should be interpreted as dealing with an extreme case within a much broader context.

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We argue that it is, in principle, not difficult to deal with selection effects in forensic science. If a suspect is selected through a process that is related to the forensic evidence, then the strength of the evidence will be compensated by very small prior odds. No further correction is necessary.

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We investigated the influence of the spatial pattern of farms on the geographical spread of infectious livestock diseases, such as classical swine fever, foot-and-mouth disease and avian influenza in a combined analytical-numerical approach. Our purpose of this paper is to develop a method to identify the areas in which an infection has the potential to spread in an outbreak. In our model, each infected farm can infect neighbouring farms and the probability of transmission is a function of the inter-farm distance (spatial kernel).

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This paper is concerned with a stochastic model, describing outbreaks of infectious diseases that have potentially great animal or human health consequences, and which can result in such severe economic losses that immediate sets of measures need to be taken to curb the spread. During an outbreak of such a disease, the environment that the infectious agent experiences is therefore changing due to the subsequent control measures taken. In our model, we introduce a general branching process in a changing (but not random) environment.

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Does the evidential strength of a DNA match depend on whether the suspect was identified through database search or through other evidence ("probable cause")? In Balding and Donnelly (1995, Journal of the Royal Statistical Society, Series A 158, 21-53) and elsewhere, it has been argued that the evidential strength is slightly larger in a database search case than in a probable cause case, while Stockmarr (1999, Biometrics 55, 671-677) reached the opposite conclusion. Both these approaches use likelihood ratios. By making an excursion to a similar problem, the two-stain problem, we argue in this article that there are certain fundamental difficulties with the use of a likelihood ratio, which can be avoided by concentrating on the posterior odds.

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We propose a new method to analyze outbreak data of an infectious disease such as classical swine fever. The underlying model is a two-type branching process. It is used to deduce information concerning the epidemic from detected cases.

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