Comparative analysis of the yields of dicentrics and chromosomal translocations.

Purpose: Chromosomal dicentrics and translocations are commonly employed as biomarkers to estimate radiation doses. The main goal of this article is to perform a comparative analysis of yields of both types of aberrations. The objective is to determine if there are relevant distinctions between both yields, allowing for a comprehensive assessment of their respective suitability and accuracy in the estimation of radiation doses.

Distinguishing between a power law and a Pareto distribution.

This paper introduces the location Pareto distribution as a natural extension of the power law distribution and gives a likelihood ratio test for choosing between the two models. Some properties of the distribution and test are thoroughly investigated, and applications to real data are provided. For large values of the observations the two models perform similarly; this explains why some classical approaches are very insensitive to the differentiation between them.

Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

Background: The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data provided by official sources were not always reliable due to data collection issues and to the high proportion of asymptomatic cases. In this work, a flexible framework is proposed, with the objective of quantifying the severity of misreporting in a time series and reconstructing the most likely evolution of the process.

Evaluation of γ-H2AX foci distribution among different peripheral blood mononucleated cell subtypes.

Introduction: The detection of γ-H2AX foci in peripheral blood mononucleated cells (PBMCs) has been incorporated as an early assay for biological dosimetry. However, overdispersion in the γ-H2AX foci distribution is generally reported. In a previous study from our group, it was suggested that overdispersion could be caused by the fact that when evaluating PBMCs, different cell subtypes are analyzed, and that these could differ in their radiosensitivity.

Biodose Tools: an R shiny application for biological dosimetry.

Introduction: In the event of a radiological accident or incident, the aim of biological dosimetry is to convert the yield of a specific biomarker of exposure to ionizing radiation into an absorbed dose. Since the 1980s, various tools have been used to deal with the statistical procedures needed for biological dosimetry, and in general those who made several calculations for different biomarkers were based on closed source software. Here we present a new open source program, Biodose Tools, that has been developed under the umbrella of RENEB (Running the European Network of Biological and retrospective Physical dosimetry).

Radiation dose estimation with time-since-exposure uncertainty using the [Formula: see text]-H2AX biomarker.

To predict the health effects of accidental or therapeutic radiation exposure, one must estimate the radiation dose that person received. A well-known ionising radiation biomarker, phosphorylated [Formula: see text]-H2AX protein, is used to evaluate cell damage and is thus suitable for the dose estimation process. In this paper, we present new Bayesian methods that, in contrast to approaches where estimation is carried out at predetermined post-irradiation times, allow for uncertainty regarding the time since radiation exposure and, as a result, produce more precise results.

Optimal Stein-type goodness-of-fit tests for count data.

Common count distributions, such as the Poisson (binomial) distribution for unbounded (bounded) counts considered here, can be characterized by appropriate Stein identities. These identities, in turn, might be utilized to define a corresponding goodness-of-fit (GoF) test, the test statistic of which involves the computation of weighted means for a user-selected weight function f. Here, the choice of f should be done with respect to the relevant alternative scenario, as it will have great impact on the GoF-test's performance.

Analysis of zero inflated dichotomous variables from a Bayesian perspective: application to occupational health.

Background: Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero).

Methods: This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structural and non-structural).

New statistical model for misreported data with application to current public health challenges.

The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

Establishment and validation of surface model for biodosimetry based on γ-H2AX foci detection.

Introduction: In the event of a radiation accident detecting γ-H2AX foci is being accepted as fast method for triage and dose assessment. However, due to their disappearance kinetics, published calibrations have been constructed at specific post-irradiation times.

Objectives: To develop a surface, or tridimensional, model to estimate doses at times not included in the calibration analysis, and to validate it.

Cumulated burden of COVID-19 in Spain from a Bayesian perspective.

Background: The main goal of this work is to estimate the actual number of cases of COVID-19 in Spain in the period 31 January 2020 to 01 June 2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately re-estimate the lethality of the disease in Spain, taking into account unreported cases.

Methods: A hierarchical Bayesian model recently proposed in the literature has been adapted to model the actual number of COVID-19 cases in Spain.

Quantifying the under-reporting of uncorrelated longitudal data: the genital warts example.

Background: Genital warts are a common and highly contagious sexually transmitted disease. They have a large economic burden and affect several aspects of quality of life. Incidence data underestimate the real occurrence of genital warts because this infection is often under-reported, mostly due to their specific characteristics such as the asymptomatic course.

Online Cost-Effectiveness ANalysis (OCEAN): a user-friendly interface to conduct cost-effectiveness analyses for cervical cancer.

Background: Most cost-effectiveness analyses in the context of cervical cancer prevention involve the use of mathematical models to simulate HPV infection, cervical disease and prevention strategies. However, it is common for professionals who would need to perform these analyses to not be familiar with the models. This work introduces the Online Cost-Effectiveness ANalysis tool, featuring an easy-to-use web interface providing health professionals, researchers and decision makers involved in cervical cancer prevention programmes with a useful instrument to conduct complex cost-effectiveness analyses, which are becoming an essential tool as an approach for supporting decision-making that involves important trade-offs.

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.

Background: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels.

A nonparametric method of estimation of the population size in capture-recapture experiments.

A recent method for estimating a lower bound of the population size in capture-recapture samples is studied. Specifically, some asymptotic properties, such as strong consistency and asymptotic normality, are provided. The introduced estimator is based on the empirical probability generating function (pgf) of the observed data, and it is consistent for count distributions having a log-convex pgf ( -class).

Untangling serially dependent underreported count data for gender-based violence.

Underreporting in gender-based violence data is a worldwide problem leading to the underestimation of the magnitude of this social and public health concern. This problem deteriorates the data quality, providing poor and biased results that lead society to misunderstand the actual scope of this domestic violence issue. The present work proposes time series models for underreported counts based on a latent integer autoregressive of order 1 time series with Poisson distributed innovations and a latent underreporting binary state, that is, a first-order Markov chain.

Probability estimation of a Carrington-like geomagnetic storm.

Intense geomagnetic storms can cause severe damage to electrical systems and communications. This work proposes a counting process with Weibull inter-occurrence times in order to estimate the probability of extreme geomagnetic events. It is found that the scale parameter of the inter-occurrence time distribution grows exponentially with the absolute value of the intensity threshold defining the storm, whereas the shape parameter keeps rather constant.

AN EXACT GOODNESS-OF-FIT TEST BASED ON THE OCCUPANCY PROBLEMS TO STUDY ZERO-INFLATION AND ZERO-DEFLATION IN BIOLOGICAL DOSIMETRY DATA.

The goal in biological dosimetry is to estimate the dose of radiation that a suspected irradiated individual has received. For that, the analysis of aberrations (most commonly dicentric chromosome aberrations) in scored cells is performed and dose response calibration curves are built. In whole body irradiation (WBI) with X- and gamma-rays, the number of aberrations in samples is properly described by the Poisson distribution, although in partial body irradiation (PBI) the excess of zeros provided by the non-irradiated cells leads, for instance, to the Zero-Inflated Poisson distribution.

Syndromic surveillance for West Nile virus using raptors in rehabilitation.

Background: Wildlife rehabilitation centers routinely gather health-related data from diverse species. Their capability to signal the occurrence of emerging pathogens and improve traditional surveillance remains largely unexplored. This paper assessed the utility for syndromic surveillance of raptors admitted to The Raptor Center (TRC) to signal circulation of West Nile Virus (WNV) in Minnesota between 1990 and 2014.

Influenza Herd-Level Prevalence and Seasonality in Breed-to-Wean Pig Farms in the Midwestern United States.

Influenza is a costly disease for pig producers and understanding its epidemiology is critical to control it. In this study, we aimed to estimate the herd-level prevalence and seasonality of influenza in breed-to-wean pig farms, evaluate the correlation between influenza herd-level prevalence and meteorological conditions, and characterize influenza genetic diversity over time. A cohort of 34 breed-to-wean farms with monthly influenza status obtained over a 5-year period in piglets prior to wean was selected.

Uncertainty of fast biological radiation dose assessment for emergency response scenarios.

Purpose: Reliable dose estimation is an important factor in appropriate dosimetric triage categorization of exposed individuals to support radiation emergency response.

Materials And Methods: Following work done under the EU FP7 MULTIBIODOSE and RENEB projects, formal methods for defining uncertainties on biological dose estimates are compared using simulated and real data from recent exercises.

Results: The results demonstrate that a Bayesian method of uncertainty assessment is the most appropriate, even in the absence of detailed prior information.

Under-reported data analysis with INAR-hidden Markov chains.

In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm.

Testing universality in critical exponents: The case of rainfall.

One of the key clues to consider rainfall as a self-organized critical phenomenon is the existence of power-law distributions for rain-event sizes. We have studied the problem of universality in the exponents of these distributions by means of a suitable statistic whose distribution is inferred by several variations of a permutational test. In contrast to more common approaches, our procedure does not suffer from the difficulties of multiple testing and does not require the precise knowledge of the uncertainties associated to the power-law exponents.