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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. Results The results of this work show that the real load of COVID-19 in Spain in the period considered is well above the data registered by the public health system. Specifically, the model estimates show that, cumulatively until 1 June 2020, there were 2 425 930 cases of COVID-19 in Spain with characteristics similar to those reported (95% credibility interval: 2 148 261–2 813 864), from which were actually registered only 518 664. Conclusions Considering the results obtained from the second wave of the Spanish seroprevalence study, which estimates 2 350 324 cases of COVID-19 produced in Spain, in the period of time considered, it can be seen that the estimates provided by the model are quite good. This work clearly shows the key importance of having good quality data to optimize decision-making in the critical context of dealing with a pandemic.

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. We also use the Laplace approximation method, which drastically cuts down on the time needed to get results. Real data are used to illustrate the methods, and analyses indicate that the models might be a practical choice for the [Formula: see text]-H2AX biomarker dose estimation process.

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. Methods Genital warts cases for the analysis were obtained from the Catalan public health system database (SIDIAP) for the period 2009-2016. People under 15 and over 94 years old were excluded from the analysis as the incidence of genital warts in this population is negligible. This work introduces a time series model based on a mixture of two distributions, capable of detecting the presence of under-reporting in the data. In order to identify potential differences in the magnitude of the under-reporting issue depending on sex and age, these covariates were included in the model. Results This work shows that only about 80% in average of genital warts incidence in Catalunya in the period 2009-2016 was registered, although the frequency of under-reporting has been decreasing over the study period. It can also be seen that this issue has a deeper impact on women over 30 years old. Conclusions Although this study shows that the quality of the registered data has improved over the considered period of time, the Catalan public health system is underestimating genital warts real burden in almost 10,000 cases, around 23% of the registered cases. The total annual cost is underestimated in about 10 million Euros respect the 54 million Euros annually devoted to genital warts in Catalunya, representing 0.4% of the total budget.

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). Results The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community. Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. Conclusions The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package (https://cran.r-project.org/package=bayesZIB).

Biological dosimetry, that is the estimation of the dose of an exposure to ionizing radiation by a biological parameter, is a very important tool in cases of radiation accidents. The score of dicentric chromosomes, considered to be the most accurate method for biological dosimetry, for low LET radiation and up to 5 Gy, fits very well to a linear-quadratic model of dose-effect curve assuming the Poisson distribution. The accuracy of this estimation raises difficulties for doses over 5 Gy, the highest dose of the majority of dose-effect curves used in biological dosimetry. At doses over 5 Gy most cells show difficulties in reaching mitosis and cannot be used to score dicentric chromosomes. In the present study with the treatment of lymphocyte cultures with caffeine and the standardization of the culture time, metaphases for doses up to 25 Gy have been analyzed. Here we present a new model for biological dosimetry, which includes a Gompertz-type function as the dose response, and also takes into account the underdispersion of aberration-among-cell distribution. The new model allows the estimation of doses of exposures to ionizing radiation of up to 25 Gy. Moreover, the model is more effective in estimating whole and partial body exposures than the classical method based on linear and linear-quadratic functions, suggesting their effectiveness and great potential to be used after high dose exposures of radiation.

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.

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. Results The users can run cost-effectiveness evaluations of cervical cancer prevention strategies without deep knowledge of the underlying mathematical model or any programming language, obtaining the most relevant costs and health outcomes in a user-friendly format. The results provided by the tool are consistent with the existing literature. Conclusions Having such a tool will be an asset to the cervical cancer prevention community, providing researchers with an easy-to-use instrument to conduct cost-effectiveness analyses.

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. Relevant theoretical properties of the models are derived, and the moment-based and maximum-based methods are presented for parameter estimation. The new time series models are applied to the quarterly complaints of domestic violence against women recorded in some judicial districts of Galicia (Spain) between 2007 and 2017. The models allow quantifying the degree of underreporting. A comprehensive discussion is presented, studying how the frequency and intensity of underreporting in this public health concern are related to some interesting socioeconomic and health indicators of the provinces of Galicia (Spain).

The Bayesian framework has been shown to be very useful in cytogenetic dose estimation. This approach allows description of the probability of an event in terms of previous knowledge, e.g. its expectation and/or its uncertainty. A new R package entitled radir (radiation inverse regression) has been implemented with the aim of reproducing a recent Bayesian-type dose estimation methodology. radir adopts the method of dose estimation under the Poisson assumption of the responses (the chromosomal aberrations counts) for the required dose-response curve (typically linear or quadratic). The individual commands are described in detail and relevant examples of the use of the methods and the corresponding radir software tools are given. The suitability of this methodology is highlighted and its application encouraged by providing a user-friendly command-type software interface within the R statistical software (version 3.1.1 or higher), which includes a complete manual.

This article is about how to improve the positional dispensing accuracy of low-volume (microliters) drops and to understand the factors affecting them. These same parameters can be investigated to reduce deleterious effects on dispensing performance. Many applications, such as those in immunoassay diagnostics, require accurate volumetric dispensing and are also less tolerant of other phenomena that cause the reagent to be located outside the target area of interest. In this article, we work with liquid inertia. This phenomenon is present in positional dispensing and affects its accuracy. The negative effect of liquid inertia is reduced by changing parameters such as pressure and the diameter of the involved conducts.

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. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John WileySons, Ltd.

A new zero-inflated Poisson model is introduced for the estimation of partial body irradiation dose and fraction of body irradiated. The Bayes factors are introduced as tools to help determine whether a data set of chromosomal aberrations obtained from a blood sample reflects partial or whole body irradiation. Two examples of simulated cytogenetic radiation exposure data are presented to demonstrate the usefulness of this methodology in cytogenetic biological dosimetry.

Within the field of cytogenetic biodosimetry, Poisson regression is the classical approach for modeling the number of chromosome aberrations as a function of radiation dose. However, it is common to find data that exhibit overdispersion. In practice, the assumption of equidispersion may be violated due to unobserved heterogeneity in the cell population, which will render the variance of observed aberration counts larger than their mean, and/or the frequency of zero counts greater than expected for the Poisson distribution. This phenomenon is observable for both full- and partial-body exposure, but more pronounced for the latter. In this work, different methodologies for analyzing cytogenetic chromosomal aberrations datasets are compared, with special focus on zero-inflated Poisson and zero-inflated negative binomial models. A score test for testing for zero inflation in Poisson regression models under the identity link is also developed.

Classical methods of assessing the uncertainty associated with radiation doses estimated using cytogenetic techniques are now extremely well defined. However, several authors have suggested that a Bayesian approach to uncertainty estimation may be more suitable for cytogenetic data, which are inherently stochastic in nature. The Bayesian analysis framework focuses on identification of probability distributions (for yield of aberrations or estimated dose), which also means that uncertainty is an intrinsic part of the analysis, rather than an 'afterthought'. In this paper Bayesian, as well as some more advanced classical, data analysis methods for radiation cytogenetics are reviewed that have been proposed in the literature. A practical overview of Bayesian cytogenetic dose estimation is also presented, with worked examples from the literature.

A number of authors have suggested that a Bayesian approach may be most appropriate for analysis of cytogenetic radiation dosimetry data. In the Bayesian framework, probability of an event is described in terms of previous expectations and uncertainty. Previously existing, or prior, information is used in combination with experimental results to infer probabilities or the likelihood that a hypothesis is true. It has been shown that the Bayesian approach increases both the accuracy and quality assurance of radiation dose estimates. New software entitled CytoBayesJ has been developed with the aim of bringing Bayesian analysis to cytogenetic biodosimetry laboratory practice. CytoBayesJ takes a number of Bayesian or ‘Bayesian like’ methods that have been proposed in the literature and presents them to the user in the form of simple user-friendly tools, including testing for the most appropriate model for distribution of chromosome aberrations and calculations of posterior probability distributions. The individual tools are described in detail and relevant examples of the use of the methods and the corresponding CytoBayesJ software tools are given. In this way, the suitability of the Bayesian approach to biological radiation dosimetry is highlighted and its wider application encouraged by providing a user-friendly software interface and manual in English and Russian.

Background High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to exploit this feature and distinguish expression profiles with higher resolution. RNA-seq data analysis methods so far have been mostly applied to data sets with few replicates and their default settings try to provide the best performance under this constraint. These methods are based on two well-known count data distributions: the Poisson and the negative binomial. The way to properly calibrate them with large RNA-seq data sets is not trivial for the non-expert bioinformatics user. Results Here we show that expression profiles produced by extensively-replicated RNA-seq experiments lead to a rich diversity of count data distributions beyond the Poisson and the negative binomial, such as Poisson-Inverse Gaussian or Pólya-Aeppli, which can be captured by a more general family of count data distributions called the Poisson-Tweedie. The flexibility of the Poisson-Tweedie family enables a direct fitting of emerging features of large expression profiles, such as heavy-tails or zero-inflation, without the need to alter a single configuration parameter. We provide a software package for R called implementing a new test for differential expression based on the Poisson-Tweedie family. Using simulations on synthetic and real RNA-seq data we show that yields P-values that are equally or more accurate than competing methods under different configuration parameters. By surveying the tiny fraction of sex-specific gene expression changes in human lymphoblastoid cell lines, we also show that accurately detects differentially expressed genes in a real large RNA-seq data set with improved performance and reproducibility over the previously compared methodologies. Finally, we compared the results with those obtained from microarrays in order to check for reproducibility. Conclusions RNA-seq data with many replicates leads to a handful of count data distributions which can be accurately estimated with the statistical model illustrated in this paper. This method provides a better fit to the underlying biological variability; this may be critical when comparing groups of RNA-seq samples with markedly different count data distributions. The package forms part of the Bioconductor project and it is available for download at http://www.bioconductor.org.

The aim of this prospective study is to evaluate prostanoid (prostacyclin and thromboxane) and lipid peroxide levels at the portal and hepatic veins, and their relation to immediate postoperative liver function. Nineteen patients with liver cirrhosis undergoing orthotopic liver transplantation were prospectively studied. Blood samples were obtained within 5 min and 1 and 2 hr after reperfusion of the new liver, through a catheter placed at the portal vein in the recipient and another at the left hepatic vein in the donor liver. Plasma prostacyclin and thromboxane were analyzed by HPLC and RIA. The formation of lipid peroxides was determined and expressed in terms of thiobarbituric acid-reacting substances. Immediate postoperative liver function was evaluated using the transaminase levels within the first 48 hr and the early postoperative graft function score, as described previously. After reperfusion, only determinations at 5 min were related with liver function. Either prostacyclin (R = -0.61, P = 0.004) levels at the hepatic vein or prostacyclin production (subtraction between hepatic and portal vein levels) (R = -0.47, P = 0.04) correlated significantly with the early postoperative graft function score. Besides, there was a significant relationship between lipid peroxide production as measured by thiobarbituric acid-reacting substances and a worse early postoperative graft function score (R = 0.61, P = .005). These results suggest that prostacyclin released after liver grafting attenuates preservation and reperfusion damage of the liver, supporting the hypothesis that there is an imbalance of prostanoids within the microvasculature in patients with a compromised postoperative liver function. Our results agree with the involvement of some degree of lipid peroxidation products in the damage of hepatocytes during anoxia and reperfusion.

In biological dosimetry by dicentric analysis, an exposure to radiation is considered non-homogeneous if the dicentric cell distribution shows overdispersion with respect to Poisson distribution. Traditionally, when this occurs, all non-homogeneous exposures are considered as partial-body exposures, assuming that there is only a mixture of irradiated and nonirradiated cells. The methods to estimate the dose in the irradiated fraction and the initial fraction of irradiated cells are based on separating which part of the cells without aberrations comes from the nonirradiated or irradiated fractions. In this study we show a new approach based on a mixed Poisson model, which allows for a distinction to be made between partial and heterogeneous exposures. To validate this approach blood samples from two donors, a male and a female, irradiated at different doses, were mixed at a 1:1 proportion to simulate partial and heterogeneous exposures. The results show a good agreement between the observed proportion of male and female cells and the proportion estimated by the model. Additionally, a good agreement was observed between the delivered doses, the initial fraction of cells and the ones estimated by the model. This good agreement was also observed after very high-dose irradiation (up to 17 Gy), when the lymphocyte cultures were treated with caffeine. Based on these results, we propose the use of this mixed Poisson model for a more accurate assessment of non-homogeneous exposures.

Peripheral blood was irradiated with 2, 3, 4 or 5 Gy of X rays and was mixed with nonirradiated blood at five different dilutions to simulate partial-body irradiations. Analysis by FISH was performed using whole-chromosome painting probes for chromosomes 1, 4 and 11 in combination with a pancentromeric probe. Chromosome aberrations affecting the painted fraction were classified according to the PAINT nomenclature; other unstable aberrations affecting the unpainted material were also recorded. To evaluate the suitability of painting for dose assessment in partial-body irradiations, the ability of the u test and a proposed s test to detect the expected overdispersion and the similarity between the real doses and the doses estimated using Dolphin's approach were considered. For short-term biodosimetry, compared with solid-stained dicentric analyses, the suitability of FISH painting techniques for the detection of partial-body exposures is reduced, because of the decrease in the frequency of aberrations detected by FISH and in the number of cells with two or more aberrations. For reconstruction of past doses, when only complete apparently simple translocations in cells free of unstable aberrations were considered, the detection of the overdispersion and the accuracy of dose estimations were dramatically reduced. In a partial-body exposure, as the original dose increased, the whole-body dose estimated a long time after irradiation would tend to be lower, and the difference from the original dose would tend to be greater.