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. The likelihood ratio test between the two models is simple to use and has a high level of discrimination power. It is recommended when the complementary cumulative distribution function exhibits linearity on a log-log scale.

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.

METHODS

The performance of Bayesian Synthetic Likelihood to estimate the parameters of a model based on AutoRegressive Conditional Heteroskedastic time series capable of dealing with misreported information and to reconstruct the most likely evolution of the phenomenon is assessed through a comprehensive simulation study and illustrated by reconstructing the weekly Covid-19 incidence in each Spanish Autonomous Community.

RESULTS

Only around 51% of the Covid-19 cases in the period 2020/02/23-2022/02/27 were reported in Spain, showing relevant differences in the severity of underreporting across the regions.

CONCLUSIONS

The proposed methodology provides public health decision-makers with a valuable tool in order to improve the assessment of a disease evolution under different scenarios.

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. This would cause a mixture of different frequencies that would result in the overdispersion observed.

OBJECTIVES

The objective of this study was to evaluate both the possible differences in the radiosensitivities of the different cell subtypes present in the PBMCs and to evaluate the distribution of γ-H2AX foci in each cell subtype.

MATERIALS AND METHODS

Peripheral blood samples from three healthy donors were obtained and total PBMCs, and CD3+, CD4+, CD8+, CD19+, and CD56+ cells were separated. Cells were irradiated with 1 and 2 Gy and incubated at 37 °C for 1, 2, 4, and 24 h. Sham-irradiated cells were also analyzed. γ-H2AX foci were detected after immunofluorescence staining and analyzed automatically using a Metafer Scanning System. For each condition, 250 nuclei were considered.

RESULTS

When the results from each donor were compared, no observable significant differences between donors were observed. When the different cell subtypes were compared, CD8+ cells showed the highest mean of γ-H2AX foci in all post-irradiation time points. The cell type that showed the lowest γ-H2AX foci frequency was CD56+. The frequencies observed in CD4+ and CD19+ cells fluctuated between CD8+ and CD56+ without any clear pattern. For all cell types evaluated, and at all post-irradiation times, overdispersion in γ-H2AX foci distribution was significant. Independent of the cell type evaluated the value of the variance was four times greater than that of the mean.

CONCLUSION

Although different PBMC subsets studied showed different radiation sensitivity, these differences did not explain the overdispersion observed in the γ-H2AX foci distribution after exposure to IR.

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).

MATERIALS AND METHODS

The application has been developed using the R programming language and the shiny package as a framework to create a user-friendly online solution. Since no unique method exists for the different mathematical processes, several meetings and periodic correspondence were held in order to reach a consensus on the solutions to be implemented.

RESULTS

The current version 3.6.1 supports dose-effect fitting for dicentric and translocation assay. For dose estimation Biodose Tools implements those methods indicated in international guidelines and a specific method to assess heterogeneous exposures. The app can include information on the irradiation conditions to generate the calibration curve. Also, in the dose estimate, information about the accident can be included as well as the explanation of the results obtained. Because the app allows generating a report in various formats, it allows traceability of each biological dosimetry study carried out. The app has been used globally in different exercises and training, which has made it possible to find errors and improve the app itself. There are some features that still need consensus, such as curve fitting and dose estimation using micronucleus analysis. It is also planned to include a package dedicated to interlaboratory comparisons and the incorporation of Bayesian methods for dose estimation.

CONCLUSION

Biodose Tools provides an open-source solution for biological dosimetry laboratories. The consensus reached helps to harmonize the way in which uncertainties are calculated. In addition, because each laboratory can download and customize the app's source code, it offers a platform to integrate new features.

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. We derive the asymptotics of both the Poisson and binomial Stein-type GoF-statistic for general count distributions (we also briefly consider the negative-binomial case), such that the asymptotic power is easily computed for arbitrary alternatives. This allows for an efficient implementation of optimal Stein tests, that is, which are most powerful within a given class  F $\mathcal {F}$ of weight functions. The performance and application of the optimal Stein-type GoF-tests is investigated by simulations and several medical data examples.

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. 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.

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).

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 ).

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.

MATERIALS AND METHODS

Calibration data was obtained irradiating peripheral mononucleated cells from one donor with radiation doses ranging from 0 to 3 Gy, and γ -H2AX foci were detected microscopically using a semi-automatic method, at different post-irradiation times from 0.5 to 24 h. For validation, in addition to the above-mentioned donor, blood samples from another donor were also used. Validation was done within the range of doses and post-irradiation times used in the calibration.

RESULTS

The calibration data clearly shows that at each analyzed time, the γ-H2AX foci frequency increases as dose increases, and for each dose this frequency decreases with post-irradiation time. The γ-H2AX foci nucleus distribution was clearly overdispersed, for this reason to obtain bidimensional and tridimensional dose-effect relationships no probability distribution was assumed, and linear and non-linear least squares weighted regression was used. In the two validation exercises for most evaluated samples, the 95% confidence limits of the estimated dose were between ±0.5 Gy of the real dose. No major differences were observed between donors.

CONCLUSION

In case of a suspected overexposure to radiation, the surface model here presented allows a correct dose estimation using γ-H2AX foci as biomarker. The advantage of this surface model is that it can be used at any post-irradiation time, in our model between 0.5 and 24 h.

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 01-31-2020/06-01-2020 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 June 1st, 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.

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.

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.

Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources

Knowing the displacement capacity and mobility patterns of industrially exploited (i.e., fished) marine resources is key to establishing effective conservation management strategies in human-impacted marine ecosystems. Acquiring accurate behavioral information of deep-sea fished ecosystems is necessary to establish the sizes of marine protected areas within the framework of large international societal programs (e.g., European Community H2020, as part of the Blue Growth economic strategy). However, such information is currently scarce, and high-frequency and prolonged data collection is rarely available. Here, we report the implementation of autonomous underwater vehicles and remotely operated vehicles as an aid for acoustic long-baseline localization systems for autonomous tracking of Norway lobster (Nephrops norvegicus), one of the key living resources exploited in European waters. In combination with seafloor moored acoustic receivers, we detected and tracked the movements of 33 tagged lobsters at 400-m depth for more than 3 months. We also identified the best procedures to localize both the acoustic receivers and the tagged lobsters, based on algorithms designed for off-the-shelf acoustic tags identification. Autonomous mobile platforms that deliver data on animal behavior beyond traditional fixed platform capabilities represent an advance for prolonged, in situ monitoring of deep-sea benthic animal behavior at meter spatial scales.

First report of the carnivorous sponge Lycopodina hypogea (Cladorhizidae) associated with marine debris, and its possible implications on deep-sea connectivity

Nowadays, there are an increasing number of reports of deep-sea accumulation of marine debris, often associated with a wide array of pernicious effects on benthic fauna. Nevertheless, there is still a huge knowledge gap regarding the interaction of benthic organisms and marine debris. In this paper, we report for the first time the colonization of plastic debris by the protected sponges Lycopodina hypogea. The sponges were discovered growing on plastic debris tangled with nylon ropes on the Blanes canyon (northwestern Mediterranean Sea). Over 30 individuals of L. hypogea were identified attached on ca. 10 cm² plastic debris, an unusual feature for a species mostly known for low-density populations and a patchy distribution. The implications of this discovery are discussed, and it is suggested that marine debris might provide substrate for benthic species on otherwise unsuitable habitats, with its possible role as stepping-stones for deep-sea benthic connectivity needing further study.

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.

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.

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. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions.

Results

The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages.

Conclusions

The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.

A probabilistic Poisson-based model to detect PRRSV recirculation using sow production records

Porcine reproductive and respiratory syndrome (PRRS) is a viral disease associated with a decrease in the number of born alive piglets (NBA) and an increase in the number of lost piglets (NLP) per farrowing. Under practical conditions, it is critical to assess whether a farm is suffering PRRSV recirculation in the sow herd as soon as possible. The aim of this research work was to develop a new method to detect potential PRRSV recirculation in sow production farms. Sow reproductive performance records from one farm (farm T) were used to set up the method and records from ten additional farms (farms V1 to V10) were used for validation. A conditional Poisson model of NLP on NBA was proposed to fit the data. A three-step procedure was implemented to detect potential PRRSV recirculation: (i) computation of the maximum-likelihood estimates of the expected values of NBA and NLP in a PRRSV non-recirculating scenario; (ii) calculation, for each farrowing, of the p-value associated with the probability of jointly observing deviations towards decreased NBA and increased NLP. The detection of a potential PRRSV recirculation was based on (iii) the combined p-value resulting from weighing the p-values of the last N farrowings by the chi-square-inverse method. In order to gain specificity, a displacement on the expected non-recirculating NBA and NLP values was used for tuning purposes. With this approach, two PRRSV circulating periods were detected in farm T, which were confirmed with standard laboratorial diagnostic techniques. The method was subsequently validated in farms V1 to V10, where ten PRRSV-recirculating time episodes had been diagnosed. The method proposed here was able to detect the ten PRRSV recirculations using a relatively small set of contiguous farrowings, with only two mismatched weeks, one as a false negative, in farm V1, and one as a false positive, in farm V4. It is concluded that a conditional Poisson-based model of NLP on NBA can be a useful tool for routinely detecting PRRSV recirculation in sow herds.

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 ( L C -class). This is a large family that includes mixed and compound Poisson distributions, and their independent sums and finite mixtures as well. The finite-sample performance of the lower bound estimator is assessed via simulation showing a better behavior than some close competitors. Several examples of application are also analyzed and discussed.

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. 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).

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. The model is able to forecast the probability of occurrence of an event for a given intensity threshold; in particular, the probability of occurrence on the next decade of an extreme event of a magnitude comparable or larger than the well-known Carrington event of 1859 is explored, and estimated to be between 0.46% and 1.88% (with a 95% confidence), a much lower value than those reported in the existing literature.

Exploring the randomness of mentally generated head–tail sequences

It is well known that people deviate from randomness as they attempt to mentally generate head–tail sequences as randomly as possible. This deviation from randomness is quantified by an excess of repetitions or alternations between successive responses more than would be expected by chance. We conducted an experiment in which a sample of students was asked to mentally simulate a sequence as if it is produced by a fair coin. We propose several models based on Markov chains for analysing the dynamic of head–tail outcomes in these sequences. First, we explore observed Markov chains and suggest some practical solutions to reduce the number of parameters. However, there is a need for more sophisticated models, and in this case, we propose latent Markov models and mixture of Markov chains to analyse these head–tail sequences. A generalization of the so-called mixture transition distribution (MTD) model is also considered.

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. Different methods are used to analyse the dosimetry data taking into account the distribution of the sample. In order to test the Poisson distribution against the Zero-Inflated Poisson distribution, several asymptotic and exact methods have been proposed which are focused on the dispersion of the data. In this work, we suggest an exact test for the Poisson distribution focused on the zero-inflation of the data developed by Rao and Chakravarti (Some small sample tests of significance for a Poisson distribution. Biometrics 1956; 12 : 264-82.), derived from the problems of occupancy. An approximation based on the standard Normal distribution is proposed in those cases where the computation of the exact test can be tedious. A Monte Carlo Simulation study was performed in order to estimate empirical confidence levels and powers of the exact test and other tests proposed in the literature. Different examples of applications based on in vitro data and also data recorded in several radiation accidents are presented and discussed. A Shiny application which computes the exact test and other interesting goodness-of-fit tests for the Poisson distribution is presented in order to provide them to all interested researchers.

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. A farm was considered positive in a given month if at least one oral fluid tested influenza positive by reverse transcriptase polymerase chain reaction. Influenza seasonality was assessed combining autoregressive integrated moving average (ARIMA) models with trigonometric functions as covariates. Meteorological conditions were gathered from local land-based weather stations, monthly aggregated and correlated with influenza herd-level prevalence. Influenza herd-level prevalence had a median of 28% with a range from 7 to 57% and followed a cyclical pattern with levels increasing during fall, peaking in both early winter (December) and late spring (May), and decreasing in summer. Influenza herd-level prevalence was correlated with mean outdoor air absolute humidity (AH) and temperature. Influenza genetic diversity was substantial over time with influenza isolates belonging to 10 distinct clades from which H1 delta 1 and H1 gamma 1 were the most common. Twenty-one percent of farms had three different clades co-circulating over time, 18% of farms had two clades, and 41% of farms had one clade. In summary, our study showed that influenza had a cyclical pattern explained in part by air AH and temperature changes over time, and highlighted the importance of active surveillance to identify high-risk periods when strategic control measures for influenza could be implemented.

Integer-valued AR processes with Hermite innovations and time-varying parameters: An application to bovine fallen stock surveillance at a local scale

In this article we present a new INteger-valued AutoRegressive (INAR) model with the aim of extracting baseline patterns of cattle fallen stock registered over an 5-year period at a local scale. We introduce HINAR as a generalization of the classical Poisson-based INAR models whose innovations follow a Hermite distribution. In order to assess trends and seasonality in these time series, we fit different models with time-dependent parameters by specifying proper functions. Using real world examples, we illustrate how to estimate parameters by maximum likelihood and validate the fitted models. We also show a detailed method to forecast. Our proposed model supposes a good solution for studying discrete time series when the counts have many zeros, low counts and moderate overdispersion. This model has been applied to the analysis of fallen cattle registered at a local scale as part of the development of a veterinary syndromic surveillance system.

No beneficial effect of bracing after anterior cruciate ligament reconstruction in a cohort of 969 athletes followed in rehabilitation

OBJECTIVE

Compare the clinical outcomes of different knee braces in the early phase of rehabilitation after anterior cruciate ligament reconstruction (ACLR) in athletes.

MATERIALS AND METHODS

We conducted a retrospective database study of athletes during early rehabilitation in a tertiary referral hospital between 1 February 2008 and 30 October 2010 after ACLR using bone patellar tendon bone (BPTB) or hamstring autograft. Differences in mid-patellar knee circumference, pain, and range of motion were assessed at admission. All patients followed the same rehabilitation protocol. Patients who had complications preventing them from following the assigned rehabilitation program were analyzed separately. Patients who completed their rehabilitation program were also assessed for thigh muscle atrophy, extension deficit≥2°, quality of walking, PPLP1 and subjective IKDC scores. The type and frequency of complications and their frequency was documented. The above-mentioned parameters were analyzed in 3 different groups: rigid brace in full extension, articulated brace (0°-90° for first 3 weeks then 0-120°) or no brace.

RESULTS

The analysis included 969 patients. Rehabilitation started at 4.5±2.9 days after surgery and ended at 32.4±3.0 days postoperative. At the beginning, flexion was lower in patients with a rigid brace (P<0.01). There was no difference in the frequency or severity of complications between the three study groups, nor was there a significant difference in the clinical outcomes listed above.

CONCLUSION

Postoperative bracing after ACLR has not beneficial effect on clinical outcomes and the complication rate. Patients who wore the rigid brace had limited flexion early on.

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. The relative accuracy and relevance of techniques for calculating uncertainty and combining assay results to produce single dose and uncertainty estimates is further discussed.

CONCLUSIONS

Finally, it is demonstrated that whatever uncertainty estimation method is employed, ignoring the uncertainty on fast dose assessments can have an important impact on rapid biodosimetric categorization.

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. 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 Wiley & Sons, Ltd.

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. When applied to seven sites monitored by the Atmospheric Radiation Measurement Program the tests lead to the rejection of the universality hypothesis, despite the fact that the exponents are rather close to each other. We discuss the reasons of the rejection.

A new Bayesian model applied to cytogenetic partial body irradiation estimation

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.

A New Model for Biological Dose Assessment in Cases of Heterogeneous Exposures to Ionizing Radiation

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.

Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study

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.

radir package: an R implementation for cytogenetic biodosimetry dose estimation

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.

A MSFD complementary approach for the assessment of pressures, knowledge and data gaps in Southern European Seas: The PERSEUS experience

PERSEUS project aims to identify the most relevant pressures exerted on the ecosystems of the Southern European Seas (SES), highlighting knowledge and data gaps that endanger the achievement of SES Good Environmental Status (GES) as mandated by the Marine Strategy Framework Directive (MSFD). A complementary approach has been adopted, by a meta-analysis of existing literature on pressure/impact/knowledge gaps summarized in tables related to the MSFD descriptors, discriminating open waters from coastal areas. A comparative assessment of the Initial Assessments (IAs) for five SES countries has been also independently performed. The comparison between meta-analysis results and IAs shows similarities for coastal areas only. Major knowledge gaps have been detected for the biodiversity, marine food web, marine litter and underwater noise descriptors. The meta-analysis also allowed the identification of additional research themes targeting research topics that are requested to the achievement of GES.

A new inverse regression model applied to radiation biodosimetry

Biological dosimetry based on chromosome aberration scoring in peripheral blood lymphocytes enables timely assessment of the ionizing radiation dose absorbed by an individual. Here, new Bayesian-type count data inverse regression methods are introduced for situations where responses are Poisson or two-parameter compound Poisson distributed. Our Poisson models are calculated in a closed form, by means of Hermite and negative binomial (NB) distributions. For compound Poisson responses, complete and simplified models are provided. The simplified models are also expressible in a closed form and involve the use of compound Hermite and compound NB distributions. Three examples of applications are given that demonstrate the usefulness of these methodologies in cytogenetic radiation biodosimetry and in radiotherapy. We provide R and SAS codes which reproduce these examples.

A new model of biodosimetry to integrate low and high doses

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.

Review of Bayesian statistical analysis methods for cytogenetic radiation biodosimetry, with a practical example

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 flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments

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 tweeDEseq 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 tweeDEseq 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 tweeDEseq 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 tweeDEseq package forms part of the Bioconductor project and it is available for download at http://www.bioconductor.org.

A statistical model for hospital admissions caused by seasonal diseases

We present a model based on two-order integer-valued autoregressive time series to analyze the number of hospital emergency service arrivals caused by diseases that present seasonal behavior. We also introduce a method to describe this seasonality, on the basis of Poisson innovations with monthly means. We show parameter estimation by maximum likelihood and model validation and show several methods for forecasting, on the basis of long-time means and short-time and long-time prediction regions. We analyze an application to model the number of hospital admissions per week caused by influenza.