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Abstract Organisms‐related data often appear as counts. The Poisson distribution is the most popular choice for modelling count data, but this distribution assumes equidispersion, which is usually not satisfied in real‐world data. Deviations from the Poisson assumption lead to discrete‐valued distributions that can fit over‐ and/or underdispersion. Although models for count data with over‐dispersion have been widely considered in the literature, models for underdispersion—the opposite phenomenon—have received less attention because underdispersion is relatively common only in certain research fields, including ecology. The Good distribution is a flexible option for modelling count data with over‐dispersion or underdispersion, although no R packages are available so far offering functionalities such as calculating quantiles, probabilities, etc., of a Good distribution or providing a method for modelling a Good‐distributed output based on a number of potential predictors. This paper presents the R package good, which computes the standard probabilistic functions, generates random samples from a population following a Good distribution and estimates the Good regression.

Abstract 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.

Abstract 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 $$\gamma $$ γ -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 $$\gamma $$ γ -H2AX biomarker dose estimation process.

Abstract 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.

Abstract 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 ).

Abstract 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.

Abstract 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.

Abstract 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.

Cet article questionne la durabilité des dispositifs de formation professionnelle par alternance que sont les Centres Éducatifs Familiaux de Formation par Alternance (CEFFA) en Afrique en général et, de manière particulière au Cameroun, où ils sont confrontés aux difficultés réelles pour assurer leur autonomisation. Les CEFFA, dont la formule d’éducation va se répandre à travers le monde tel un essaim d’abeilles grâce à la coopération entre la France et des pays partenaires, peinent encore à s’enraciner ou à s’ancrer dans le territoire. L’analyse est davantage focalisée sur la viabilité financière, en tant qu’aptitude des associations promotrices de CEFFA à assurer leur autonomie de fonctionnement en disposant des ressources nécessaires pour couvrir toutes les charges. L’article est à la fois un travail de recherche historique et d’analyse des principales difficultés d’ancrage territorial des CEFFA dans le contexte africain et des défis liés à une implantation durable des CEFFA au Cameroun.

L'année 2019 constitue une étape importante dans l'éducation brésilienne, en particulier pour les expériences formatrices de “Educação do Campo”, que nous traduisons de façon plus ou moins adéquate par éducation rurale, ainsi que pour la célébration des 50 ans de la Pédagogie de l’Alternance. Née en France et amenée dans l’État de Espírito Santo par un prêtre jésuite, elle a été construite par des paysans à travers les différentes réalités du Brésil comme une possibilité contre-hégémonique d'éducation qui combine la formation scolaire académique avec le monde du travail, ainsi qu’avec l'appartenance à la terre et à ses identités culturelles. Dans cette perspective, l'Association internationale des mouvements familiaux de formation rurale (AIMFR), le groupe de recherche CNPq/UFES Culturas, Parcerias e Educação do Campo ainsi que le Grupo internacional de investigación y de reflexión sobre alternancia (GIIRA), basé à l’Université de Sherbrooke, Canada, assument, avec la Revista Brasileira de Educação do Campo, la réalisation du présent dossier. C'est l'occasion de rassembler, dans le cadre de cet important périodique, une pluralité de discussions et de résultats de recherches qui émergent de la pratique des moniteurs-éducateurs-enseignants et de chercheurs qui travaillent dans et sur la formation par alternance au Brésil, au Canada, en Espagne, au Cameroun, en Italie et en France. Cette opportunité est envisagée comme un cadre de réflexion, de problématisation et de partage de bonnes expériences, compte tenu des innombrables défis du contexte actuel. Dans cette perspective, il ne s'agit pas d'un dossier contemplatif, mais d'un dossier permettant de réfléchir et de problématiser, dans le sens d'une pratique vivante et dynamique, produite par des sujets historiquement situés, qui se placent dans le champ de la résistance et se constituent ainsi en fonction des contextes et des possibilités. Ainsi, les recherches partagées ici émergent des expériences les plus diverses, de l'éducation de base à l'enseignement supérieur, ce qui nous révèle le potentiel transgresseur de l’Alternance. Reconnaître cette diversité de pratiques, c'est aussi affirmer que la Pédagogie par Alternance, née en terre brésilienne il y a 50 ans, constitue aujourd'hui un patrimoine de l'éducation brésilienne et, à ce titre, une figure du droit et des politiques publiques. Cependant, le cheminement historique et social pose aussi des défis qui surgissent dans le mouvement même de transformation de la société et des modes de vie, de travail et de production. Faire face à ces défis et rester dans la lutte sont ici élevés comme des drapeaux clés dans la production de nouveaux et d'autres sens de la Formation par Alternance, sans toutefois s'éloigner de ses piliers, de ses médiations et/ou de ses instruments pédagogiques et, surtout, avec le renforcement de ses biais politiques, émancipateur et transformateur des réalités et de leurs sujets. C’est pourquoi nous exprimons ici nos sincères remerciements à la Revista Brasileira de Educação do Campo pour nous avoir accordé cet espace, qui se traduit à son tour par une action de diffusion des connaissances et des actions de la Pédagogie par Alternance. En vue d’assurer une certaine organisation des textes en fonction des différents sujets traités, nous avons regroupé les vingt-six articles suivant trois apports: d'abord les articles qui traitent de l'histoire de la pédagogie de l'alternance et de son entrelacement avec l'éducation rurale ; les principes épistémologiques et l'analyse de leurs médiations ou instruments pédagogiques. Suivent les textes qui traitent spécifiquement de la pédagogie de l'alternance dans la formation des éducateurs-moniteurs-enseignants sur le terrain et enfin, en plus grand nombre, nous présentons les recherches autour des pratiques dans les différentes expériences d'éducation scolaire de base et au-delà. Par conséquent, ce que la recherche nationale et internationale sur la Pédagogie par Alternance dit en ces 50 ans d'histoire est une invitation à la connaissance produite par des sujets qui agissent, réfléchissent et problématisent la praxis par le biais de la recherche scientifique.

La communication voit l’anniversaire de ces 50 ans de l’alternance au Brésil, comme un moyen d’opérer une double boucle réflexive sur deux histoires de vie socio-professionnelle avec l’alternance. Elle commence par un survol historique principalement francophone, d’une voie de recherche-formation de l’alternance comme synchroniseur éco-social de temporalités multiples en deux temps (expérientiel/formel) et trois mouvements (auto, socio, écoformation). La deuxième partie expose la philosophie éducative d’un master international Formation et développement durable (2002-2004), construit avec et pour les professionnels de l’alternance au Brésil. Elle présente les 17 mémoires produits selon une structure quadripolaire de l’alternance, croisant deux finalités - développement de la personne et du milieu – avec deux moyens principaux – une pédagogie spécifique et une organisation partenariale. La conclusion évoque l’horizon d’une compétence rythmique existentielle à apprendre par une attention plus grande aux rythmes écologiques qu’aux cours de la bourse, des maîtres et contremaîtres.

Le milieu rural présente quelques caractéristiques induisant une pédagogie de formation adaptée. Parmi ces caractéristiques, on peut relever : 1) la vie de travail, la vie de famille, la vie sociale, la vie de loisirs, orientées et rythmées par les exigences de la profession ; 2) le poids faible des discussions ou des discours : seuls les faits et les actes ont de la valeur ; 3) la difficulté relative à s’exprimer et à faire connaître sa pensée ; 4) un éventuel complexe d’infériorité face au citadin ; 5) des préjugés ténus qui peuvent faire douter d’avoir le droit de se cultiver et de s’épanouir ; 6) l’idée prégnante qu’il ne faut surtout rien changer aux habitudes ; 7) une certaine méfiance face à la nouveauté et à l’inconnu ; 8) la mentalité qui oblige à faire passer l’innovation par des personnes influentes ou par des leaders ; 9) une indépendance de caractère et une autonomie d’entreprise ; 10) le poids de la transmission ; 11) et de la tradition orale par rapport à l’écrit. Certains pays ont innové pour adapter la formation à cette réalité, avec des succès incontestables. D’autres pays s’y essaient avec des succès non négligeables, car « même si la culture n’est pas la même, tous les paysans réagissent de la même façon ! » (Blois, entretiens).

The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and survival models are considered for analyzing the individual probabilities of stroke and the times to the occurrence of an ischemic stroke event. Demographic and socioeconomic variables as well as drug prescription information are available at an individual level. Spatial variation is considered by means of region-level random effects.

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.

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.

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. An exhaustive descriptive analysis using grouping time series structures and models of interrupted times series was conducted for indicator subsets. Results: A total of 13,080 raptors were monitored. The most representative species were red-tailed hawks, great horned owls, Cooper's hawks, American kestrels and bald eagles. Results indicated that temporal patterns of accessions at the TRC changed distinctively after the incursion of WNV in 2002. The frequency of hawks showing WNV-like signs increased almost 3 times during July and August, suggesting that monitoring of hawks admitted to TRC with WNV-like signs could serve as an indicator of WNV circulation. These findings were also supported by the results of laboratory diagnosis. Conclusions: This study demonstrates that monitoring of data routinely collected by wildlife rehabilitation centers has the potential to signal the spread of pathogens that may affect wild, domestic animals and humans, thus supporting the early detection of disease incursions in a region and monitoring of disease trends. Ultimately, data collected in rehabilitation centers may provide insights to efficiently allocate financial and human resources on disease prevention and surveillance. [ABSTRACT FROM AUTHOR]

In this paper, the rth-order univariate Hermite distributions are proposed to model the number of dicentrics in biological dosimetry. These families of distributions are introduced from compound Poisson process modelling. Regression models appropriate for analysing the number of dicentrics as a function of doses of radiation are presented, and an example of application is also given. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]