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Background: COVID-19 dynamics are driven by a complex interplay of factors including population behaviour, new variants, vaccination and immunity from prior infections. We quantify drivers of SARS-CoV-2 transmission in the Dominican Republic, an upper-middle income country of 10.8 million people. We then assess the impact of the vaccination campaign implemented in February 2021, primarily using CoronaVac, in saving lives and averting hospitalisations. Methods: We fit an age-structured, multi-variant transmission dynamic model to reported deaths, hospital bed occupancy, and seroprevalence data until December 2021, and simulate epidemic trajectories under different counterfactual scenarios. Findings: We estimate that vaccination averted 7210 hospital admissions (95% credible interval, CrI: 6830-7600), 2180 intensive care unit admissions (95% CrI: 2080-2280) and 766 deaths (95% CrI: 694-859) in the first 6 months of the campaign. If no vaccination had occurred, we estimate that an additional decrease of 10-20% in population mobility would have been required to maintain equivalent death and hospitalisation outcomes. We also found that early vaccination with CoronaVac was preferable to delayed vaccination using a product with higher efficacy. Interpretation: SARS-CoV-2 transmission dynamics in the Dominican Republic were driven by a substantial accumulation of immunity during the first two years of the pandemic but, despite this, vaccination was essential in enabling a return to pre-pandemic mobility levels without considerable additional morbidity and mortality. Funding: Medical Research Council, Wellcome Trust, Royal Society, US CDC and Australian National Health and Medical Research Council.

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Many techniques have been proposed to model space-varying observation processes with a nonstationary spatial covariance structure and/or anisotropy, usually on a geostatistical framework. Nevertheless, there is an increasing interest in point process applications, and methodologies that take nonstationarity into account are welcomed. In this sense, this work proposes an extension of a class of spatial Cox process using spatial deformation. The proposed method enables the deformation behavior to be data-driven, through a multivariate latent Gaussian process. Inference leads to intractable posterior distributions that are approximated via MCMC. The convergence of algorithms based on the Metropolis-Hastings steps proved to be slow, and the computational efficiency of the Bayesian updating scheme was improved by adopting Hamiltonian Monte Carlo (HMC) methods. Our proposal was also compared against an alternative anisotropic formulation. Studies based on synthetic data provided empirical evidence of the benefit brought by the adoption of nonstationarity through our anisotropic structure. A real data application was conducted on the spatial spread of the Spodoptera frugiperda pest in a corn-producing agricultural area in southern Brazil. Once again, the proposed method demonstrated its benefit over alternatives.

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Function and structure are strongly coupled in obligated oligomers such as Triosephosphate isomerase (TIM). In animals and fungi, TIM monomers are inactive and unstable. Previously, we used ancestral sequence reconstruction to study TIM evolution and found that before these lineages diverged, the last opisthokonta common ancestor of TIM (LOCATIM) was an obligated oligomer that resembles those of extant TIMs. Notably, calorimetric evidence indicated that ancestral TIM monomers are more structured than extant ones. To further increase confidence about the function, structure, and stability of the LOCATIM, in this work, we applied two different inference methodologies and the worst plausible case scenario for both of them, to infer four sequences of this ancestor and test the robustness of their physicochemical properties. The extensive biophysical characterization of the four reconstructed sequences of LOCATIM showed very similar hydrodynamic and spectroscopic properties, as well as ligand-binding energetics and catalytic parameters. Their 3D structures were also conserved. Although differences were observed in melting temperature, all LOCATIMs showed reversible urea-induced unfolding transitions, and for those that reached equilibrium, high conformational stability was estimated (ΔGTot = 40.6-46.2 kcal/mol). The stability of the inactive monomeric intermediates was also high (ΔGunf = 12.6-18.4 kcal/mol), resembling some protozoan TIMs rather than the unstable monomer observed in extant opisthokonts. A comparative analysis of the 3D structure of ancestral and extant TIMs shows a correlation between the higher stability of the ancestral monomers with the presence of several hydrogen bonds located in the "bottom" part of the barrel.

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Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model's accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model's predictive capability. This measure, which is straightforward and easy to understand, includes a decision criterion for model rejection. The development of this proposal adopts a Bayesian perspective of inference, elucidating the underlying concepts and outlining the necessary procedures for application. To illustrate its utility, the proposed methodology was applied to real-world data, facilitating an assessment of its practicality in real-world scenarios.

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The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets.

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Considering the context of functional data analysis, we developed and applied a new Bayesian approach via the Gibbs sampler to select basis functions for a finite representation of functional data. The proposed methodology uses Bernoulli latent variables to assign zero to some of the basis function coefficients with a positive probability. This procedure allows for an adaptive basis selection since it can determine the number of bases and which ones should be selected to represent functional data. Moreover, the proposed procedure measures the uncertainty of the selection process and can be applied to multiple curves simultaneously. The methodology developed can deal with observed curves that may differ due to experimental error and random individual differences between subjects, which one can observe in a real dataset application involving daily numbers of COVID-19 cases in Brazil. Simulation studies show the main properties of the proposed method, such as its accuracy in estimating the coefficients and the strength of the procedure to find the true set of basis functions. Despite having been developed in the context of functional data analysis, we also compared the proposed model via simulation with the well-established LASSO and Bayesian LASSO, which are methods developed for non-functional data.

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The list of occurrences linked to significant climate change has grown in recent decades. These changes can be influenced by a set of covariates, such as temperature, location and period of the year. Analyzing the relation among elements and factors that influence the behavior of such events is extremely important for decision-making in order to minimize damages and losses. Exceedance analysis uses the tail of the distribution based on Extreme Value Theory (EVT). Extensions for these models have been proposed in literature, such as regression models for the tail parameters and a parametric or semi-parametric distribution for the part that comes before the tail (well known as bulk distribution). This work presents a new extension to exceedance model, in which the parameters for the bulk distribution capture the effect of covariates such as location and seasonality. We considered a Bayesian approach in the inference procedure. The estimation was done using MCMC -- Markov Chain Monte Carlo methods. Application results for modeling maximum and minimum temperature data showed an efficient estimation of extreme quantiles and a predictive advantage compared to models previously used in literature.

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Mosquito-borne diseases such as dengue and chikungunya have been co-circulating in the Americas, causing great damage to the population. In 2021, for instance, almost 1.5 million cases were reported on the continent, being Brazil the responsible for most of them. Even though they are transmitted by the same mosquito, it remains unclear whether there exists a relationship between both diseases. In this paper, we model the geographic distributions of dengue and chikungunya over the years 2016 to 2021 in the Brazilian state of Ceará. We use a Bayesian hierarchical spatial model for the joint analysis of two arboviruses that includes spatial covariates as well as specific and shared spatial effects that take into account the potential autocorrelation between the two diseases. Our findings allow us to identify areas with high risk of one or both diseases. Only 7% of the areas present high relative risk for both diseases, which suggests a competition between viruses. This study advances the understanding of the geographic patterns and the identification of risk factors of dengue and chikungunya being able to help health decision-making.

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Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed-effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed-effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed-effects state-space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew-t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG-315) clinical trial data set.

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Pheromones mediate species-level communication in the search for mates, nesting, and feeding sites. Although the role of pheromones has long been discussed by various authors, their existence was not proven until the mid-twentieth century when the first sex pheromone was identified. From this finding, much has been speculated about whether this communication mechanism has acted as a regulatory agent in the process of speciation, competition, and sexual selection since it acts as an intraspecific barrier. Chrysomelidae is one of the major Phytophaga lineages, with approximately 40,000 species. Due to this immense diversity the internal relationships remain unstable when analyzed only with morphological data, consequently recent efforts have been directed to molecular analyses to establish clarity for the relationships and found their respective monophyly. Therefore, our goals are twofold 1) to synthesize the current literature on Chrysomelidae sex pheromones and 2) to test whether Chrysomelidae sex pheromones and their chemical structures could be used in phylogenetic analysis for the group. The results show that, although this is the first analysis in Chrysomelidae to use pheromones as a phylogenetic character, much can be observed in agreement with previous analyses, thus confirming that pheromones, when known in their entirety within lineages, can be used as characters in phylogenetic analyses, bringing elucidation to the relationships and evolution of organisms.

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The competitive exclusion principle establishes that the coexistence of closely related species requires a certain degree of resource partitioning. However, populations have individuals with different morphological or behavioral traits (e.g., maturity stages, sexes, temporal or spatial segregation). This interaction often results in a multi-level differentiation in food preferences and habits. We explored such resource partitioning between and within three batoid species: Hypanus dipterurus, Narcine entemedor, and Rhinoptera steindachneri in the southern Gulf of California, Mexico, using a combination of stomach content (excluding R. steindachneri) and stable isotope analyses. We found a clear differentiation between H. dipterurus and N. entemedor, where the latter exhibited more benthic habitats, supported by a greater association to infaunal prey and higher δ13C values. Though the degree and patterns of intra-specific segregation varied among species, there was a notable differentiation in both sex and stage of maturity, corresponding to changes in specialization (i.e., isotopic niche breadth) or trophic spectrum (varying prey importance and isotopic values per group). This work is a promising step towards understanding the dietary niche dynamics of these species in a potentially important feeding area within the southern Gulf of California, as well as the biological and ecological mechanisms that facilitate their coexistence.

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Ignoring the presence of dependent censoring in data analysis can lead to biased estimates, for example, not considering the effect of abandonment of the tuberculosis treatment may influence inferences about the cure probability. In order to assess the relationship between cure and abandonment outcomes, we propose a copula Bayesian approach. Therefore, the main objective of this work is to introduce a Bayesian survival regression model, capable of taking into account the dependent censoring in the adjustment. So, this proposed approach is based on Clayton's copula, to provide the relation between survival and dependent censoring times. In addition, the Weibull and the piecewise exponential marginal distributions are considered in order to fit the times. A simulation study is carried out to perform comparisons between different scenarios of dependence, different specifications of prior distributions, and comparisons with the maximum likelihood inference. Finally, we apply the proposed approach to a tuberculosis treatment adherence dataset of an HIV cohort from Alvorada-RS, Brazil. Results show that cure and abandonment outcomes are negatively correlated, that is, as long as the chance of abandoning the treatment increases, the chance of tuberculosis cure decreases.

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Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.

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To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

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The first 18 months of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Colombia were characterized by three epidemic waves. During the third wave, from March through August 2021, intervariant competition resulted in Mu replacing Alpha and Gamma. We employed Bayesian phylodynamic inference and epidemiological modeling to characterize the variants in the country during this period of competition. Phylogeographic analysis indicated that Mu did not emerge in Colombia but acquired increased fitness there through local transmission and diversification, contributing to its export to North America and Europe. Despite not having the highest transmissibility, Mu's genetic composition and ability to evade preexisting immunity facilitated its domination of the Colombian epidemic landscape. Our results support previous modeling studies demonstrating that both intrinsic factors (transmissibility and genetic diversity) and extrinsic factors (time of introduction and acquired immunity) influence the outcome of intervariant competition. This analysis will help set practical expectations about the inevitable emergences of new variants and their trajectories. IMPORTANCE Before the appearance of the Omicron variant in late 2021, numerous SARS-CoV-2 variants emerged, were established, and declined, often with different outcomes in different geographic areas. In this study, we considered the trajectory of the Mu variant, which only successfully dominated the epidemic landscape of a single country: Colombia. We demonstrate that Mu competed successfully there due to its early and opportune introduction time in late 2020, combined with its ability to evade immunity granted by prior infection or the first generation of vaccines. Mu likely did not effectively spread outside of Colombia because other immune-evading variants, such as Delta, had arrived in those locales and established themselves first. On the other hand, Mu's early spread within Colombia may have prevented the successful establishment of Delta there. Our analysis highlights the geographic heterogeneity of early SARS-CoV-2 variant spread and helps to reframe the expectations for the competition behaviors of future variants.

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The circulation of the four-dengue virus (DENV) serotypes has significantly increased in recent years, accompanied by an increase in viral genetic diversity. In order to conduct disease surveillance and understand DENV evolution and its effects on virus transmission and disease, efficient and accurate methods for phylogenetic classification are required. Phylogenetic analysis of different viral genes sequences is the most used method, the envelope gene (E) being the most frequently selected target. We explored the genetic variability of the four DENV serotypes throughout their complete coding sequence (CDS) of sequences available in GenBank and used genomic regions of different variability rate to recapitulate the phylogeny obtained with the DENV CDS. Our results indicate that the use of high or low variable regions accurately recapitulate the phylogeny obtained with CDS of sequences from different DENV genotypes. However, when analyzing the phylogeny of a single genotype, highly variable regions performed better in recapitulating the distance branch length, topology, and support of the CDS phylogeny. The use of three concatenated highly variable regions was not statistically different in distance branch length and support to that obtained in CDS phylogeny.•This study demonstrated the ability of highly variable regions of the DENV genome to recapitulate the phylogeny obtained with the full coding sequence (CDS).•The use of genomic regions of high or low variability did not affect the performance in recapitulating the phylogeny obtained with CDS from different genotypes. However, when phylogeny was analyzed for sequences from a single genotype, highly variable regions performed better in recapitulating the distance branch length, topology, and support of the CDS phylogeny.•The use of concatenated highly variable genome regions represent a useful option for recapitulating genome-wide phylogenies in analyses of sequences belonging to the same DENV genotype.

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DNA-barcoding is a species identification tool that uses a short section of the genome that provides a genetic signature of the species. The main advantage of this novel technique is that it requires a small sample of tissue from the tested organism. In most animal groups, this technique is very effective. However, in plants, the recommended standard markers, such as rbcLa, may not always work, and their efficacy remains to be tested in many plant groups, particularly from the Neotropical region. We examined the discriminating power of rbcLa in 55 tropical cloud forest vascular plant species from 38 families (Oaxaca, Mexico). We followed the CBOL criteria using BLASTn, genetic distance, and monophyly tree-based analyses (neighbor-joining, NJ, maximum likelihood, ML, and Bayesian inference, BI). rbcLa universal primers amplified 69.0% of the samples and yielded 91.3% bi-directional sequences. Sixty-three new rbcLa sequences were established. BLAST discriminates 80.8% of the genus but only 15.4% of the species. There was nil minimum interspecific genetic distances in Quercus, Oreopanax, and Daphnopsis. Contrastingly, Ericaceae (5.6%), Euphorbiaceae (4.6%), and Asteraceae (3.3%) species displayed the highest within-family genetic distances. According to the most recent angiosperm classification, NJ and ML trees successfully resolved (100%) monophyletic species. ML trees showed the highest mean branch support value (87.3%). Only NJ and ML trees could successfully discriminate Quercus species belonging to different subsections: Quercus martinezii (white oaks) from Q. callophylla and Q. laurina (red oaks). The ML topology could distinguish species in the Solanaceae clade with similar BLAST matches. Also, the BI topology showed a polytomy in this clade, and the NJ tree displayed low-support values. We do not recommend genetic-distance approaches for species discrimination. Severe shortages of rbcLa sequences in public databases of neotropical species hindered effective BLAST comparisons. Instead, ML tree-based analysis displays the highest species discrimination among the tree-based analyses. With the ML topology in selected genera, rbcLa helped distinguish infra-generic taxonomic categories, such as subsections, grouping affine species within the same genus, and discriminating species. Since the ML phylogenetic tree could discriminate 48 species out of our 55 studied species, we recommend this approach to resolve tropical montane cloud forest species using rbcLa, as an initial step and improve DNA amplification methods.

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This paper proposes a differing methodology from the Brazilian Electricity Regulatory Agency on the efficiency estimation for the Brazilian electricity distribution sector. Our proposal combines robust state-space models and stochastic frontier analysis to measure the operational cost efficiency in a panel data set from 60 Brazilian electricity distribution utilities. The modeling joins the main literature in energy economics with advanced econometric and statistic techniques in order to estimate the efficiencies. Moreover, the suggested model is able to deal with changes in the inefficiencies across time whilst the Bayesian paradigm - through Markov chain Monte Carlo techniques - facilitates the inference on all unknowns. The method enables a significant degree of flexibility in the resultant efficiencies and a complete photography about the distribution sector.

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Crime is a negative phenomenon that affects the daily life of the population and its development. When modeling crime data, assumptions on either the spatial or the temporal relationship between observations are necessary if any statistical analysis is to be performed. In this paper, we structure space-time dependency for count data by considering a stochastic difference equation for the intensity of the space-time process rather than placing structure on a latent space-time process, as Cox processes would do. We introduce a class of spatially correlated self-exciting spatio-temporal models for count data that capture both dependence due to self-excitation, as well as dependence in an underlying spatial process. We follow the principles in Clark and Dixon (2021) but considering a generalized additive structure on spatio-temporal varying covariates. A Bayesian framework is proposed for inference of model parameters. We analyze three distinct crime datasets in the city of Riobamba (Ecuador). Our model fits the data well and provides better predictions than other alternatives.

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The increasing number of COVID-19 infections brought by the current pandemic has encouraged the scientific community to analyze the seroprevalence in populations to support health policies. In this context, accurate estimations of SARS-CoV-2 antibodies based on antibody tests metrics (e.g., specificity and sensitivity) and the study of population characteristics are essential. Here, we propose a Bayesian analysis using IgA and IgG antibody levels through multiple scenarios regarding data availability from different information sources to estimate the seroprevalence of health professionals in a Northeastern Brazilian city: no data available, data only related to the test performance, data from other regions. The study population comprises 432 subjects with more than 620 collections analyzed via IgA/IgG ELISA tests. We conducted the study in pre- and post-vaccination campaigns started in Brazil. We discuss the importance of aggregating available data from various sources to create informative prior knowledge. Considering prior information from the USA and Europe, the pre-vaccine seroprevalence means are 8.04% and 10.09% for IgG and 7.40% and 9.11% for IgA. For the post-vaccination campaign and considering local informative prior, the median is 84.83% for IgG, which confirms a sharp increase in the seroprevalence after vaccination. Additionally, stratification considering differences in sex, age (younger than 30 years, between 30 and 49 years, and older than 49 years), and presence of comorbidities are provided for all scenarios.

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