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We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens' preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil's 2022 presidential election. We use a train-test cross-validation set-up to estimate the accuracy with which the LLMs predict both: a subject's individual political choices and the aggregate preferences of the full sample of participants. At the individual level, we find that LLMs predict out of sample preferences more accurately than a 'bundle rule', which would assume that citizens always vote for the proposals of the candidate aligned with their self-reported political orientation. At the population level, we show that a probabilistic sample augmented by an LLM provides a more accurate estimate of the aggregate preferences of a population than the non-augmented probabilistic sample alone. Together, these results indicate that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

The present study aimed to identify parameters that best discriminate between high-level and scholar-level players for the Brazilian 13-14-year-old girl's handball and propose a mathematical model to identify sports talent for handball. The sample was made up of all available handball players comprising these two groups: 100 girls who participated in the high-level handball championship in Brazil and 53 girls (age 13-14 years) as finalists of the school-level games in one region of Brazil. We assess the anthropometric profile, maturity offset, physical fitness, and technical skills for handball. To propose the equation, the Discriminant Function Analysis method was used. The discriminant function was significant ( p  ≤ .05), had a good canonical correlation (0.590), and still had an average Wilk Lambda (0.652). The variables considered in the talent identification model included: 1. flexibility, 2. abdominal strength, 3. lower limbs muscle power, 4. agility, 5. defensive movement and 6. slalom with ball. The values from the equation for identifying school-age athletes with high motor skills and performance for handball can be classified by a cutoff point (Y = 0.192). The results showed that the mathematical-model obtained was able to select school-age athletes with high motor skills for handball, and with the profile for participation in high-level championships.

The global demand for agricultural commodities has driven extensive land conversion to agriculture in Brazil, especially in the MATOPIBA region. This area encompasses the Rio Grande Basin, a major tributary of the São Francisco Basin that is known for expanding intensive irrigated agriculture and hydropower generation. However, recent data reveal declining precipitation and aquifer recharge, potentially exacerbating ongoing water and land conflicts. This study investigates the long-term sustainability of agricultural expansion amid the worsening water scarcity using a system dynamics model. Findings suggest that rising costs and decreasing profits due to irrigation water shortages may hinder the expansion of irrigated land. By 2040, the irrigation demand may remain partly unmet, while downstream flow and baseflow could decrease. Additionally, agricultural expansion will significantly raise energy demand, posing a developmental challenge. We suggest that ensuring the sustainability of the Rio Grande Basin depends on improved water management and exploring alternative energy sources to address existing constraints., Competing Interests: Declarations Conflict of interest All authors declare that they have no conflicts of interest., (© 2024. The Author(s) under exclusive licence to Royal Swedish Academy of Sciences.)

The forests of the Amazon basin are threatened by climate and land use changes. Due to the transition towards a drier climate, moisture-dependent organisms such as canopy epiphytes are particularly affected. Even if the topography in the Amazon lowland is moderate, mesoscale nocturnal katabatic flows result from cold air production related to radiative cooling. From a certain level of mass the cold air starts to flow downslope towards the valley centers leading to temperature inversions. The resulting cooling in the valleys drives localized fog formation in the valleys at night. This correlates with high epiphyte abundance and diversity in the valleys, which is much less pronounced upslope. The underlying temperature dynamics are, however, not sufficiently included in coarse-resolution reanalysis models such as ERA5-Land. Since high resolution climate data are needed e.g. for proper niche modeling of locally distributed species such as canopy epiphytes, downscaling models such as microclima have been developed and include micro- and mesoscale effects. However, it is unclear how well the elevation-related diurnal course of air temperature can be simulated. Here, we test functions for downscaling coarse-resolution temperature data to high spatial resolution data implemented in the R-package microclima for the South American tropical lowland forests. To do so we compared microclima-downscaled ERA5-Land air temperature data with meteorological station data. We found that the microclima functions only properly detect 73 temperature inversions out of 412 nocturnal cold air drainage (CAD) events during the dry season study period and only 18 out of 400 during the wet season with default settings. By modifying default values such as the emissivity threshold and time frames of possible CAD condition detection, we found 345 of 412 CAD events during the dry season and 177 out of 400 during the wet season. Despite problems with the distinction between CAD and non-CAD events the microclima algorithms show difficulties in correctly modeling the diurnal course of the temperature data and the amplitudes of elevational temperature gradients. For future studies focusing on temperature downscaling approaches, the modules implemented in the microclima package have to be adjusted for their usage in tropical lowland forest studies and beyond., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pohl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The inequalities of Brazilian society are amplified in favelas, affecting access to basic sanitation, health, education services, and food. More research is needed to better understand the favela food environment and propose appropriate public food and nutrition policies to increase the availability of and access to healthy food. In this context, this study aimed to develop a conceptual model of the relationship between access to food and the favela food environment and its determinants. In developing the conceptual model, this study undertook a bibliographical survey of the food environment, and a preliminary version was submitted to an expert panel. The model represents a set of dimensions (individual, micro-environment, macro-environment, and decision-making) and elements that interact in a complex manner and help understand access to food in areas subject to multiple social vulnerabilities. This model can guide future research and aid policymakers in designing effective strategies to improve the food security and health of populations in areas of high socio-spatial vulnerability.

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Places of worship serve as a venue for both mass and routine gathering around the world, and therefore are associated with risk of large-scale SARS-CoV-2 transmission. However, such routine gatherings also offer an opportunity to distribute self-tests to members of the community to potentially help mitigate transmission and reduce broader community spread of SARS-CoV-2. Over the past four years, self-testing strategies have been an impactful tool for countries' response to the COVID-19 pandemic, especially early on to mitigate the spread when vaccination and treatment options were limited. We used an agent-based mathematical model to estimate the impact of various strategies of symptomatic and asymptomatic self-testing for a fixed percentage of weekly routine gatherings at places of worship on community transmission of SARS-CoV-2 in Brazil, Georgia, and Zambia. Testing strategies assessed included weekly and bi-weekly self-testing across varying levels of vaccine effectiveness, vaccine coverage, and reproductive numbers to simulate developing stages of the COVID-19 pandemic. Self-testing symptomatic people attending routine gatherings can cost-effectively reduce the spread of SARS-CoV-2 within places of worship and the community, resulting in incremental cost-effectiveness ratios of $69-$303 USD. This trend is especially true in contexts where population level attendance at such gatherings is high, demonstrating that a distribution approach is more impactful when a greater proportion of the population is reached. Asymptomatic self-testing of attendees at 100% of places of worship in a country results in the greatest percent of infections averted and is consistently cost-effective but remains costly. Budgetary needs for asymptomatic testing are expensive and likely unaffordable for lower-middle income countries (520-1550x greater than that of symptomatic testing alone), promoting that strategies to strengthen symptomatic testing should remain a higher priority., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The views expressed in this manuscript are those of the authors and do not necessarily represent the official position of the WHO., (Copyright: © 2024 Hansen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Wildlife tourism plays a crucial role in biodiversity conservation. However, long-term sustainability is difficult to achieve. In this paper, we use property theory to produce a mathematical model that aims to better support stakeholders from the wildlife tourism industry to better guarantee a balance between sightings probability, tourists' overall experience and operators' sharing behaviour. We illustrate our model with the case study of Porto Jofre in the Pantanal wetland, Brazil. We show that while dealing with low sighting probability, tourist operators must share information about species' locations, leading to a system of open access regarding mobility and information. However, when sightings become common, sharing must be restricted to a bounded group avoiding overcrowding, a system of limited open access. Finally, when the sighting probability is high, no sharing is needed to achieve maximum overall experience. Our case study in Porto Jofre, Pantanal, Brazil, clearly shows these shifts in terms of governance strategies. We show that by looking at sighting probability it is possible to predict the best optimal social strategy that will guarantee long-term sustainability of the wildlife tourism initiatives. We also show the need for external support on adaptation in cases where current strategies do not match the predicted ones., (© 2024. The Author(s).)

In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rafael Pedro Longhi reports financial support was provided by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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Brazil has historically invested few resources in its transport infrastructure, leaving gaps and reducing its efficiency. The country presents a high dependence on road transport, which results in increased operational costs and higher greenhouse gas (GHG) emissions. Consequently, the performance of cargo transportation in Brazil has been deteriorating, accompanied by a rise in the consumption of fossil fuels and noteworthy levels of GHG emissions. This article assesses the carbon intensity of soybean transport operations within Brazil. Utilizing a network equilibrium model, this study estimated the soybean transportation flows that minimize the total cost of transporting this product across the origins and destinations within the grain handling system. The modeling also calculated the GHG emissions in transportation. The results show that the transportation of soybeans produced 2.74 million tonnes of CO2 equivalent annually, with road transport accounting for 81.7% of these emissions. The state of Mato Grosso, responsible for 44.08 kg CO2 equivalent per tonne of soybeans transported, contributed almost 49% of the total emissions due to the extensive distances involved. In contrast, states like Paraná and Rio Grande do Sul, located closer to southern ports, exhibited the lowest emissions, with rates of 11.55 kg CO2 eq/t and 12.52 kg CO2 eq/t, respectively. The analysis highlights the significant potential for reducing GHG emissions by increasing the use of rail and barge transport, particularly in high-emission regions such as Mato Grosso., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The inverse problem method can be applied to determine the properties of hydrological phenomena and estimate the parameters, which cannot be measured directly. This type of inverse focus can facilitate the implementation of the kinematic wave model (direct model-DM), to fill gaps for lateral inflow rate and runoff depth in watersheds. Thus, the goal of the study was the application of the inverse problem method (IP). The lateral inflow rate was generally obtained as a Fourier transform to represent any watersheds. The study was developed using a small catchment in the Amazon where intense rainfall events occur, producing runoff and sediments, which affect rural populations. Lateral inflow rate and runoff depth were derived using precipitation data and parameters estimated through the KINEROS2 (K2)/direct model (DM) model and the ensuing solution methods with MCMC (Markov chains Monte Carlo)/Fourier transform. The developed method was applied to four rainfall-runoff events, leading to a good fit between the observed and predicted data (Nash-Sutcliffe coefficients between 0.76 and 0.85 and RMSE values between 1.80 mm and 6.72 mm).

Outcrops play an important role in groundwater recharge. Understanding groundwater origins, dynamics and its correlation with different water sources is essential for effective water resources management and planning in terms of quantity and quality. In the case of the Guarani Aquifer System (GAS) outcrop areas are particularly vulnerable to groundwater pollution due to direct recharge processes. This study focuses on the Alto Jacaré-Pepira sub-basin, a watershed near Brotas, a city in the central region of the state of São Paulo, Brazil, where groundwater is vital for supporting tourism, agriculture, urban water supply, creeks, river and wetlands. The area has a humid tropical climate with periods of both intense rainfall and drought, and the rivers remain perennial throughout the year. Therefore, the aim of this study is to investigate the interconnections between a spring and its potential sources of contribution, namely rain and groundwater, in order to elucidate the relationships between the different water sources. To achieve this, on-site monitoring of groundwater depth, rainfall amount, and stable isotope ratios (deuterium ( 2 H) and oxygen-18 ( 18 O)) from rain, spring discharge, and a monitoring well was carried out from 2013 to 2021. The results indicate that the mean and standard deviations for δ 18 O in rainwater exhibit higher variability, resulting in -4.49 ± 3.18 ‰ VSMOW, while δ 18 O values from the well show minor variations, similar to those of the spring, recording -7.25 ± 0.32 ‰ and -6.94 ± 0.28 ‰ VSMOW, respectively. The mixing model's outcomes reveal seasonal variations in water sources contribution and indicate that groundwater accounts for approximately 80 % of spring discharge throughout the year. Incorporating stable isotopes into hydrological monitoring provides valuable data for complementing watershed analysis. The values obtained support the significance of the aquifer as a primary source, thereby offering critical insights into stream dynamics of the region.

The leafroller Argyrotaenia sphaleropa (Meyrick) is an important pest of temperate fruits. Its biology and population dynamics are strongly influenced by temperature. In this context, this study aims to select a mathematical model that accurately describes the temperature-dependent development rate of A. sphaleropa and applies this model to predict the impact of climate change on the number of annual generations (voltinism) of the pest in southern Brazil. Nine mathematical models were employed to fit the species' developmental rate at different constant temperatures. Voltinism was projected using climate data from the current period (1994-2013) and projections for 2050 and 2070. The Brière-1 model (D(T) = aT(T-T L )(T H -T) 1/2 ) provided the best fit for the temperature-dependent developmental rate of A. sphaleropa. According to this model, the regions with the highest voltinism under current climatic conditions are the northern and central areas of Paraná, the western and northeastern regions of Santa Catarina, and northwestern Rio Grande do Sul. The model also predicts a rise in A. sphaleropa voltinism as a consequence of climate change, especially in the mountainous regions of Santa Catarina and Rio Grande do Sul, with projected increases of up to 25.1%. These regions encompass most areas where temperate fruits used as hosts by the leafroller are cultivated. This study represents a significant advancement in understanding the implications of global warming on A. sphaleropa voltinism and suggests that forthcoming climatic conditions will likely favor the species across much of southern Brazil., (© 2024. Sociedade Entomológica do Brasil.)

In this paper, it is aimed, for the first time, at deriving simple models, leveraging the trend analysis in order to estimate the future greenhouse gas emissions associated with coal combustion. Due to the expectations of becoming the center of global economic development in the future, BRICS-T (Brazil, the Russian Federation, India, China, South Africa, and Turkiye) countries are adopted as cases in the study. Following the models' derivation, their statistical validations and estimating accuracies are also tested through various metrics. In addition, the future greenhouse gas emissions associated with coal combustion are estimated by the derived models. The results demonstrate that the derived models can be successfully used as a tool for estimating the greenhouse gas emissions associated with coal combustions with accuracy ranges from at least 90% to almost 98%. Moreover, the estimating results show that the total amount of greenhouse gas emissions associated with coal combustions in the relevant countries and in the world will increase to 14 BtCO 2eq and 19 BtCO 2eq by 2035, with an annual growth of 2.39% and 1.71%, respectively. In summary, the current study's findings affirm the usefulness of trend analysis in deriving models to estimate greenhouse gas emissions associated with coal combustion., (© 2024. The Author(s).)

Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42-2.46), decreasing to 1.00 (95% CI: 0.99-1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5-13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2-6.4), 12.9 (95% CI: 12.5-13.3), and 16.8 (95% CI: 15.8-17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%-0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011-0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lima et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A comprehensive mathematical model is proposed to study two strains of dengue virus with saturated incidence rates and quarantine measures. Imperfect dengue vaccination is also assumed in the model. Existence, uniqueness and stability of the proposed model are proved using the results from fixed point and degree theory. Additionally, well constructed Lyapunov function candidates are also applied to prove the global stability of infection-free equilibria. It is also demonstrated that the model is generalized Ulam-Hyers stable under some appropriate conditions. The model is fitted to the real data of dengue epidemic taken from the city of Espirito Santo in Brazil. For the approximate solution of the model, a non-standard finite difference(NSFD) approach is applied. Sensitivity analysis is also carried out to show the influence of different parameters involved in the model. The behaviour of the NSFD is also assessed under different denominator functions and it is observed that the choice of the denominator function could influence the solution trajectories. Different scenario analysis are also assessed when the reproduction number is below or above one. Furthermore, simulations are also presented to assess the epidemiological impact of dengue vaccination and quarantine measures for infected individuals., (© 2024. The Author(s).)

The global economic downturn due to the COVID-19 pandemic, war in Ukraine, and worldwide inflation surge may have a profound impact on poverty-related infectious diseases, especially in low-and middle-income countries (LMICs). In this work, we developed mathematical models for HIV/AIDS and Tuberculosis (TB) in Brazil, one of the largest and most unequal LMICs, incorporating poverty rates and temporal dynamics to evaluate and forecast the impact of the increase in poverty due to the economic crisis, and estimate the mitigation effects of alternative poverty-reduction policies on the incidence and mortality from AIDS and TB up to 2030. Three main intervention scenarios were simulated-an economic crisis followed by the implementation of social protection policies with none, moderate, or strong coverage-evaluating the incidence and mortality from AIDS and TB. Without social protection policies to mitigate the impact of the economic crisis, the burden of HIV/AIDS and TB would be significantly larger over the next decade, being responsible in 2030 for an incidence 13% (95% CI 4-31%) and mortality 21% (95% CI 12-34%) higher for HIV/AIDS, and an incidence 16% (95% CI 10-25%) and mortality 22% (95% CI 15-31%) higher for TB, if compared with a scenario of moderate social protection. These differences would be significantly larger if compared with a scenario of strong social protection, resulting in more than 230,000 cases and 34,000 deaths from AIDS and TB averted over the next decade in Brazil. Using a comprehensive approach, that integrated economic forecasting with mathematical and epidemiological models, we were able to show the importance of implementing robust social protection policies to avert a significant increase in incidence and mortality from AIDS and TB during the current global economic downturn., (© 2024. The Author(s).)

This article aims to evaluate the association between birth weight and asthma in adulthood, estimated by employing structural equation modeling. Cohort study with 1,958 participants aged 23-25 years from Ribeirão Preto, São Paulo, Brazil. Standardized questionnaires were applied and pulmonary function evaluated, including bronchial reactivity with methacholine. A theoretical model was proposed to explore the effects of birth weight and asthma in adulthood. Asthma, socioeconomic status at birth (Birth SES), and current socioeconomic status (Adult SES) were obtained by constructs. Maternal age, sex, skin color, body mass index (BMI), smoking, parental asthma history, history of respiratory infection before five years old, history of hospitalization for lung disease before two years old, and atopy were the studied variables. 14.1% of participants were diagnosed with asthma. Birth weight was associated with asthma (Standardized Coefficient - SCtotal=-0.110; p=0.030), and an indirect effect was also observed (SCindirect=-0.220; p=0.037), mediated by hospitalization before two years and respiratory infection before five years. Lower birth weight showed an increased risk of asthma in adulthood and the SES Birth and Adult SES variables underlie this association.

This study analyzes the main organization patterns used by primary health care (PHC) services in municipal networks and evaluates them according to indicators of local management-administration interface. Evaluative research analyzed 461 municipalities in São Paulo, Brazil, that participated in the Primary Care Services Quality Assessment Survey (QualiAB) in 2017/2018, classified according to the organizational arrangements composition of 2,472 PHC services. Eight indicators of local management and administration were selected to evaluate the identified patterns. Results indicate two groups of municipalities: homogeneous, with services presenting the same arrangement (43.6%); and heterogeneous, with different arrangements (56.4%). These were subdivided into seven patterns that ranged from homogeneous-traditional, homogeneous-Family Health Strategy, homogeneous-mixed, and different combinations in the heterogeneous group. All indicators showed significant differences between groups (p < 0.001), especially the homogeneous-traditional group, which presented an organizational pattern far from the desired model of a comprehensive and problem-solving PHC. Those integrated with family health units (FHU) and basic health units with community health workers and/or family health teams (BHU/FHU) showed a pattern closer to a comprehensive model - with planning and evaluation actions committed to the local reality and qualification of care. Implementation of federal and state policies are essential for defining the PHC health care model adopted by municipalities.

This paper presents the graphical results of the Lagrangian-model and the weathering processes associated with oil spills in the tropical South Atlantic, taking into account the meteorological and oceanographic conditions of the study region. The scenarios were created in the Brazilian-NE waters adjacent, with simulation times of 670 h, and densities of 35, 25, and 15API with volume of 1590 m 3 were considered. The main results showed that the meteo-oceanographic characteristics of the study region influence the trajectories and weathering processes in the oil spill. The trajectories varied for each launch point and reached the continent severely in January and October. The associated weathering processes showed higher rates in September and lower rates in April, indicative of the influence of phenomena such as Intertropical Tropical Convergence Zone and warm pool in the South Atlantic region. Sea surface temperature and wind speed are key factors that correlate positively with these months., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objectives: Constructing and validating a theoretical model of relationships between dental services use and socioeconomic characteristics, oral health status, primary care coverage, and public dental services., Methods: The first stage of the study consisted of developing a theoretical-conceptual model to demonstrate the expected relationships between variables based on the literature. In the second stage, we tested the proposed theoretical model using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, using data from the Brazilian National Health Survey conducted in 2019 with a sample of 41,664 individuals aged 15 or older., Results: This study successfully defined a theoretical model that explains the systematic relationships involving public dental services utilization. Socioeconomic status was negatively associated with oral health status (β = -0.376), enrollment in primary care facilities (β = -0.254), and the use of public dental consultations (β = -0.251). Being black, indigenous, or living in a rural area was directly associated with lower socioeconomic status and greater use of public dental services., Conclusions: The identified relationships, establishing a theoretical basis for further investigations, also provide evidence of a public access policy's effect on oral health services on equity, supporting the construction of more effective and equitable public policies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Galvão, Roncalli. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: Longitudinal patterns of growth in early childhood are associated with health conditions throughout life. Knowledge of such patterns and the ability to predict them can lead to better prevention and improved health promotion in adulthood. However, growth analyses are characterized by significant variability, and pattern detection is affected by the method applied. Moreover, pattern labelling is typically performed based on ad hoc methods, such as visualizations or clinical experience. Here, we propose a novel pipeline using features extracted from growth trajectories using mathematical, statistical and machine-learning approaches to predict growth patterns and label them in a systematic and unequivocal manner., Methods: We extracted mathematical and clinical features from 9577 children growth trajectories embedded with machine-learning predictions of the growth patterns. We experimented with two sets of features (CAnonical Time-series Characteristics and trajectory features specific to growth), developmental periods and six machine-learning classifiers. Clinical experts provided labels for the detected patterns and decision rules were created to associate the features with the labelled patterns. The predictive capacity of the extracted features was validated on two heterogenous populations (The Applied Research Group for Kids and the 2004 Pelotas Birth Cohort, based in Canada and Brazil, respectively)., Results: Features predictive ability measured by accuracy and F1 score was ≥ 80% and ≥ 0.76 respectively in both cohorts. A small number of features (n = 74) was sufficient to distinguish between growth patterns in both cohorts. Slope, intercept of the trajectory, age at peak value, start value and change of the growth measure were among the top identified features., Conclusion: Growth features can be reliably used as predictors of growth patterns and provide an unbiased understanding of growth patterns. They can be used as tool to reduce the effort to repeat analysis and variability concerning anthropometric measures, time points and analytical methods, in the context of the same or similar populations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Sporotrichosis is a subcutaneous mycosis with a global distribution, also known as "rose gardener's disease". Brazil is experiencing a rapid spread of the zoonotic transmission of of Sporothrix brasiliensis, the main etiological agent of this disease in this country, affecting domestic felines. Cost-effective interventions need to be developed to control this emergent public health problem. To allow for the comparison of alternative control strategies, we propose in this paper, a mathematical model representing the transmission of S. brasiliensis among cats, stratified by age and sex. Analytical properties of the model are derived and simulations show possible strategies for reducing the endemic levels of the disease in the cat population, with a positive impact on human health. The scenarios included mass treatment of infected cats and mass implementation of contact reduction practices, such as neutering. The results indicate that mass treatment can reduce substantially the disease prevalence, and this effect is potentialized when combined with neutering or other contact-reduction interventions. On the other hand, contact-reduction methods alone are not sufficient to reduce prevalence., Competing Interests: NO authors have competing interests Enter: The authors have declared that no competing interests exist., (Copyright: © 2023 Araújo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO 2 dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Area (RJMA), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes so that the largest SO 2 concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Currently, it is estimated that 37.6 million people are living with the HIV/AIDS virus worldwide, placing HIV/AIDS among the ten leading causes of death, mostly among low- and lower-middle-income countries. Despite the effective intervention in the prevention and treatment, this reduction did not occur equally among populations, subpopulations and geographic regions. This difference in the occurrence of the disease is associated with the social determinants of health (SDH), which could affect the transmission and maintenance of HIV. With the recognition of the importance of SDH in HIV transmission, the development of mathematical models that incorporate these determinants could increase the accuracy and robustness of the modeling. This article aims to propose a theoretical and conceptual way of including SDH in the mathematical modeling of HIV/AIDS. The theoretical mathematical model with the Social Determinants of Health has been developed in stages. For the selection of SDH that were incorporated into the model, a narrative literature review was conducted. Secondly, we proposed an extended model in which the population (N) is divided into Susceptible (S), HIV-positive (I), Individual with AIDS (A) and individual under treatment (T). Each SDH had a different approach to embedding in the model. We performed a calibration and validation of the model. A total of 31 SDH were obtained in the review, divided into four groups: Individual Factors, Socioeconomic Factors, Social Participation, and Health Services. In the end, four determinants were selected for incorporation into the model: Education, Poverty, Use of Drugs and Alcohol abuse, and Condoms Use. the section "Numerical simulation" to simulate the influence of the poverty rate on the AIDS incidence and mortality rates. We used a Brazilian dataset of new AIDS cases and deaths, which is publicly available. We calibrated the model using a multiobjective genetic algorithm for the years 2003 to 2019. To forecast from 2020 to 2035, we assumed two lines of poverty rate representing (i) a scenario of increasing and (ii) a scenario of decreasing. To avoid overfitting, we fixed some parameters and estimated the remaining. The equations presented with the chosen SDH exemplify some approaches that we can adopt when thinking about modeling social effects on the occurrence of HIV. The model was able to capture the influence of the employment/poverty on the HIV/AIDS incidence and mortality rates, evidencing the importance of SDOH in the occurrence of diseases. The recognition of the importance of including the SDH in the modeling and studies on HIV/AIDS is evident, due to its complexity and multicausality. Models that do not take into account in their structure, will probably miss a great part of the real trends, especially in periods, as the current on, of economic crisis and strong socioeconomic changes., (© 2022. The Author(s).)

The objective of this study was to reflect on the meanings of the work of Brazilian nursing care in the context of the Covid-19 pandemic. This is a theoretical study anchored in the definition of meanings of work, according to Estelle Morin's perspective. The work developed by nursing professionals became even more evident in pandemic times, with the precarious conditions of health services in Brazil coming to light. During the pandemic, the incorporation of meanings of work became more important, given that the society recognized the relevance of these professionals in dealing with the pandemic, and this allowed the discussion about their social, political, and economic recognition. The impacts of nursing performance during the Covid-19 pandemic are related to the economic issue, social values, autonomy in the exercise of the profession, recognition, and safety, reflecting on the sense of purpose of work. Thus, the work that makes sense for nursing professionals is related to professional appreciation, specifically, to salary recognition, while what makes no sense is what hinders intellectual, cognitive, and financial progress. Thus, conditions were imposed that give directions to ambivalent meanings to work.

The SIR (susceptible-infectious-recovered) model is a well known method for predicting the number of people (or animals) in a population who become infected by and then recover from a disease. Modifications can include categories such people who have been exposed to the disease but are not yet infectious or those who die from the disease. However, the model has nearly always been applied to the entire population of a country or state but there is considerable observational evidence that diseases can spread at different rates in densely populated urban regions and sparsely populated rural areas. This work presents a new approach that applies a SIR type model to a country or state that has been divided into a number of geographical regions, and uses different infection rates in each region which depend on the population density in that region. Further, the model contains a simple matrix based method for simulating the movement of people between different regions. The model is applied to the spread of disease in the United Kingdom and the state of Rio Grande do Sul in Brazil., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The objective of this study was to identify parameters that best discriminate between selected and non-selected players for the Brazilian under-19 men's volleyball team and propose mathematical models to identify high-performance players. To this end, 18 selected (16.89±0.96 years) and 138 non-selected (16.91±0.74 years) players for the under-19 team were assessed for the training profile, anthropometric profile, and physical performance level. The discriminant function analysis was used to build the models, with a significance of α<0.05. The spike jump reach showed a greater correlation with the discriminant scores obtained in the two models (r=0.701; r=0.782). The 10 variables included in Model 1 helped identify 88.9% of the players selected in their group of origin; Model 2 - obtained by the spike jump reach and duration of playing experience - identified 83.3% of the players selected. Therefore, coaches should be aware that differences between the selected and non-selected players are multi-factorial, with the spike jump reach being the most relevant assessment factor. Furthermore, good players for the selection can be identified using the two models: Model 1 promises greater success with ten assessments, whereas Model 2 allows the identification of suitable players for the under-19 men's volleyball team with only two simple assessments.

Since the emergence of the novel coronavirus disease 2019 (COVID-19), mathematical modelling has become an important tool for planning strategies to combat the pandemic by supporting decision-making and public policies, as well as allowing an assessment of the effect of different intervention scenarios. A proliferation of compartmental models were developed by the mathematical modelling community in order to understand and make predictions about the spread of COVID-19. While compartmental models are suitable for simulating large populations, the underlying assumption of a well-mixed population might be problematic when considering non-pharmaceutical interventions (NPIs) which have a major impact on the connectivity between individuals in a population. Here we propose a modification to an extended age-structured SEIR (susceptible-exposed-infected-recovered) framework, with dynamic transmission modelled using contact matrices for various settings in Brazil. By assuming that the mitigation strategies for COVID-19 affect the connections among different households, network percolation theory predicts that the connectivity among all households decreases drastically above a certain threshold of removed connections. We incorporated this emergent effect at population level by modulating home contact matrices through a percolation correction function, with the few additional parameters fitted to hospitalisation and mortality data from the city of São Paulo. Our model with percolation effects was better supported by the data than the same model without such effects. By allowing a more reliable assessment of the impact of NPIs, our improved model provides a better description of the epidemiological dynamics and, consequently, better policy recommendations., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To elaborate a Theoretical Model of Sexual and Reproductive Health care offered in Primary Health Care., Method: Documentary research, with a qualitative approach. Government documents and evaluative studies constituted the data sources, collected between August 2018 and June 2019, and analyzed based on the health evaluation literature., Results: Promotion of Sexual and Reproductive Health and Clinical Care were two structural components of the theoretical model of sexual and reproductive health care in Primary Care. The model includes disease prevention activities, health promotion, treatments, and diagnoses related to sexual and reproductive health., Conclusion: The theoretical model developed in this study presents the theory that supports the operationalization of sexual and reproductive health care in primary care according to government regulations in Brazilian scenario and may be useful in future evaluative research on the subject.

Background: Medication literacy reflects the specific literacy skills necessary to act on medication-related information. A comprehensive and clear conceptual model that reflects medication literacy is lacking. Conceptual models describe the components that represent a construct, allowing for its understanding and providing a rationale for its measurement., Objectives: To propose a conceptual model to support the development of an instrument to measure medication literacy., Methods: This is a three-phase methodological study. A literature review was conducted to identify the components that should be part of the preliminary conceptual model. Subsequently, the importance of its components was judged on a Likert-5 scale. Then, the model was refined, and the dimensions and subdimensions of medication literacy were defined., Results: Forty-five experts were included, with a mean age of 39.9 (SD = 10.5) years, most of them females (86.7%) and pharmacists (80%). All components of the preliminary conceptual model were considered important to extremely important by most experts. "Dosing information", "medication name", and "processing the information received about their medication and acting upon medication instructions" were the components considered most important. In the refinement phase, we identified that the construct has four dimensions - functional literacy, communicative literacy, critical literacy, and numeracy, which include the subdimensions to access, understand, evaluate, calculate, and communicate medication-related information., Conclusions: The conceptual model allowed identifying the components that represent medication literacy, which will support the development of an instrument for measuring the construct in Brazil., (Copyright © 2021 Elsevier Inc. All rights reserved.)

We report strong evidence of the importance of contact hubs (or superspreaders) in mitigating the current COVID-19 pandemic. Contact hubs have a much larger number of contacts than the average in the population, and play a key role on the effectiveness of vaccination strategies. By using an age-structures compartmental SEIAHRV (Susceptible, Exposed, Infected symptomatic, Asymptomatic, Hospitalized, Recovered, Vaccinated) model, calibrated from available demographic and COVID-19 incidence, and considering separately those individuals with a much greater number of contacts than the average in the population, we show that carefully choosing who will compose the first group to be vaccinated can impact positively the total death toll and the demand for health services. This is even more relevant in countries with a lack of basic resources for proper vaccination and a significant reduction in social isolation. In order to demonstrate our approach we show the effect of hypothetical vaccination scenarios in two countries of very different scales and mitigation policies, Brazil and Portugal., Competing Interests: The authors have declared that no competing interests exist.

The global impact of coronavirus disease 2019 (COVID-19) is unprecedented, and many control and prevention measures have been implemented to test for and trace COVID-19. However, invisible-spreaders, who are associated with nucleic acid detection and asymptomatic infections, have received insufficient attention in the current COVID-19 control efforts. In this paper, we analyze the time series infection data for Italy, Germany, Brazil, India and Sweden since the first wave outbreak to address the following issues through a series of experiments. We conclude that: 1) As of June 1, 2020, the proportion of invisible-spreaders is close to 0.4% in Sweden, 0.8% in early Italy and Germany, and 0.4% in the middle and late stages. However, in Brazil and India, the proportion still shows a gradual upward trend; 2) During the spread of this pandemic, even a slight increase in the proportion of invisible-spreaders could have large implications for the health of the community; and 3) On resuming work, the pandemic intervention measures will be relaxed, and invisible-spreaders will cause a new round of outbreaks., Competing Interests: The authors have declared that no competing interests exist.

There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases., (© 2021. The Author(s).)

We investigate, through a data-driven contact tracing model, the transmission of COVID-19 inside buses during distinct phases of the pandemic in a large Brazilian city. From this microscopic approach, we recover the networks of close contacts within consecutive time windows. A longitudinal comparison is then performed by upscaling the traced contacts with the transmission computed from a mean-field compartmental model for the entire city. Our results show that the effective reproduction numbers inside the buses, [Formula: see text], and in the city, [Formula: see text], followed a compatible behavior during the first wave of the local outbreak. Moreover, by distinguishing the close contacts of healthcare workers in the buses, we discovered that their transmission, [Formula: see text], during the same period, was systematically higher than [Formula: see text]. This result reinforces the need for special public transportation policies for highly exposed groups of people., (© 2021. The Author(s).)

In the past year, the global epidemic situation is still not optimistic, showing a trend of continuous expansion. With the research and application of vaccines, there is an urgent need to develop some optimal vaccination strategies. How to make a reasonable vaccination strategy to determine the priority of vaccination under the limited vaccine resources to control the epidemic and reduce human casualties? We build a dynamic model with vaccination which is extended the classical SEIR model. By fitting the epidemic data of three countries-China, Brazil, Indonesia, we have evaluated age-specific vaccination strategy for the number of infections and deaths. Furthermore, we have evaluated the impact of age-specific vaccination strategies on the number of the basic reproduction number. At last, we also have evaluated the different age structure of the vaccination priority. It shows that giving priority to vaccination of young people can control the number of infections, while giving priority to vaccination of the elderly can greatly reduce the number of deaths in most cases. Furthermore, we have found that young people should be mainly vaccinated to reduce the number of infections. When the emphasis is on reducing the number of deaths, it is important to focus vaccination on the elderly. Simulations suggest that appropriate age-specific vaccination strategies can effectively control the epidemic, both in terms of the number of infections and deaths., Competing Interests: The authors have declared that no competing interests exist.

This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies in Brazil. Using both aggregated (from large Internet companies) and fine-grained (from Departments of Motor Vehicles) mobility data sources, our work sheds light on the effect of mobility on the pandemic situation in the Brazilian territory. Our main contribution is to show how mobility data, particularly fine-grained ones, can offer valuable insights into virus propagation. For this, we propose a modification in the SENUR model to add mobility information, evaluating different data availability scenarios (different information granularities), and finally, we carry out simulations to evaluate possible public policies. In particular, we conduct a case study that shows, through simulations of hypothetical scenarios, that the contagion curve in several Brazilian cities could have been milder if the government had imposed mobility restrictions soon after reporting the first case. Our results also show that if the government had not taken any action and the only safety measure taken was the population's voluntary isolation (out of fear), the time until the contagion peak for the first wave would have been postponed, but its value would more than double., Competing Interests: NO authors have competing interests.

Objective: To interpret and build a theoretical model of the user's experience in the education process in physical rehabilitation settings., Method: Qualitative research, based on the Grounded Theory. The setting was a Rehabilitation Center of Rede Lucy Montoro, located in an inland city of the State of São Paulo. Data collection took place from January to October 2019, based on 28 interviews with three sample groups. The collection and analysis process was guided by theoretical sampling., Results: A total of 122 conceptual codes emerged. The main category established was "Experiencing education in rehabilitation"; as Conditions, the categories: relying on team support, and considering the group dynamics; as Actions-Interactions, the category: experiencing instructions; and as Consequences, the categories: promoting change of habits, and finding resistance in the follow-up., Conclusion: The experience in the education process in rehabilitation indicates that this is an action that requires adequate communication and acceptance by the team. In group activities, although there are experiences exchanges among the members, the heterogeneity of situations shall be considered.

Biodiversity conservation has faced many challenges, especially the conversion of natural areas that compete with use for agriculture, energy production and mineral extraction. This problem is further aggravated by lack of knowledge of the biodiversity that exists and the geographical distribution of different groups. The objectives of our study were to examine the distributional pattern of Gerromorpha diversity in Brazil, create a map of conservation priority areas, estimate the degree of protection that the current network of protected areas guarantees to this insect group, and identify the size thresholds in geographical distributions that would allow species to be protected. We used species occurrences from the Water Bugs Distributional Database, and we used 19 bioclimatic variables to build models of the potential distributions of species using the MaxEnt program. Using the potential model results, we calculated diversity metrics and overlapped them with the current state and federal "conservation units" (protected areas for biodiversity) in Brazil. Total beta diversity and turnover portions were separated into two faunistic groups, one in northern and the other in southern Brazil. The Amazon has higher beta diversity than what was predicted by the null models. We detected a positive relationship between species distribution area and occurrence in conservation units. Conservation units with less than 250 km 2 do not protect Gerromorpha species. Our results reinforce the necessity of formulating new conservation strategies for this group, contemplating species with both restricted and ample distributions, because rare and specialist species are the most harmed by habitat reduction, given that they are more sensitive to environmental disturbance., (© 2021. The Author(s).)

This essay aims to present and discuss the theoretical framework for the COVID-19 syndemic. The first part presents the foundations and principles of syndemic theory. For the purposes of this essay, syndemic was defined as a process of synergic interaction between two or more diseases, in which the effects are mutually enhanced. We discussed the three principal typologies of syndemic interaction: mutually causal epidemics; epidemics interacting synergically; and serial causal epidemics. In the second part, COVID-19 is analyzed as a syndemic resulting from the interaction between various groups of diseases and the socioeconomic context. The theoretical model considered the interaction between COVID-19 and chronic noncommunicable diseases, infectious and parasitic diseases, and mental health problems. The essay addressed how social iniquities and conditions of vulnerability act at various levels to increase the effect of COVID-19 and other pandemics. The last section discusses the need for comprehensive, multisector, and integrated responses to COVID-19. A model for intervention was presented that involves the patient care and socioeconomic dimensions. In the sphere of patient care, the authors defend the structuring of strong and responsive health systems, accessible to the entire population. The economic and social dimension addressed the issue of reclaiming the ideals of solidarity, the health promotion strategy, and emphasis on social determinants of health. In conclusion, the lessons learned from the syndemic approach to COVID-19 call on government and society to develop policies that link clinical, sanitary, socioeconomic, and environmental interventions.

The number of reforestation projects worldwide is increasing. In many cases funding is obtained through the claimed carbon capture of the trees, presented as immediate and durable, whereas reforested plots need time and maintenance to realise their carbon capture potential. Further, claims usually overlook the environmental costs of natural or anthropogenic disturbances during the forest's lifetime, and greenhouse gas (GHG) emissions associated with the reforestation are not allowed for. This study uses life cycle assessment to quantify the carbon footprint of setting up a reforestation plot in the Peruvian Amazon. In parallel, we combine a soil carbon model with an above- and below-ground plant carbon model to predict the increase in carbon stocks after planting. We compare our results with the carbon capture claims made by a reforestation platform. Our results show major errors in carbon accounting in reforestation projects if they (1) ignore the time needed for trees to reach their carbon capture potential; (2) ignore the GHG emissions involved in setting up a plot; (3) report the carbon capture potential per tree planted, thereby ignoring limitations at the forest ecosystem level; or (4) under-estimate tree losses due to inevitable human and climatic disturbances. Further, we show that applications of biochar during reforestation can partially compensate for project emissions., (© 2021. The Author(s).)

The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes is one of the major challenges of disease control. Considering the growth of epidemic curve and the circulating SARS-CoV-2 variants in Brazil, the role of locally prevalent E484K and N501Y substitutions in contributing to the epidemiological outcomes is of public health interest for investigation. We developed a likelihood-based statistical framework to reconstruct reproduction numbers, estimate transmission advantage associated with different SARS-CoV-2 variants regarding the marking (identifying) 484K and 501Y substitutions (including Alpha, Zeta, and Gamma variants) in Brazil, and explored the interactive effects of genetic activities on transmission advantage marked by these two mutations. We found a significant transmission advantage associated with the 484K/501Y variants (including P.1 or Gamma variants), which increased the infectivity significantly by 23%. In contrast and by comparison to Gamma variants, E484K or N501Y (including Alpha or Zeta variants) substitution alone appeared less likely to secure a concrete transmission advantage in Brazil. Our finding indicates that the combined impact of genetic activities on transmission advantage marked by 484K/501Y outperforms their independent contributions in Brazil, which implies an interactive effect in shaping the increase in the infectivity of COVID-19. Future studies are needed to investigate the mechanisms of how E484K and N501Y mutations and the complex genetic mutation activities marked by them in SARS-CoV-2 affect the transmissibility of COVID-19.

To support COVID-19 pandemic planning, we develop a model-inference system to estimate epidemiological properties of new SARS-CoV-2 variants of concern using case and mortality data while accounting for under-ascertainment, disease seasonality, non-pharmaceutical interventions, and mass-vaccination. Applying this system to study three variants of concern, we estimate that B.1.1.7 has a 46.6% (95% CI: 32.3-54.6%) transmissibility increase but nominal immune escape from protection induced by prior wild-type infection; B.1.351 has a 32.4% (95% CI: 14.6-48.0%) transmissibility increase and 61.3% (95% CI: 42.6-85.8%) immune escape; and P.1 has a 43.3% (95% CI: 30.3-65.3%) transmissibility increase and 52.5% (95% CI: 0-75.8%) immune escape. Model simulations indicate that B.1.351 and P.1 could outcompete B.1.1.7 and lead to increased infections. Our findings highlight the importance of preventing the spread of variants of concern, via continued preventive measures, prompt mass-vaccination, continued vaccine efficacy monitoring, and possible updating of vaccine formulations to ensure high efficacy., (© 2021. The Author(s).)

Reinfection and multiple viral strains are among the latest challenges in the current COVID-19 pandemic. In contrast, epidemic models often consider a single strain and perennial immunity. To bridge this gap, we present a new epidemic model that simultaneously considers multiple viral strains and reinfection due to waning immunity. The model is general, applies to any viral disease and includes an optimal control formulation to seek a trade-off between the societal and economic costs of mitigation. We validate the model, with and without mitigation, in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil. The model can derive optimal mitigation strategies for any number of viral strains, whilst also evaluating the effect of distinct mitigation costs on the infection levels. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, and therefore demand more restrictive measures in the future., Competing Interests: The authors have declared that no competing interests exist.

Background: We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated., Methods: The model's main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal., Results: The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation., Conclusions: Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system's cost is minimized., (© 2021. The Author(s).)

This paper empirically models the dynamics of Brazilian government bond (BGB) yields based on monthly macroeconomic data, in the context of the evolution of the key macroeconomic variables in Brazil. The results show that the current short-term interest rate has a decisive influence on the long-term interest rate on BGBs, after controlling for various key macroeconomic variables, such as inflation and industrial production. These findings support John Maynard Keynes's claim that the central bank's actions influence the long-term interest rate on government bonds mainly through the current short-term interest rate. These findings have important policy implications for Brazil. This paper relates the findings of the estimated models to ongoing debates in fiscal and monetary policies., Competing Interests: The authors have declared that no competing interests exist.

Brazil was considered one of the emerging epicenters of the coronavirus pandemic in 2021, experiencing over 3000 daily deaths caused by the virus at the peak of the second wave. In total, the country had more than 20.8 million confirmed cases of COVID-19, including over 582,764 fatalities. A set of emerging variants arose in the country, some of them posing new challenges for COVID-19 control. The goal of this study was to describe mutational events across samples from Brazilian SARS-CoV-2 sequences publicly obtainable on Global Initiative on Sharing Avian Influenza Data-EpiCoV (GISAID-EpiCoV) platform and to generate indexes of new mutations by each genome. A total of 16,953 SARS-CoV-2 genomes were obtained, which were not proportionally representative of the five Brazilian geographical regions. A comparative sequence analysis was conducted to identify common mutations located at 42 positions of the genome (38 were in coding regions, whereas two were in 5' and two in 3' UTR). Moreover, 11 were synonymous variants, 27 were missense variants, and more than 44.4% were located in the spike gene. Across the total of single nucleotide variations (SNVs) identified, 32 were found in genomes obtained from all five Brazilian regions. While a high genomic diversity has been reported in Europe given the large number of sequenced genomes, Africa has demonstrated high potential for new variants. In South America, Brazil, and Chile, rates have been similar to those found in South Africa and India, providing enough "space" for new mutations to arise. Genomic surveillance is the central key to identifying the emerging variants of SARS-CoV-2 in Brazil and has shown that the country is one of the "hotspots" in the generation of new variants.

Climate change represents an unprecedented threat to global biodiversity and, for many species, gaps in our knowledge of their biology remain acute. Gaps in baseline knowledge, such as confirmed identifications (Linnean shortfalls) and adequate collections (Wallacean shortfalls), need to be minimized with new studies, since this is often critical for effective conservation. Despite the increase in scientific research on primates in the southwest of the Brazilian Amazon, little is known about the species Mico nigriceps (Ferrari & Lopes, 1992) Primates, Platirryni. In the current study, we sought to reduce the extent of the Wallacean shortfall for M. nigriceps, understand whether climate change represents a threat to the distribution of the species, and identify priority areas for its conservation. Accordingly, we provide 121 new records in 14 locations, obtained directly from the field, and five from the literature. Using this, we carried out ecological niche modeling, to better understand how environmental suitability might limit the area occupied by the species. We then projected a distribution for 2070 with the SSP2-4.5 (more optimistic) and SSP5-8.5 (more pessimistic) scenarios. Our data confirmed the geographic distribution of the species as being restricted to headwaters of the Ji-Paraná/Machado river, but with a 400 km extension to the south. Under the modeled climate change scenarios, the area suitable for the species declines by 21% under the most optimistic, and by 27% in the pessimistic, scenario across the projected 50-year period. Although we have expanded the area of known occurrence for this species, we point out that climate change threatens the stability of this newly-discovered population strongly, and that this danger is intensified by deforestation, fire and hunting. We recommend that further studies be carried out to confirm the presence of the species in adjacent areas, those indicated by generated models as being potential environmentally suitable. In addition, we recommend intensifying forest restoration in currently pastured areas, and protection of the areas forming the current and future habitat of this species through such measures as protected area creation., Competing Interests: No authors have competing intests.

Excess of deaths is a technique used in epidemiology to assess the deaths caused by an unexpected event. For the present COVID-19 pandemic, we discuss the performance of some linear and nonlinear time series forecasting techniques widely used for modeling the actual pandemic and provide estimates for this metric from January 2020 to April 2021. We apply the results obtained to evaluate the evolution of the present pandemic in Brazil and Spain, which allows in particular to compare how well (or bad) these countries have managed the pandemic. For Brazil, our calculations refute the claim made by some officials that the present pandemic is "a little flu". Some studies suggest that the virus could be lying dormant across the world before been detected for the first time. In that regard, our results show that there is no evidence of deaths by the virus in 2019., Competing Interests: The authors have declared that no competing interests exist.