Enhanced coalbed methane well production prediction framework utilizing the CNN-BL-MHA approach.

As the mechanization of the CBM extraction process advances and geological conditions continuously evolve, the production data from CBM wells is deviating increasingly from linearity, thereby presenting a significant challenge in accurately predicting future gas production from these wells. When it comes to predicting the production of CBM, a single deep-learning model can face several drawbacks such as overfitting, gradient explosion, and gradient disappearance. These issues can ultimately result in insufficient prediction accuracy, making it important to carefully consider the limitations of any given model. It's impressive to see how advanced technology can enhance the prediction accuracy of CBM. In this paper, the use of a CNN model to extract features from CBM well data and combine it with Bi-LSTM and a Multi-Head Attention mechanism to construct a production prediction model for CBM wells-the CNN-BL-MHA model-is fascinating. It is even more exciting that predictions of gas production for experimental wells can be conducted using production data from Wells W1 and W2 as the model's database. We compared and analyzed the prediction results obtained from the CNN-BL-MHA model we constructed with those from single models like ARIMA, LSTM, MLP, and GRU. The results show that the CNN-BL-MHA model proposed in the study has shown promising results in improving the accuracy of gas production prediction for CBM wells. It's also impressive that this model demonstrated super stability, which is essential for reliable predictions. Compared to the single deep learning model used in this study, its prediction accuracy can be improved up to 35%, and the prediction results match the actual yield data with lower error.

Multiple sclerosis: economic burden, therapeutic advances, and future forecasts in the Middle East and North Africa region.

INTRODUCTION: Multiple sclerosis (MS) is a persistent condition characterized by immune-mediated processes in the central nervous system, affecting around 2.8 million individuals globally. While historically less prevalent in the Middle East and North Africa (MENA) region, recent trends mirror the global rise in MS. AREA COVERED: The impact of MS is substantial, particularly in the MENA region, with costs per patient surpassing nominal GDP per capita in certain countries. Disease-modifying therapies aim to alleviate MS effects, but challenges persist, especially in managing progressive MS as it shifts from inflammatory to neurodegenerative phases. Limited resources in the MENA region hinder care delivery, though awareness initiatives and multidisciplinary centers are emerging. Contrary to global projections of a decline in the MS market, the MENA region is poised for growth due to increased prevalence, healthcare expenditures, and infrastructure investments. EXPERT OPINION: This review underscores the urgent necessity for effective treatments, robust disease management, and early diagnosis in tackling MS's repercussions in the MENA region. Bolstering resources tailored to MS patients and elevating the quality of care stand as pivotal strategies for enhancing health outcomes in this context. Taking decisive action holds the key to enhancing the overall well-being of individuals grappling with MS.

Reporting delays: A widely neglected impact factor in COVID-19 forecasts.

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean, and devoid of noise? The complexity and variability inherent in data collection and reporting suggest otherwise. While we cannot evaluate the integrity of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Using our framework, we expose and analyze reporting delays in eight regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data by using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50%. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.

Enhancing seasonal influenza projections: A mechanistic metapopulation model for long-term scenario planning.

In temperate regions, annual preparation by public health officials for seasonal influenza requires early-season long-term projections. These projections are different from short-term (e.g., 1-4 weeks ahead) forecasts that are typically updated weekly. Whereas short-term forecasts estimate what "will" likely happen in the near term, the goal of scenario projections is to guide long-term decision-making using "what if" scenarios. We developed a mechanistic metapopulation model and used it to provide long-term influenza projections to the Flu Scenario Modeling Hub. The scenarios differed in their assumptions about influenza vaccine effectiveness and prior immunity. The parameters of the model were inferred from early season hospitalization data and then simulated forward in time until June 3, 2023. We submitted two rounds of projections (mid-November and early December), with the second round being a repeat of the first with three more weeks of data (and consequently different model parameters). In this study, we describe the model, its calibration, and projections targets. The scenario projection outcomes for two rounds are compared with each other at state and national level reported daily hospitalizations. We show that although Rounds 2 and 3 were identical in definition, the addition of three weeks of data produced an improvement to model fits. These changes resulted in earlier projections for peak incidence, lower projections for peak magnitude and relatively small changes to cumulative projections. In both rounds, all four scenarios presented conceivable outcomes, with some scenarios agreeing well with observations. We discuss how to interpret this agreement, emphasizing that this does not imply that one scenario or another provides the ground truth. Our model's performance suggests that its underlying assumptions provided plausible bounds for what could happen during an influenza season following two seasons of low circulation. We suggest that such projections would provide actionable estimates for public health officials.

Economic and labour market impacts of migration in Austria: an agent-based modelling approach.

This study examines the potential economic and labour market impacts of a hypothetical but plausible migration scenario of 250,000 new migrants inspired by Austria's experience in 2015. Using the agent-based macroeconomic model developed by Poledna et al. (Eur Econ Rev, 151:104306, 2023. 10.1016/j.euroecorev.2022.104306, the study explores the detailed labour market outcomes for different groups in Austria's population and the macroeconomic effects of the migration scenario. The analysis suggests that Austria's economy and labour market have the potential to be resilient to the simulated migration influx. The results indicate a positive impact on GDP due to increased aggregate consumption and investment. The labour market experiences an increase in the unemployment rates of natives and previous migrants. In some industries, the increase in the unemployment rates is more significant, potentially indicating competition among different groups of migrants. This research provides insights for policymakers and stakeholders in Austria and other countries that may face the challenge of managing large-scale migration in the near future. Supplementary Information: The online version contains supplementary material available at 10.1186/s40878-024-00374-3.

A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients.

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D) presents considerable challenges. Accordingly, we present a personalized multitasking framework aimed to forecast blood glucose levels in patients. The patient data was initially categorized according to gender and age and subsequently utilized as input for a modified GRU network model, creating five prediction sub-models. The model hyperparameters were optimized and tuned after introducing the decay factor and incorporating the TCN network and attention mechanism into the GRU model. This step was undertaken to improve the capability of feature extraction. The Ohio T1DM clinical dataset was used to train and evaluate the performance of the proposed model. The metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Clark Error Grid Analysis (EGA), were used to evaluate the performance. The results showed that the average RMSE and the MAE of the proposed model were 16.896 and 9.978 mg/dL, respectively, over the prediction horizon (PH) of 30 minutes. The average RMSE and the MAE were 28.881 and 19.347 mg/dL, respectively, over the PH of 60 min. The proposed model demonstrated excellent prediction accuracy. In addition, the EGA analysis showed that the proposed model accurately predicted 30-minute and 60-minute PH within zones A and B, demonstrating that the framework is clinically feasible. The proposed personalized multitask prediction model in this study offers robust assistance for clinical decision-making, playing a pivotal role in improving the outcomes of individuals with diabetes.

Osband's principle for identification functions.

Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identification functions are key objects in forecast validation, statistical estimation and dynamic modelling. For a possibly vector-valued functional of interest, we fully characterise the class of (strict) identification functions subject to mild regularity conditions.

Adaptive reservoir operation considering water quantity and quality objectives: Application of parallel cellular automata and sub-seasonal streamflow forecasts.

This paper presents a new framework for the adaptive reservoir operation considering water quantity and quality objectives. In this framework, using the European Centre for Medium-Range Weather Forecasts (ECMWF) database, the monthly precipitation forecasts, with up to 6-month lead time, are downscaled and bias corrected. The rainfall forecasts are used as inputs to a rainfall-runoff simulation model to predict sub-seasonal inflows to reservoir. The water storage at the end of a short-term planning horizon (e.g. 6 months) is obtained from some probabilistic optimal reservoir storage volume curves, which are developed using a long-term reservoir operation optimization model. The adaptive optimization model is linked with the CE-QUAL-W2 water quality simulation model to assess the quality of outflow from each gate as well as the in-reservoir water quality. At the first of each month, the inflow forecasts for the coming months are updated and operating policies for each gate are revised. To tackle the computational burden of the adaptive simulation-optimization model, it is run using Parallel Cellular Automata with Local Search (PCA-LS) optimization algorithm. To evaluate the applicability and efficiency of the framework, it is applied to the Karkheh dam, which is the largest reservoir in Iran. By comparing the run times of the PCA-LS and the Non-dominated Sorting Genetic Algorithms II (NSGA-II), it is shown that the computational time of PCA-LS is 95 % less than NSGA-II. According to the results, the difference between the objective function of the proposed adaptive optimization model and a perfect model, which uses the observed inflow data, is only 1.68 %. It shows the appropriate accuracy of the adaptive model and justifies using the proposed framework for the adaptive operation of reservoirs considering water quantity and quality objectives.

Economic activity and suicides: Causal evidence from macroeconomic shocks in England and Wales.

The relationship between economic activity and suicides has been the subject of much scrutiny, but the focus in the extant literature has been almost exclusively on estimating associations rather than causal effects. In this paper, using data from England and Wales between January 1, 1997 and December 31, 2017, we propose a plausible set of assumptions to estimate the causal impacts of well-known macroeconomic variables on the daily suicide rate. Our identification strategy relies on scheduled macroeconomic announcements and professional economic forecasts. An important advantage of using these variables to model suicide rates is that they can efficiently capture the elements of 'surprise or shock' via the observed difference between how the economy actually performed and how it was expected to perform. Provided that professional forecasts are unbiased and efficient, the estimated 'surprises or shocks' are 'as good as random', and therefore are exogenous. We employ time series regressions and present robust evidence that these exogenous macroeconomic shocks affect the suicide rate. Overall, our results are consistent with economic theory that shocks that reduce estimated permanent income, and therefore expected lifetime utility, can propel suicide rates. Specifically, at the population level, negative shocks to consumer confidence and house prices accelerate the suicide rate. However, there is evidence of behavioural heterogeneity between sexes, states of the economy, and levels of public trust in government. Negative shocks to the retail price index (RPI) raise the suicide rate for males. Negative shocks to the growth rate in gross domestic product (GDP) raise the population suicide rate when the economy is doing poorly. When public trust in government is low, increases in the unemployment rate increase the suicide rate for females.

Climate Change Habitat Model Forecasts for Eight Owl Species in the Southwestern US.

The high-resolution forecasting of vegetation type shifts may prove essential in anticipating and mitigating the impacts of future climate change on bird populations. Here, we used the US Forest Service Ecological Response Unit (ERU) classification to develop and assess vegetation-based breeding habitat profiles for eight owl species occurring in the foothills and mountains of the Southwestern US. Shifts in mapped habitat were forecast using an ecosystem vulnerability model based on the pre-1990 climate envelopes of ERUs and the Intergovernmental Panel on Climate Change's (IPCC) A1B moderate-emission scenario for the future climate. For five of the eight owl species, the regional breeding habitat extent was projected to decline by at least 60% by 2090. Three species, the boreal owl (Aegolius funereus; at the trailing edge of its distribution), flammulated owl (Psiloscops flammeolus), and northern pygmy-owl (Glaucidium gnoma), were projected to experience the steepest habitat loss rates of 85%, 85%, and 76%, respectively. Projected vegetation shifts overlaid with well-documented flammulated owl breeding populations showed the complete or near complete loss of habitat by 2090 in areas of montane forest currently supporting dense aggregations of owl territories. Generalist or lower-elevation owl species were predicted to be less impacted, while, for the whiskered screech-owl (Megascops trichopsis), the contraction of the current habitat was nearly offset by a projected northward expansion. In general, the results of this study suggest high exposure to climate change impacts for the upper-elevation forest owls of semi-arid Southwestern North America. Long-distance migration and low natal philopatry may prove important to some montane owl populations in adapting to the regional loss of habitat.

Multipopulation mortality modelling and forecasting: the weighted multivariate functional principal component approaches.

Human mortality patterns and trajectories in closely related populations are likely linked together and share similarities. It is always desirable to model them simultaneously while taking their heterogeneity into account. This article introduces two new models for joint mortality modelling and forecasting multiple subpopulations using the multivariate functional principal component analysis techniques. The first model extends the independent functional data model to a multipopulation modelling setting. In the second one, we propose a novel multivariate functional principal component method for coherent modelling. Its design primarily fulfils the idea that when several subpopulation groups have similar socio-economic conditions or common biological characteristics such close connections are expected to evolve in a non-diverging fashion. We demonstrate the proposed methods by using sex-specific mortality data. Their forecast performances are further compared with several existing models, including the independent functional data model and the Product-Ratio model, through comparisons with mortality data of ten developed countries. The numerical examples show that the first proposed model maintains a comparable forecast ability with the existing methods. In contrast, the second proposed model outperforms the first model as well as the existing models in terms of forecast accuracy.

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.

Snow Cover Response to Climatological Factors at the Beas River Basin of W. Himalayas from MODIS and ERA5 Datasets.

Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management.

Real-time reservoir operation using inflow and irrigation demand forecasts in a reservoir-regulated river basin.

Real-time reservoir operation using inflow and irrigation demand forecasts can help reservoir system managers make effective water management decisions. Forecasting of inflow and irrigation demands is challenging, owing to the variability of the weather variables that affect inflows and irrigation demands. In this context, bias-corrected Global Forecasting System (GFS) forecasts are used here in a hybrid approach (reservoir module with Long Short Term Memory (LSTM)) to forecast the reservoir inflows. Concurrently, the bias-corrected GFS forecasts are used in irrigation demand module to forecast the irrigation demands. The 'Scaled Distribution Mapping' method is used to bias-correct the GFS data of 1-5 days lead. The study area is the Damodar river basin, India, consisting of five major reservoirs: Tenughat and Konar located upstream of Panchet, and Tilaya situated upstream of Maithon. With the upstream reservoir outflow forecasts, the inflows are forecasted in Panchet and Maithon reservoirs with NSE values of 0.88-0.96 and 0.78-0.88, respectively, up to a 5-day lead. The irrigation demand module with bias-corrected GFS forecasts forecasted the irrigation demands close to the irrigation demands with the observed weather data. The percentage errors in irrigation demand forecasts of the Kharif (June-October) season at 1-5 days lead are 9.45 %, -15.45 %, -20.52 %, -26.36 %, -27.31 %, respectively. On the contrary, percentage errors in irrigation demand forecasts of Rabi (November-February) and Boro (January-May) are in the range of 8.17-8.79 % and 3.48-8.06 %, respectively. With the inflows and irrigation demand forecasts, the Panchet and Maithon reservoirs satisfied the downstream demands and reduced the floods. The inflow and irrigation demand forecasts, based on the GFS forecasts, have substantial potential for real-time reservoir operation, leading to efficient water management downstream.

The effect of likelihood and impact information on public response to severe weather warnings.

Meteorological services are increasingly moving away from issuing weather warnings based on the exceedance of meteorological thresholds (e.g., windspeed), toward risk-based (or "impact-based") approaches. The UK Met Office's National Severe Weather Warning Service has been a pioneer of this approach, issuing yellow, amber, and red warnings based on an integrated evaluation of information about the likelihood of occurrence and potential impact severity. However, although this approach is inherently probabilistic, probabilistic information does not currently accompany public weather warning communications. In this study, we explored whether providing information about the likelihood and impact severity of forecast weather affected subjective judgments of likelihood, severity, concern, trust in forecast, and intention to take protective action. In a mixed-factorial online experiment, 550 UK residents from 2 regions with different weather profiles were randomly assigned to 1 of 3 Warning Format conditions (Color-only, Text, Risk Matrix) and presented with 3 warnings: high-probability/moderate-impact (amber HPMI); low-probability/high-impact (amber); high-probability/high-impact (red). Amongst those presented with information about probability and impact severity, red high-likelihood/high-impact warnings elicited the strongest ratings on all dependent variables, followed by amber HPMI warnings. Amber low-likelihood/high-impact warnings elicited the lowest perceived likelihood, severity, concern, trust, and intention to take protective responses. Taken together, this indicates that UK residents are sensitive to probabilistic information for amber warnings, and that communicating that severe events are unlikely to occur reduces perceived risk, trust in the warning, and behavioral intention, even though potential impacts could be severe. We discuss the practical implications of this for weather warning communication.

Human and Algorithmic Predictions in Geopolitical Forecasting: Quantifying Uncertainty in Hard-to-Quantify Domains.

Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon-nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms.

Regional economic costs of climate change: An interdisciplinary impact assessment for Upper Austria.

Region-specific meteorological data show that Upper Austria will mainly be affected by increasing temperatures (up to +2.7 °C in 2050) and decreasing precipitation (up to - 27 mm in 2050). Using an interdisciplinary framework, we derive climatic developments and quantify the resulting direct sectoral and macroeconomic impacts for Upper Austria. Based on a set of climate change indicators, sectoral damages are monetized for selected impact chains in forestry, health, agriculture, space heating and cooling, and winter tourism. These damage costs are used as input for ex-ante simulations to quantify the macroeconomic impacts in 2022-2050. The results show an annual decline in gross regional product, accompanied by an annual decline in employment. This study provides a basis for decision making in Upper Austria, as well as in regions with comparable geographical, economic or demographic structures, and highlights the importance of region-specific climate change adaptation strategies.

Local rainfall forecast knowledge across the globe used for agricultural decision-making.

The agriculture sector is vital to the world's economy and weather and climate are key drivers that affect the productivity and profitability of agricultural systems. At the same time, weather-related risks pose significant challenges to farmers' livelihoods. Although scientific weather forecast (SFK) is available, many farmers, especially in the Global South, have limited access to this information, and they rely on local forecast knowledge (LFK) to make farming decisions. Many studies also recognize the value of combining both forecasting systems; yet, unlike SFK which is readily available, indicators for LFK needs to be collected first. Therefore, this study identifies and documents the spatial distribution of LFK use for agriculture across the globe through a systematic literature review. Results show that a high number of LFK regions with a total of around 1350 local environmental indicators were found in Africa and Asia and less in South and North America. The low usability of scientific weather forecasts is perceived as the main reason farmers use LFK instead of SFK, yet the accessibility of LFK both for scientists and users, needs to be improved. Indicators based on animals and meteorology appeared to be more frequently used for weather predictions than plant- and astronomy-based indicators. Digitalizing the LFK inventory and collecting more detailed information about the regions where LFK was identified could promote and foster research on integrating scientific and local forecasting systems. This study will draw attention to the importance of LFK in weather forecasting, maintain this knowledge and enhance it.

Tendências temporais e projeções de cesariana no Brasil, macrorregiões administrativas e unidades federativas / Temporal trends and projections of caesarean sections in Brazil, its administrative macro-regions, and federative units

Resumo Os estudos de tendência sobre a via de nascimento no Brasil têm revelado um cenário de sucessivos aumentos lineares nas proporções de cesariana. Entretanto, a possibilidade de mudanças na evolução temporal da via cirúrgica não tem sido considerada. Dessa forma, objetivou-se verificar possíveis pontos de inflexão na proporção de cesarianas no Brasil, macrorregiões e unidades federativas, bem como estimar suas projeções para o ano de 2030. Utilizou-se a série temporal com as cesarianas notificadas no Departamento de Informática do SUS no período de 1994 a 2019. Foram utilizados modelos autorregressivos integrados de médias móveis e de regressão joinpoint para obtenção de projeções e de tendências das proporções de cesariana, respectivamente. As proporções de cesarianas apresentaram tendência significativa de aumento ao longo dos 26 anos de estudo em todos os níveis de agregação. Por outro lado, quando se considera a formação de segmentos, observa-se tendência de estabilização no país e nas regiões Sul e Centro-Oeste, a partir de 2012. Norte e Nordeste apresentaram tendência de aumento e o Sudeste, de queda significativa. Projeções indicam que no ano de 2030, 57,4% dos nascimentos no país ocorrerão por via cirúrgica e que nas regiões Sudeste e Sul, serão observadas proporções superiores a 70%.

Abstract Trend studies on the model of birth in Brazil show a scenario of successive linear increases in cesarean rates. However, they ignore possible changes in the temporal evolution of this delivery modality. Thus, this study aimed to evaluate possible inflection points in cesarean rates in Brazil, its macro-regions, and federated units, as well as to estimate projections for 2030. A time series with information on cesarean sections from 1994 to 2019 from the SUS Department of Informatics was used. Autoregressive integrated moving average and joinpoint regression models were used to obtain cesarean rate projections and trends, respectively. Caesarean rates showed a significant upward trend over the 26 study years at all levels of aggregation. On the other hand, when considering the formation of segments, a stabilization trend was observed both in the country and in the South and Midwest regions, starting in 2012. Rates tended to increase in North and Northeast and significantly decrease in Southeast. Projections show that in 2030, 57.4% of births in Brazil will be cesarean, with rates higher than 70% in Southeast and South regions.