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This study aims to predict the potential for secondary lead recovery from motorcycle batteries in Brazil, since this is considered the second largest category of automobiles in the country. To achieve this objective, a forecasting model based on the ARIMA methodology was applied, with input data taken from Brazilian sectorial platforms. Furthermore, an analysis of the data, of the residuals, autocorrelation tests, as well as Kolmogorov-Smirnov and Dickey-Fuller tests, were performed. The SARIMA model (3,1,0) (2,0,0)12 presented a better adaptation to the behavior of the series. The results showed that the amount of secondary lead obtained based on the forecast model will be 89,972,842.08 million tons between 2021 and 2030 (14 million tons of lead originated only from motorcycle LABs in 2021). These results show a possible insufficiency of the installed capacity to supply the amount of lead to be processed in the country, not to mention the LABs from other vehicles (light and heavy) and other emerging battery technologies from electric vehicles. In addition, an analysis was conducted on the importance of secondary lead for the economy and the dangers of illegal recycling in Brazil. In general, this study contributes to the understanding of the importance of secondary production of lead in Brazil, an important asset for a country that does not have sufficient primary production for its domestic demand. The findings may assist in several alternatives for the proper planning and management of the collection, disposal and recycling of lead, providing the Brazilian government with directions for the development of new policies related to lead recycling.

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Since the introduction of Universal Varicella Vaccination (UVV) in the Argentinean National Immunization Program in 2015, a significant decline in the incidence of varicella has been reported. This study aimed to estimate the economic impact of single-dose UVV in Argentina from 2015 to 2019. The economic impact was assessed based on the observed incidence of varicella in the post-UVV period and the number of cases avoided, obtained from a previously published study that used an Autoregressive Integrated Moving Average (ARIMA) model. The weighted average cost per case was calculated using local studies. The post-UVV cost reductions were calculated by multiplying the number of cases avoided from 2015 -2019 by the weighted average cost per case. Data were summarized yearly and by peak (September-November) periods for the target (1-4 years) and overall populations. We estimated avoided costs of United States dollars (USD) $65 million in the target population and $112 million in the overall population over 4 years following UVV introduction. We observed a trend toward greater reductions in costs over time, with substantial differences observed in peak periods. We estimated that the single-dose UVV program considerably reduced the economic burden of varicella in Argentina by avoiding direct and indirect costs associated with varicella management.

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In the livestock sector, strategies are available to mitigate gas emissions, such as methane, one of the alternatives that have shown potential correspondence to changes in the composition of the diet. The main aim of this study was to analyze the influence of methane emissions with data on enteric fermentation obtained from the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database and based on forecasts of methane emissions by enteric fermentation with an autoregressive integrated moving average (ARIMA) model and the application of statistical tests to identify the association between methane emissions from enteric fermentation and the variables of the chemical composition and nutritional value of forage resources in Colombia. The results reported positive correlations between methane emissions and the variables ash content, ethereal extract, neutral detergent fiber (NDF), and acid detergent fiber (ADF) and negative correlations between methane emissions and the variables percentage of unstructured carbohydrates, total digestible nutrients (TDN), digestibility of dry matter, metabolizable energy (MERuminants), net maintenance energy (NEm), net energy gain (NEg), and net lactation energy (NEI). The variables with the most significant influence on the reduction of methane emissions by enteric fermentation are the percentage of unstructured carbohydrates and the percentage of starch. In conclusion, the analysis of variance and the correlations between the chemical composition and the nutritive value of forage resources in Colombia help to understand the influence of diet variables on methane emissions of a particular family and with it in the application of strategies of mitigation.

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ABSTRACT: The study predicted chicken meat production in 2019-2025 period for the leading chicken-producing countries with the help of the 1961-2018 Food and Agriculture Organization (FAO) data since chicken meat consumption is so high worldwide. The USA ranks the first place while Brazil and China take second and third places, respectively. The analysis of the pioneer chicken meat-producing countries indicates that while the portion of the USA in world production decreases, the share, particularly Brazil and China, will approach that of the USA. World chicken meat production, which was 7.56 million tons in 1961, will increase to 139.19 million tons in 2025, and this production per capita is predicted to increase to 17.0 kg in 2025 from 2.4, 5.35, 9.80, and 15.0 kg in 1961, 1981, 2001, and 2018, respectively. Indonesia, Russia, Brazil, Japan, and India will run the highest increases in production. However, the share of countries in chicken meat production will decrease from 61% to 60% in 2019-2025 compared to the 2012-2018 periods. This condition showed that apart from some leading countries, the production will keep a rapid increase in production. The increase in chicken meat production and chicken meat import worldwide will improve human nutrition, especially in developing and underdeveloped countries. Countries that run cost advantages and high-quality life standards in line with technological innovations produce processed chicken products, strengthen animal health, hygiene, and transportation standards, and attach importance to advertising activities that increase consumer demand will be more advantageous in this market.

RESUMO: O estudo visa prever a produção de carne de frango no período de 2019-2025 para os principais países produtores de frango com a ajuda dos dados da Organização para Agricultura e Alimentação (FAO) de 1961-2018, uma vez que o consumo de carne de frango é tão alto emtodo o mundo. Os EUA ocupam o primeiro lugar, enquanto o Brasil e a China ficam com o segundo e o terceiro lugares, respectivamente. A análise dos países pioneiros na produção de carne de frango indica que enquanto a participação dos EUA na produção mundial diminui a participação, principalmente do Brasil e da China, se aproximará da dos EUA. A produção mundial de carne de frango, que era de 7,56 milhões de tonelada sem 1961, aumentará para 139,19 milhões de tonelada sem 2025, e esta produção per capita deve aumentar para 17,0 kg em 2025 de 2,4, 5,35, 9,80 e 15,0 kg em 1961, 1981, 2001 e 2018, respectivamente. Indonésia, Rússia, Brasil, Japão e Índia terão os maiores aumentos de produção. No entanto, a participação dos países na produção de carne de frango cairá de 61% para 60% em 2019-2025 em relação ao período de 2012-2018. Essa condição mostra que, além de alguns países líderes, a produção manterá um rápido aumento da produção. O aumento na produção e importação de carne de frango em todo o mundo melhorará a nutrição humana, especialmente nos países em desenvolvimento e subdesenvolvidos. Os países que possuem vantagens de custo e padrões de vida de alta qualidade alinhados às inovações tecnológicas produzem produtos processados de frango, fortalecemos padrões de saúde, higiene e transporte animal e atribuem importância a atividades de publicidade que aumentem a demanda do consumidor serão mais vantajosas neste mercado.

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The study predicted chicken meat production in 2019-2025 period for the leading chicken-producing countries with the help of the 1961-2018 Food and Agriculture Organization (FAO) data since chicken meat consumption is so high worldwide. The USA ranks the first place while Brazil and China take second and third places, respectively. The analysis of the pioneer chicken meat-producing countries indicates that while the portion of the USA in world production decreases, the share, particularly Brazil and China, will approach that of the USA. World chicken meat production, which was 7.56 million tons in 1961, will increase to 139.19 million tons in 2025, and this production per capita is predicted to increase to 17.0 kg in 2025 from 2.4, 5.35, 9.80, and 15.0 kg in 1961, 1981, 2001, and 2018, respectively. Indonesia, Russia, Brazil, Japan, and India will run the highest increases in production. However, the share of countries in chicken meat production will decrease from 61% to 60% in 2019-2025 compared to the 2012-2018 periods. This condition showed that apart from some leading countries, the production will keep a rapid increase in production. The increase in chicken meat production and chicken meat import worldwide will improve human nutrition, especially in developing and underdeveloped countries. Countries that run cost advantages and high-quality life standards in line with technological innovations produce processed chicken products, strengthen animal health, hygiene, and transportation standards, and attach importance to advertising activities that increase consumer demand will be more advantageous in this market.

O estudo visa prever a produção de carne de frango no período de 2019-2025 para os principais países produtores de frango com a ajuda dos dados da Organização para Agricultura e Alimentação (FAO) de 1961-2018, uma vez que o consumo de carne de frango é tão alto emtodo o mundo. Os EUA ocupam o primeiro lugar, enquanto o Brasil e a China ficam com o segundo e o terceiro lugares, respectivamente. A análise dos países pioneiros na produção de carne de frango indica que enquanto a participação dos EUA na produção mundial diminui a participação, principalmente do Brasil e da China, se aproximará da dos EUA. A produção mundial de carne de frango, que era de 7,56 milhões de tonelada sem 1961, aumentará para 139,19 milhões de tonelada sem 2025, e esta produção per capita deve aumentar para 17,0 kg em 2025 de 2,4, 5,35, 9,80 e 15,0 kg em 1961, 1981, 2001 e 2018, respectivamente. Indonésia, Rússia, Brasil, Japão e Índia terão os maiores aumentos de produção. No entanto, a participação dos países na produção de carne de frango cairá de 61% para 60% em 2019-2025 em relação ao período de 2012-2018. Essa condição mostra que, além de alguns países líderes, a produção manterá um rápido aumento da produção. O aumento na produção e importação de carne de frango em todo o mundo melhorará a nutrição humana, especialmente nos países em desenvolvimento e subdesenvolvidos. Os países que possuem vantagens de custo e padrões de vida de alta qualidade alinhados às inovações tecnológicas produzem produtos processados de frango, fortalecemos padrões de saúde, higiene e transporte animal e atribuem importância a atividades de publicidade que aumentem a demanda do consumidor serão mais vantajosas neste mercado.

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In 2015, one-dose universal varicella vaccination (UVV) was introduced in the Colombian National Immunization Program targeting children aged 12 months, expanding to a two-dose program in 2019. This study aimed to examine the effect of one-dose UVV on the burden of varicella in Colombia. A retrospective study was conducted using national databases to estimate incidence and mortality for the target (1-4 years old), non-target (less than 1 and 5 years and older) and overall (all age groups) populations from the pre-UVV period (January 2008-June 2015) to the post-UVV period (July 2015-December 2019). A time-series analyses with ARIMA modeling was used to project expected varicella incidence and mortality in the absence of UVV in the post-UVV period. UVV impact was estimated by comparing predicted and observed values, providing point estimates and prediction intervals (PI). Overall vaccination coverage rate was over 90 % from 2016-2019. Following UVV introduction, mean annual incidence rates reduced from 743.6 to 676.8 per 100,000 in the target population and from 203.2 to 198.1 per 100,000 in the overall population. Our study estimated a reduction in varicella incidence from 2017, with the highest reduction of 70.5 % (95 % PI: 78.2-54.2) and 54.8 % (95 % PI: 65.0-36.4) observed in 2019 for the target and the overall populations, respectively. The ARIMA model estimated UVV in Colombia to have prevented 198,236 varicella cases from 2015 to 2019. Mortality reduced in the overall population from 0.8 per 1,000,000 to 0.5 per 1,000,000 and from 1.3 per 1,000,000 to 0.5 per 1,000,000 in the target population, in the pre-UVV and post-UVV periods, respectively. However, these differences were not statistically significant. Our study showed a significant reduction in varicella incidence after implementation of a one-dose UVV program in Colombia, increasing over time. Further assessment is needed to evaluate the impact of a two-dose UVV program in Colombia.

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One-dose universal varicella vaccination (UVV) was introduced in the Argentinian National Immunization Program in July 2015. This study examined the impact of one-dose UVV on varicella incidence and mortality in Argentina. Incidence and mortality data were obtained from official databases for pre-UVV (January 2008-June 2015) and post-UVV (July 2015-December 2019) periods. Time series analyses with autoregressive integrated moving average (ARIMA) modeling predicted varicella incidence and mortality in absence of UVV in the target (aged 1-4 years) and overall population. Predicted and observed values post-UVV were compared to estimate UVV impact. Mean annual incidence rates per 100,000 reduced from 1999 (pre-UVV) to 1122 (post-UVV) in the target population and from 178 to 154 in the overall population. Significant declines in incidence were observed, reaching reductions of 83.9% (95% prediction interval [PI]: 58.9, 90.0) and 69.1% (95% PI: 23.6, 80.7) in the target and overall populations, respectively, during peak months (September-November) post-UVV. Decreasing trends in mortality rate from 0.4 to 0.2 per 1,000,000 population were observed. Over the last four years, one-dose UVV has significantly reduced varicella burden of disease in Argentina. Continuous efforts to improve vaccination coverage rates and long-term follow-up are needed to better understand the benefits of the UVV program.

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The use of models to predict disease cases is common in epidemiology and related areas, in the context of Covid-19, both ARIMA and Neural Network models can be applied for purposes of optimized resource management, so the aim of this study is to capture the linear and non-linear structures of daily Covid-19 cases in the world by using a hybrid forecasting model. In summary, the proposed hybrid system methodology consists of two steps. In the first step, an ARIMA model is used to analyze the linear part of the problem. In the second step, a neural network model is developed to model the residuals of the ARIMA model, which would be the non-linear part of it. The neural network model was superior to the ARIMA when considering the capture of weekly seasonality and in two weeks, the combination of models with the capture of seasonality in two weeks provided a mixed model with good error metrics, that allows actions to be premeditated with greater certainty, such as increasing the number of nurses in a location, or the acceleration of vaccination campaigns to diminish a possible increase in the number of cases.

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BACKGROUND: This study aimed to determine whether crime patterns in Mexico City changed due to the COVID-19 pandemic, and to test whether any changes observed were associated with the disruption of routine activities, as measured by changes in public transport passenger numbers. METHOD: The first objective was assessed by comparing the observed incidence of crime after the COVID-19 pandemic was detected in the country with that expected based on ARIMA forecasts based on the pre-pandemic trends. The second objective was assessed by examining the association between crime incidence and the number of passengers on public transport using regressions with ARIMA errors. RESULTS: Results indicated that most crime categories decreased significantly after the pandemic was detected in the country or after a national lockdown was instituted. Furthermore, the study found that some of the declines observed were associated with the reductions seen in public transport passenger numbers. However, the findings suggested that the changes in mobility explain part of the declines observed, with important variations per crime type. CONCLUSION: The findings contribute to the global evaluation of the effects of COVID-19 on crime and propose a robust method to explicitly test whether the changes observed are associated with changes in routine activities.

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COVID-19 pandemic has reshaped our world in a timescale much shorter than what we can understand. Particularities of SARS-CoV-2, such as its persistence in surfaces and the lack of a curative treatment or vaccine against COVID-19, have pushed authorities to apply restrictive policies to control its spreading. As data drove most of the decisions made in this global contingency, their quality is a critical variable for decision-making actors, and therefore should be carefully curated. In this work, we analyze the sources of error in typically reported epidemiological variables and usual tests used for diagnosis, and their impact on our understanding of COVID-19 spreading dynamics. We address the existence of different delays in the report of new cases, induced by the incubation time of the virus and testing-diagnosis time gaps, and other error sources related to the sensitivity/specificity of the tests used to diagnose COVID-19. Using a statistically-based algorithm, we perform a temporal reclassification of cases to avoid delay-induced errors, building up new epidemiologic curves centered in the day where the contagion effectively occurred. We also statistically enhance the robustness behind the discharge/recovery clinical criteria in the absence of a direct test, which is typically the case of non-first world countries, where the limited testing capabilities are fully dedicated to the evaluation of new cases. Finally, we applied our methodology to assess the evolution of the pandemic in Chile through the Effective Reproduction Number Rt , identifying different moments in which data was misleading governmental actions. In doing so, we aim to raise public awareness of the need for proper data reporting and processing protocols for epidemiological modelling and predictions.

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BACKGROUND AND AIMS: In a little over six months, the Corona virus epidemic has affected over ten million and killed over half a million people worldwide as on June 30, 2020. With no vaccine in sight, the spread of the virus is likely to continue unabated. This article aims to analyze the time series data for top five countries affected by the COVID-19 for forecasting the spread of the epidemic. MATERIAL AND METHODS: Daily time series data from 15th February to June 30, 2020 of total infected cases from the top five countries namely US, Brazil, India, Russia and Spain were collected from the online database. ARIMA model specifications were estimated using Hannan and Rissanen algorithm. Out of sample forecast for the next 77 days was computed using the ARIMA models. RESULTS: Forecast for the first 18 days of July was compared with the actual data and the forecast accuracy was using MAD and MAPE were found within acceptable agreement. The graphic plots of forecast data suggest that While Russia and Spain have reached the inflexion point in the spread of epidemic, the US, Brazil and India are still experiencing an exponential curve. CONCLUSION: Our analysis shows that India and Brazil will hit 1.38 million and 2.47 million mark while the US will reach the 4.29 million mark by 31st July. With no effective cure available at the moment, this forecast will help the governments to be better prepared to combat the epidemic by ramping up their healthcare facilities.

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El objetivo principal de este trabajo es emplear modelos ARIMA para la estimación de nuevos contagios usando datos públicos disponibles para Venezuela y la región suramericana, actualmente foco principal de un segundo brote de la COVID-19. Se realiza la predicción a 30 días del número de casos de Covid-19 en países suramericanos usando los datos públicos disponibles. Se emplearon modelos ARIMA para estimar el impacto de nuevos contagios en las dinámicas de infección para Suramérica. Desde la aparición del primer caso de la nueva neumonía Covid-19 en China, esta enfermedad se ha convertido en un problema de salud pública global y representa un gran reto el control de la infección para los países de Suramérica. Al 24 de junio de 2020 un total de 1.866.090 casos han sido detectados en la región y en el caso particular de Venezuela un total de 4.365 casos. El rápido incremento en el número de casos y la alta tasa de contagios asociado con el virus han llevado al desarrollo de distintas aproximaciones matemáticas, tales como: modelos SIR, SEIR, redes neuronales y regresiones lineales que permitan predecir la probable evolución de la epidemia. Los modelos ARIMA han sido empleados con éxito en otras infecciones como influenza, malaria, SARS, entre otras. Los resultados de las estimaciones realizadas empleando estos modelos muestran que aún en la región hacen falta mayores esfuerzos que conlleven al control de la epidemia(AU)

The main objective of this work is to use ARIMA models for the estimation of new contagions using public data available for Venezuela and the South American region, currently the main focus of a second COVID19 outbreak. A 30-day prediction is made for the num-ber of Covid-19 cases in South American countries using available public data. ARIMA models were used to estimate the impact of new contagions on infection dynamics for South America Since the appearance of the first case of the new Covid-19 pneumonia in China, which has become a global public health problem and the great challenge that the infection has represented for the countries of South America to June 24, 2020, a total of 1,866,090 cases have been detected and in the particular case of Venezuela a total of 4,365 cases have been detected for the same date. The rapid increase in the number of cases and the high rate of contagion associated with the virus have led to the development of different mathematical approaches, such as: SIR, SEIR models, neural networks and linear regressions that allow predicting the probable evolution of the epidemic. The ARIMA model has been successfully used in other infections such as influenza, malaria, SARS, among others. In the following work, the 30 - day prediction of the number of Covid-19 cases in South American countries is made using public data available. The results of the estimates made using these models show that even in the region, greater efforts are needed to control the epidemic(AU)

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The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87%-3.51%, 1.02%-5.63%, and 0.95%-6.90% in one, three, and six-days-ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.

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Equine infectious anemia (EIA) is a viral infectious disease that affects Equidae and is clinically characterized by intermittent fever, anemia, depression, emaciation, and edema. To evaluate disease dynamics in the state of Tocantins, Brazil, a time series of EIA cases in the period 2007–2019 was analyzed to describe the pattern of occurrence and define the autoregressive integrated by moving average (ARIMA) model best suited to make predictions of cases of this disease for the period 2020–2021. The modeling and statistical analysis of the time series were performed using R software. The ARIMA model (2,1,1) was evaluated by Holdout cross-validation, in which data from the periods 2007–2017 and 2018–2019 were used as training and test data, respectively. The analyses showed that EIA was endemic and non-seasonal in Tocantins. The ARIMA model (2,1,1) showed good predictive capacity adjusted for this time series. However, the prediction of 276 cases of EIA in Tocantins for the period 2020–2021 may vary depending on the demand for diagnostic tests for Equidae transportation and herd sanitation in farms considered infection foci. The ARIMA model helps predict the number of EIA cases in Tocantins and improves planning for disease control by the Official Veterinary Service.

A anemia infecciosa equina (AIE), doença infecciosa viral que acomete os equídeos, é caracterizada clinicamente por causar febre intermitente, anemia, depressão, emaciação e edema. Com o objetivo de elucidar a dinâmica dessa doença no estado do Tocantins, foi realizada a análise da série temporal dos casos de AIE em equídeos entre 2007 e 2019 para descrever o padrão de sua ocorrência, além de definir o modelo autorregressivo integrado por média móvel (Autoregressive Integrated by Moving Average - ARIMA) mais adequado para se realizar previsões dos casos dessa doença para os anos de2020 e 2021. A modelagem e análise estatística da série temporal em estudo foi realizada por meio do software R. O modelo preditivo ARIMA (2,1,1) foi avaliado por meio da validação cruzada utilizando a técnica de Holdout, em que os dados de 2007 a 2017 foram utilizados como treino e os dados de 2018 e2019 foram utilizados como teste. As análises mostraram que a AIE é endêmica no estado do Tocantins e sem padrão de sazonalidade. O modelo ARIMA (2,1,1) apresentou boa capacidade preditiva ajustada para a série temporal em estudo. Porém, a previsão aproximada de 276 casos de AIE em equídeos para os anos de 2020 e 2021 no estado do Tocantins pode variar em decorrência da demanda por exames dessa doença para o trânsito dos equídeos, bem como do saneamento de propriedades consideradas foco. A modelagem ARIMA pode ser utilizada na previsão dos casos de AIE em equídeos no estado do Tocantins o que permite melhorar o planejamento para a execução das ações de controle dessa doença por parte do Serviço Veterinário Oficial.

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Equine infectious anemia (EIA) is a viral infectious disease that affects Equidae and is clinically characterized by intermittent fever, anemia, depression, emaciation, and edema. To evaluate disease dynamics in the state of Tocantins, Brazil, a time series of EIA cases in the period 2007–2019 was analyzed to describe the pattern of occurrence and define the autoregressive integrated by moving average (ARIMA) model best suited to make predictions of cases of this disease for the period 2020–2021. The modeling and statistical analysis of the time series were performed using R software. The ARIMA model (2,1,1) was evaluated by Holdout cross-validation, in which data from the periods 2007–2017 and 2018–2019 were used as training and test data, respectively. The analyses showed that EIA was endemic and non-seasonal in Tocantins. The ARIMA model (2,1,1) showed good predictive capacity adjusted for this time series. However, the prediction of 276 cases of EIA in Tocantins for the period 2020–2021 may vary depending on the demand for diagnostic tests for Equidae transportation and herd sanitation in farms considered infection foci. The ARIMA model helps predict the number of EIA cases in Tocantins and improves planning for disease control by the Official Veterinary Service.(AU)

A anemia infecciosa equina (AIE), doença infecciosa viral que acomete os equídeos, é caracterizada clinicamente por causar febre intermitente, anemia, depressão, emaciação e edema. Com o objetivo de elucidar a dinâmica dessa doença no estado do Tocantins, foi realizada a análise da série temporal dos casos de AIE em equídeos entre 2007 e 2019 para descrever o padrão de sua ocorrência, além de definir o modelo autorregressivo integrado por média móvel (Autoregressive Integrated by Moving Average - ARIMA) mais adequado para se realizar previsões dos casos dessa doença para os anos de2020 e 2021. A modelagem e análise estatística da série temporal em estudo foi realizada por meio do software R. O modelo preditivo ARIMA (2,1,1) foi avaliado por meio da validação cruzada utilizando a técnica de Holdout, em que os dados de 2007 a 2017 foram utilizados como treino e os dados de 2018 e2019 foram utilizados como teste. As análises mostraram que a AIE é endêmica no estado do Tocantins e sem padrão de sazonalidade. O modelo ARIMA (2,1,1) apresentou boa capacidade preditiva ajustada para a série temporal em estudo. Porém, a previsão aproximada de 276 casos de AIE em equídeos para os anos de 2020 e 2021 no estado do Tocantins pode variar em decorrência da demanda por exames dessa doença para o trânsito dos equídeos, bem como do saneamento de propriedades consideradas foco. A modelagem ARIMA pode ser utilizada na previsão dos casos de AIE em equídeos no estado do Tocantins o que permite melhorar o planejamento para a execução das ações de controle dessa doença por parte do Serviço Veterinário Oficial.(AU)

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In 2008 Brazil enacted Law n° 11.705, known as the Lei Seca (in Portuguese) or Dry Law, altering the National Traffic Code by establishing zero tolerance for the presence of alcohol in drivers' bloodstreams and toughening punishment for offenders. In 2012 the New Dry Law, Law n° 12.760 came into force in an effort to correct for legal loopholes in the earlier version and make it feasible to produce alternative forms of proof of alcohol impediment against those drivers who refused to take the breath analysis test. Sanctions for offenders were made even more severe. Ten years after the advent of the first Lei Seca this study set out to make a quantitative assessment of the two laws' impacts regarding the reduction of lethal traffic accidents in the Federal District, Brazil. Intervention Analysis of Time Series was the technique used and transfer functions enabled the incorporation of the effects of dummy exogenous variables to the Box and Jenkins ARIMA model. Results showed that while Law n° 11.705 had no significant impact, Law 12.760 did have a statistically significant impact in reducing lethal accidents. Such results underscore the need for ex post monitoring and evaluation of Laws and confirm the premise that legislation only successfully produces its effects when compliance can be enforced.

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BACKGROUND: Brazil is the largest producer of coffee in the world. Studies on climate change estimate large impacts on the production of Coffea arabica (C. arabica). In this context, it is necessary to know the quantitative production values to provide evidence for policy makers to target the prompt answer. RESULTS: Using data from 18 municipalities located in five Brazilian states that produce more coffee in Brazil, in an unprecedented way, in this work it is shown that although the minimum temperature is the most important climatic variable for the production, its effect, although positive, and its degree of explanation, were technically too small to explain the volume of production in Brazilian conditions. According to the model of non-stationary time series ARIMA (1, 1, 0) coffee production in the future may reach almost four million tons, and the productivity almost 2500 kg ha-1 on average, with the advancement of technology as the main factor that should promote simultaneous increases in production and productivity. However, despite natural climate variations, which make it the most responsible for the variability of annual coffee production, the producer must increase the use of the technologies to support the Brazilian coffee agribusiness. CONCLUSIONS: The results of this study reveal that coffee production in Brazil is due much more to productivity than to the minimum ambient temperature change over the long term; despite this, the climate variable should be considered the most influential on the production and productivity of coffee. © 2017 Embrapa. Journal of the Science of Food and Agriculture © 2019 Society of Chemical Industry.

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The aim of the study was to evaluate the temporal patterns of dengue incidence from 2001 to 2014 and forecast for 2015 in two Brazilian cities. We analysed dengue surveillance data (SINAN) from Recife, 1.6 million population, and Goiania, 1.4 million population. We used Auto-Regressive Integrated Moving Average (ARIMA) modelling of monthly notified dengue incidence (2001-2014). Forecasting models (95% prediction interval) were developed to predict numbers of dengue cases for 2015. During the study period, 73,479 dengue cases were reported in Recife varying from 11 cases/100,000 inhab (2004) to 2418 cases/100,000 inhab (2002). In Goiania, 253,008 dengue cases were reported and the yearly incidence varied from 293 cases/100,000 inhab (2004) to 3927 cases/100,000 inhab (2013). Trend was the most important component for Recife, while seasonality was the most important one in Goiania. For Recife, the best fitted model was ARIMA (1,1,3)12 and for Goiania Seasonal ARIMA (1,0,2) (1,1,2)12. The model predicted 4254 dengue cases for Recife in 2015; SINAN registered 35,724 cases. For Goiania the model predicted 33,757 cases for 2015; the reported number of cases by SINAN was 74,095, within the 95% prediction interval. The difference between notified and forecasted dengue cases in Recife can be explained by the co-circulation of dengue and Zika virus in 2015. In this year, all cases with rash were notified as "dengue-like" illness. The ARIMA models may be considered a baseline for the time series analysis of dengue incidence before the Zika epidemic.

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RESUMO

BACKGROUND AND AIMS: In December 2006 the United States regulated sodium permanganate, a cocaine essential chemical. In March 2007 Mexico, the United States' primary source for methamphetamine, closed a chemical company accused of illicitly importing 60+ tons of pseudoephedrine, a methamphetamine precursor chemical. US cocaine availability and methamphetamine availability, respectively, decreased in association. This study tested whether the controls had impacts upon the numbers of US cocaine users and methamphetamine users. DESIGN: Auto-regressive integrated moving average (ARIMA) intervention time-series analysis. Comparison series-heroin and marijuana users-were used. SETTING: United States, 2002-14. PARTICIPANTS: The National Survey on Drug Use and Health (n = 723 283), a complex sample survey of the US civilian, non-institutionalized population. MEASUREMENTS: Estimates of the numbers of (1) past-year users and (2) past-month users were constructed for each calendar quarter from 2002 to 2014, providing each series with 52 time-periods. FINDINGS: Downward shifts in cocaine users started at the time of the cocaine regulation. Past-year and past-month cocaine users series levels decreased by approximately 1 946 271 (-32%) (P < 0.05) and 694 770 (-29%) (P < 0.01), respectively-no apparent recovery occurred through 2014. Downward shifts in methamphetamine users started at the time of the chemical company closure. Past-year and past-month methamphetamine series levels decreased by 494 440 (-35%) [P < 0.01; 95% confidence interval (CI) = -771 897, -216 982] and 277 380 (-45%) (P < 0.05; CI = -554 073, -686), respectively-partial recovery possibly occurred in 2013. The comparison series changed little at the intervention times. CONCLUSIONS: Essential/precursor chemical controls in the United States (2006) and Mexico (2007) were associated with large, extended (7+ years) reductions in cocaine users and methamphetamine users in the United States.

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