A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil

INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.

The feasibility of forecasting influenza epidemics in Cuba

A large influenza epidemic took place in Havana during the winter of 1988. The epidemiologic surveillance unit of the Pedro Kouri Institute of Tropical Medicine detected the begining of the epidemic wave. The Rvachev-Baroyan mathematical model of the geographic spread of an epidemic was used to forecast this epidemic under routine conditions of the public health system. The expected number of individuals who would attend outpatient services, because of influenza-like illness, was calculated and communicated to the health authorities within enough time to permit the introduction of available control measures. The approximate date of the epidemic peak, the daily expected number of individuals attending medical services, and the approximate time of the end of the epidemic wave were estimated. The prediction error was 12%. The model was sufficienty accurate to warrant its use as a pratical forecasting tool in the Cuban public health system.

The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix (Lepidoptera, Arctiidae)

Species' geographic ranges are usually considered as basic units in macroecology and biogeography, yet it is still difficult to measure them accurately for many reasons. About 20 years ago, researchers started using local data on species' occurrences to estimate broad scale ranges, thereby establishing the niche modeling approach. However, there are still many problems in model evaluation and application, and one of the solutions is to find a consensus solution among models derived from different mathematical and statistical models for niche modeling, climatic projections and variable combination, all of which are sources of uncertainty during niche modeling. In this paper, we discuss this approach of ensemble forecasting and propose that it can be divided into three phases with increasing levels of complexity. Phase I is the simple combination of maps to achieve a consensual and hopefully conservative solution. In Phase II, differences among the maps used are described by multivariate analyses, and Phase III consists of the quantitative evaluation of the relative magnitude of uncertainties from different sources and their mapping. To illustrate these developments, we analyzed the occurrence data of the tiger moth, Utetheisa ornatrix (Lepidoptera, Arctiidae), a Neotropical moth species, and modeled its geographic range in current and future climates.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

MÉTODOS COLABORATIVOS NA GESTÃO DE CADEIAS DE SUPRIMENTOS: DESAFIOS DE IMPLEMENTAÇÃO

RESUMOO tema gestão colaborativa vem sendo abordado como forma de gerar vantagem competitiva. Iniciativas colaborativas em cadeias de suprimentos (CS) proporcionaram o surgimento de métodos e práticas que corroboram a ideia de que a competitividade não se dá mais entre empresas isoladas, e sim entre cadeias. Na literatura, além de métodos colaborativos para a gestão da cadeia de suprimentos (GCS), é possível encontrar iniciativas para projeto, configuração, otimização, entre outras. Porém, observa-se baixa adesão de métodos colaborativos para a GCS, assim como dificuldades de implementação. O objetivo do artigo é investigar os métodos colaborativos referentes à coordenação de CS e analisar suas características e dificuldades de implementação. Uma revisão sistemática apontou o Collaborative Planning, Forecasting and Replenishment (CPFR) como o método colaborativo mais abordado e, posteriormente, um estudo de caso identificou dificuldades na implementação que colaboraram com o que foi pesquisado na literatura.

Smoking, consumption of alcohol and sedentary life style in population grouping and their relationships with lipemic disorders

The study, part of the project "Atherosclerotic cardiovascular diseases, lipemic disorders, hypertension, obesity and diabetis mellitus in a population of the metropolitan area of the southeastern region of Brazil", had the following objectives: a) the characterization and distribution among typical human socio-economic groupings, of the prevalence of some particular habits which constitute aspects of life-style-the use of tobacco, the use of alcohol and sedentary activity; b) the establishment of the interrelation between the above-mentioned habits and some lipemic disorders. The prevalence of the habits cited behaved in the following manner: the use of tobacco predominated among men, distributed uniformly throughout the social strata; among the women the average percentage of smokers was 18,9%, a significant difference occurring among the highest socio-economic class, where the average was of 40.2%. The sedentary style of life presented high prevalence, among both men and women with exception of the women of the highest socio-economic level and of the skilled working class. The use of alcohol, as one would expect, is a habit basically practised by the men, without any statistically significant differences between classes. For the purpose of establishing associations between these risk fictors and lipemic conditions four situations were chosen, of the following characteristics: 1- total cholesterol > or = 220 mg/dl and triglycerides > or = 150 mg/dl; 2- HDL cholesterol <35 mg/dl for men and <45 mg/dl for women and triglycerides levels > or = 150 mg/dl; 3- HDL cholesterol <35 mg/dl for men and <45 mg/dl for women and triglycerides levels <150 mg/dl; 4- total cholesterol 220 mg/dl with triglycerides levels <150 mg/dl. Six models of multiple (backward) regression were established, with seven independent variables- age, sex, use of tobacco, consumption of alcohol, light physical activity, hypertension and obesity. Significant associations (P<0,05) were revealed with hypercholesterolemia, accompanied by triglyceride levels > or = 150 mg/dl, and the following independent variables: age, use of tobacco and the interactions between obesity and smoking, age and sedentary lifestyle, sex and obesity (R2=22%); the standardized B coefficient showed that the variables with the greatest weight in the forecasting of the variation in the levels of cholesterol were smoking and the interaction between obesity and smoking. The hypercholesterolemia accompanied by triglycerides levels <150 mg/dl showed a positive association between total cholesterol and sex and the interactions obesity/smoking and sex/obesity. As regards HDL cholesterol accompanied by triglyceride/ levels > or = 150 mg/dl was inversely associated with obesity and the interaction smoking/ age and directly with age (R=31%). The standardized B coeffients, indicated that the variables obesity and the interactions smoking/age possessed a weight three times greater than age alone in accounting for the variation in the serum levels of HDL cholesterol. When accompanied by triglycerides <150 mg/dl there was no association between and the independent variables and the set of them presented R equal to 22%. The sum of top, in the population stutied in this project, the component habits of life-style (smoking, alcohol consumption and sedentary activity) which constitute risk factors which determine morbidity from atherosclerotic cardiovascular diseases are be found distributed through all the typical social groupings of this particular form of social organization. On the other hand, the seven independent variables used in the multiple regression models for the explanation of the lipemic conditions considered presented multiple determination coefficients which varied, approximately, between 20% and 30%. Thus it is important that in the genetic epidemiology the study of the morbidities in question be emphasized.

An Equation for the Prediction of Oxygen Consumption in a Brazilian Population

Background: The equations predicting maximal oxygen uptake (VO2max or peak) presently in use in cardiopulmonary exercise testing (CPET) softwares in Brazil have not been adequately validated. These equations are very important for the diagnostic capacity of this method. Objective: Build and validate a Brazilian Equation (BE) for prediction of VO2peak in comparison to the equation cited by Jones (JE) and the Wasserman algorithm (WA). Methods: Treadmill evaluation was performed on 3119 individuals with CPET (breath by breath). The construction group (CG) of the equation consisted of 2495 healthy participants. The other 624 individuals were allocated to the external validation group (EVG). At the BE (derived from a multivariate regression model), age, gender, body mass index (BMI) and physical activity level were considered. The same equation was also tested in the EVG. Dispersion graphs and Bland-Altman analyses were built. Results: In the CG, the mean age was 42.6 years, 51.5% were male, the average BMI was 27.2, and the physical activity distribution level was: 51.3% sedentary, 44.4% active and 4.3% athletes. An optimal correlation between the BE and the CPET measured VO2peak was observed (0.807). On the other hand, difference came up between the average VO2peak expected by the JE and WA and the CPET measured VO2peak, as well as the one gotten from the BE (p = 0.001). Conclusion: BE presents VO2peak values close to those directly measured by CPET, while Jones and Wasserman differ significantly from the real VO2peak.

Forecast of acute respiratory infections: expected nonepidemic mobidity in Cuba

A forecast of nonepidemic morbidity due to acute respiratory infections were carry out by using time series analysis. The data consisted of the weekly reports of medical patient consultation from ambulatory facilities from the whole country. A version of regression model was fitted to the data. Using this approach, we were able to detect the starting data of the epidemic under routine surveillance conditions for various age groups. It will be necessary to improve the data reporting system in order to introduce these procedures at the local health center level, as well as on the provincial level.

Detection of all four dengue serotypes in Aedes aegypti female mosquitoes collected in a rural area in Colombia

The Aedes aegypti vector for dengue virus (DENV) has been reported in urban and periurban areas. The information about DENV circulation in mosquitoes in Colombian rural areas is limited, so we aimed to evaluate the presence of DENV in Ae. aegypti females caught in rural locations of two Colombian municipalities, Anapoima and La Mesa. Mosquitoes from 497 rural households in 44 different rural settlements were collected. Pools of about 20 Ae. aegypti females were processed for DENV serotype detection. DENV in mosquitoes was detected in 74% of the analysed settlements with a pool positivity rate of 62%. The estimated individual mosquito infection rate was 4.12% and the minimum infection rate was 33.3/1,000 mosquitoes. All four serotypes were detected; the most frequent being DENV-2 (50%) and DENV-1 (35%). Two-three serotypes were detected simultaneously in separate pools. This is the first report on the co-occurrence of natural DENV infection of mosquitoes in Colombian rural areas. The findings are important for understanding dengue transmission and planning control strategies. A potential latent virus reservoir in rural areas could spill over to urban areas during population movements. Detecting DENV in wild-caught adult mosquitoes should be included in the development of dengue epidemic forecasting models.

Sooty blotch and flyspeck control with fungicide applications based on calendar, local IPM, and warning system

The objective of this work was to compare fungicide application timing for the control of sooty blotch and flyspeck (SBFS) of 'Fuji' apples in Rio Grande do Sul state, Brazil. The following treatments were evaluated in two growing seasons: two warning system-based (modified version of the Brown-Sutton-Hartmann system) spray of captan plus thiophanate methyl, with or without summer pruning; two calendar/rain-based spray of captan or a mixture of captan plus thiophanate methyl; fungicide spray timing based on a local integrated pest management (IPM) for the control of summer diseases; and a check without spraying. Sooty blotch and flyspeck incidence over time and their severity at harvest were evaluated. The highest number of spray was required by calendar/rain-based treatments (eight and seven sprays in the sequential years). The warning system recommended five and three sprays, in the sequential years, which led to the highest SBFS control efficacy expressed by the reduced initial inoculum and disease progress rate. Summer pruning enhanced SBFS control efficacy, especially by suppressing SBFS signs which tended to be restrained to the peduncle region of the fruit. Sooty blotch and flyspeck can be managed both with calendar and the grower-based IPM practices in Brazil, but a reduced number of sprays is required when the warning system is used.

Simulating maize yield in sub‑tropical conditions of southern Brazil using Glam model

The objective of this work was to evaluate the feasibility of simulating maize yield in a sub‑tropical region of southern Brazil using the general large area model (Glam). A 16‑year time series of daily weather data were used. The model was adjusted and tested as an alternative for simulating maize yield at small and large spatial scales. Simulated and observed grain yields were highly correlated (r above 0.8; p<0.01) at large scales (greater than 100,000 km²), with variable and mostly lower correlations (r from 0.65 to 0.87; p<0.1) at small spatial scales (lower than 10,000 km²). Large area models can contribute to monitoring or forecasting regional patterns of variability in maize production in the region, providing a basis for agricultural decision making, and Glam‑Maize is one of the alternatives.

Cadeia do biodiesel da bancada à indústria: uma visão geral com prospecção de tarefas e oportunidades para P&D&I

Contextualized overview of the Biodiesel Production Chain, from the lab bench to the industry, with critical evaluation of state-of-art and technological development through scientific articles and patents, focusing on feedstock, reaction/production, first and second generation processes, specification and quality, transport, storage, co-products (effluents and sub-products), and emissions. Challenges are identified and solutions are proposed based on the Brazilian feedstock, edaphoclimatic conditions, process monitoring in remote regions, state policy, and environment preservation, among others. Forecasts are made based on the technology assessment, identifying future trends and opportunities for R&D&I.

Patenteamento em nanotecnologia no Brasil: desenvolvimento, potencialidades e reflexões para o meio ambiente e a saúde humana

Nanotechnology developments continue to be produced at exponential rates for a wide and diverse range of applications. In this paper was done a study of technological forecasting in nanotechnology applied to health, based on information drawn in Brazil from 1991 to 2010. The longitudinal evolutions of the number of patent applications, their topics, and their respective patent families have been evaluated for the total global activity. There were obtained 1352 patent applications in this period. It were analyzed the legal nature of the depositors, the year of deposit, depositors' home countries and processes. It has been a goal subsidizes the policy-makers to adapt and modernize the regulatory framework on nanotechnology and risks involving health as a strategic area in the politics of Science.

Models and applications for risk assessment and prediction of Asian soybean rust epidemics

Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed.