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.

Effect of working pressure at different spray nozzles on drift quantification in wind tunnel

Each year, there is an increase in pesticide consumption and in its importance of use in the large-scale agricultural production, being fundamental the knowledge of application technology to the activity success. The objective of the present study was to evaluate the influence of working pressure on the drift generated by different spray nozzles, assessed in wind tunnel. The treatments were composed of two spray nozzles AXI 110015 and AXI 11002 with pressure levels of 276 and 414 kPa. The spray solution was composed by water and NaCl at 10%. The applications were conducted at wind speed of 2.0 m s-1, being the drift collected at 5.0; 10.0 and 15.0 m away from the spray boom and at heights of 0.2; 0.4; 0.6; 0.8 e 1.0 m from the tunnel floor. To both spray nozzles, the greatest drift was collected at the smallest distance to the spray-boom and at the lowest height. The AXI 11002 nozzle gave a smaller drift relative to the AXI 110015 nozzle for the two tested pressures and for all the collection points. Regardless of the nozzle, a rise in the working pressure increases the spray drift percentage at all distances in the wind tunnel.

Performance of small wind turbines: simulation of electricity supply to loads connected to the public or isolated grid

ABSTRACT The successful in the implementation of wind turbines depends on several factors, including: the wind resource at the installation site, the equipment used, project acquisition and operational costs. In this paper, the production of electricity from two small wind turbines was compared through simulation using the computer software HOMER - a national model of 6kW and an imported one of 5kW. The wind resources in three different cities were considered: Campinas (SP/BR), Cubatão (São Paulo/BR) and Roscoe (Texas/ USA). A wind power system connected to the grid and a wind isolated system - batteries were evaluated. The results showed that the energy cost ($/kWh) is strongly dependent on the windmill characteristics and local wind resource. Regarding the isolated wind system – batteries, the full supply guarantee to the simulated electrical load is only achieved with a battery bank with many units and high number of wind turbines, due to the intermittency of wind power.

Prediction of transients and control reactions in a transonic wind tunnel

This work describes a lumped parameter mathematical model for the prediction of transients in an aerodynamic circuit of a transonic wind tunnel. Control actions to properly handle those perturbations are also assessed. The tunnel circuit technology is up to date and incorporates a novel feature: high-enthalpy air injection to extend the tunnel’s Reynolds number capability. The model solves the equations of continuity, energy and momentum and defines density, internal energy and mass flow as the basic parameters in the aerodynamic study as well as Mach number, stagnation pressure and stagnation temperature, all referred to test section conditions, as the main control variables. The tunnel circuit response to control actions and the stability of the flow are numerically investigated. Initially, for validation purposes, the code was applied to the AWT ("Altitude Wind Tunnel" of NASA-Lewis). In the sequel, the Brazilian transonic wind tunnel was investigated, with all the main control systems modeled, including injection.

Wind tunnel simulation of atmospheric boundary layer flows

The present work shows how thick boundary layers can be produced in a short wind tunnel with a view to simulate atmospheric flows. Several types of thickening devices are analysed. The experimental assessment of the devices was conducted by considering integral properties of the flow and the spectra: skin-friction, mean velocity profiles in inner and outer co-ordinates and longitudinal turbulence. Designs based on screens, elliptic wedge generators, and cylindrical rod generators are analysed. The paper describes in detail the experimental arrangement, including the features of the wind tunnel and of the instrumentation. The results are compared with experimental data published by other authors and with naturally developed flows.