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.

Economic burden of expected epidemiological changes in diseases related to tobacco, Mexico

OBJECTIVE: To determine health care costs and economic burden of epidemiological changes in diseases related to tobacco consumption. METHODS: A time-series analysis in Mexico (1994-2005) was carried out on seven health interventions: chronic obstructive pulmonary diseases, lung cancer with and without surgical intervention, asthma in smokers and non-smokers, full treatment course with nicotine gum, and full treatment course with nicotine patch. According with Box-Jenkins methodology, probabilistic models were developed to forecast the expected changes in the epidemiologic profile and the expected changes in health care services required for selected interventions. Health care costs were estimated following the instrumentation methods and validated with consensus technique. RESULTS: A comparison of the economic impact in 2006 vs. 2008 showed 20-90% increase in expected cases depending on the disease (p<0.05), and 25-93% increase in financial requirements (p<0.01). The study data suggest that changes in the demand for health services for patients with respiratory diseases related to tobacco consumption will continue showing an increasing trend. CONCLUSIONS: In economic terms, the growing number of cases expected during the study period indicates a process of internal competition and adds an element of intrinsic competition in the management of preventive and curative interventions. The study results support the assumption that if preventive programs remain unchanged, the increasing demands for curative health care may cause great financial and management challenges to the health care system of middle-income countries like Mexico.

Enhanced Vegetation Index (EVI) na análise da dinâmica da vegetação da reserva biológica de Sooretama, ES

Técnicas de análises de séries temporais são utilizadas para caracterizar o comportamento de fenômenos naturais no domínio do tempo. Neste artigo, segundo a metodologia proposta por Box et al. (1994), 125 observações do Enhanced Vegetation Index (EVI) foram analisadas. Os valores modelados correspondem às variações temporais ocorridas no dossel florestal da reserva biológica de Sooretama, localizada ao Norte do Estado do Espírito Santo, no Município de Linhares. Os resultados indicaram que a metodologia foi adequada. Os resíduos do modelo ajustado são não correlacionados com distribuição normal, média zero e variância s². Com o menor valor do Critério de Informação de Akaike (AIC) -570,51, o modelo ajustado foi o Sazonal Auto-Regressivo Integrado de Médias Móveis (1,0,1)(1,0,1)12.

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.

Armazenagem de pequenos insetos secos em bandeja-manta adaptada de "cd box"

The display tray holds the specimens over a thin cotton layer glued to a thick paper attached to the cd holder tray. Althought only a temporary storing method, it is a good alternative when compared to other layer models. It has the advantages of low cost, protection of specimens, minimal or no damage, as well as good visibility through its cover.

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.

Molecular characterization of DDX26, a human DEAD-box RNA helicase, located on chromosome 7p12

DEAD-box proteins comprise a family of ATP-dependent RNA helicases involved in several aspects of RNA metabolism. Here we report the characterization of the human DEAD-box RNA helicase DDX26. The gene is composed of 14 exons distributed over an extension of 8,123 bp of genomic sequence and encodes a transcript of 1.8 kb that is expressed in all tissues evaluated. The predicted amino acid sequence shows a high similarity to a yeast DEAD-box RNA helicase (Dbp9b) involved in ribosome biogenesis. The new helicase maps to 7p12, a region of frequent chromosome amplifications in glioblastomas involving the epidermal growth factor receptor (EGFR) gene. Nevertheless, co-amplification of DDX26 with EGFR was not detected in nine tumors analyzed.

High mobility group box 1 as a mediator of endotoxin administration after hemorrhagic shock-primed lung injury

High mobility group box 1 (HMGB1) was discovered as a novel late-acting cytokine that contributes to acute lung injury (ALI). However, the contribution of HMGB1 to two-hit-induced ALI has not been investigated. To examine the participation of HMGB1 in the pathogenesis of ALI caused by the two-hit hypothesis, endotoxin was injected intratracheally in a hemorrhagic shock-primed ALI mouse model. Concentrations of HMGB1 in the lung of the shock group were markedly increased at 16 h (1.63 ± 0.05, compared to the control group: 1.02 ± 0.03; P < 0.05), with the highest concentration being observed at 24 h. In the sham/lipopolysaccharide group, lung HMGB1 concentrations were found to be markedly increased at 24 h (1.98 ± 0.08, compared to the control group: 1.07 ± 0.03; P < 0.05). Administration of lipopolysaccharide to the hemorrhagic shock group resulted in a notable HMGB1 increase by 4 h, with a further increase by 16 h. Intratracheal lipopolysaccharide injection after hemorrhagic shock resulted in the highest lung leak at 16 h (2.68 ± 0.08, compared to the control group: 1.05 ± 0.04; P < 0.05). Compared to the hemorrhagic shock/lipopolysaccharide mice, blockade of HMGB1 at the same time as lipopolysaccharide injection prevented significantly pulmonary tumor necrosis factor-alpha, interleukin-1beta and myeloperoxidase. Lung leak was also markedly reduced at 16 h; blockade of HMGB1 24 h after lipopolysaccharide injection failed to alter lung leak or myeloperoxidase at 48 h. Our observations suggest that HMGB1 plays a key role as a late mediator when lipopolysaccharide is injected after hemorrhagic shock-primed ALI and the kinetics of its release differs from that of one-hit ALI. The therapeutic window to suppress HMGB1 activity should not be delayed to 24 h after the disease onset.

Role of high mobility group box-1 and protection of growth hormone and somatostatin in severe acute pancreatitis

In this study, we investigated the potential role of high-mobility group box 1 (HMGB1) in severe acute pancreatitis (SAP) and the effects of growth hormone (G) and somatostatin (S) in SAP rats. The rats were randomly divided into 6 groups of 20 each: sham-operated, SAP, SAP+saline, SAP+G, SAP+S and SAP+G+S. Ileum and pancreas tissues of rats in each group were evaluated histologically. HMGB1 mRNA expression was measured by reverse transcription-PCR. Levels of circulating TNF-α, IL-1, IL-6, and endotoxin were also measured. In the SAP group, interstitial congestion and edema, inflammatory cell infiltration, and interstitial hemorrhage occurred in ileum and pancreas tissues. The levels of HMGB1, TNF-α, IL-1, IL-6 and endotoxin were significantly up-regulated in the SAP group compared with those in the sham-operated group, and the 7-day survival rate was 0%. In the SAP+G and SAP+S groups, the inflammatory response of the morphological structures was alleviated, the levels of HMGB1, TNF-α, IL-1, IL-6, and endotoxin were significantly decreased compared with those in the SAP group, and the survival rate was increased. Moreover, in the SAP+G+S group, all histological scores were significantly improved and the survival rate was significantly higher compared with the SAP group. In conclusion, HMGB1 might participate in pancreas and ileum injury in SAP. Growth hormone and somatostatin might play a therapeutic role in the inflammatory response of SAP.