Rural tourism as risk factor for the transmission of schistosomiasis in Minas Gerais, Brazil

Recently, the booming rural tourism in endemic areas of the state of Minas Gerais was identified as a contributing factor in the dissemination of the infection with Schistosoma mansoni. This article presents data from six holiday resorts in a rural district approximately 100 km distant from Belo Horizonte, MG, Brazil, where a possibly new and until now unperceived way of transmission was observed. The infection takes place in swimming pools and little ponds, which are offered to tourists and the local population for fishing and leisure activities. The health authorities of the district reported cases of schistosomiasis among the local population after visiting these sites. As individuals of the non-immune middle class parts of the society of big urban centers also frequent these resorts, infection of these persons cannot be excluded. A malacological survey revealed the presence of molluscs of the species Biomphalaria glabrata and Biomphalaria straminea at the resorts. The snails (B. glabrata) of one resort tested positive for S. mansoni. In order to resolve this complex problem a multidisciplinary approach including health education, sanitation measures, assistance to the local health services, and evolvement of the local political authorities, the local community, the tourism association, and the owners of the leisure resorts is necessary. This evidence emphasizes the urgent need for a participative strategic plan to develop the local tourism in an organized and well-administered way. Only so this important source of income for the region can be ensured on the long term without disseminating the disease and putting the health of the visitors at risk.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R² = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.

Rural tourism: a risk factor for schistosomiasis transmission in Brazil

This paper reports an outbreak of acute schistosomiasis among 38 tourists who rented a country house in the district of Igarapé, the metropolitan region of Belo Horizonte, Brazil, during a holiday period in 2006. A total number of 32 individuals were positive for Schistosoma mansoni. Results of stool examinations revealed individual S. mansoni egg counts per gram of faeces (epg) ranging from 4-768 epg with a geometric mean egg count of 45. The most frequent clinical symptoms were abdominal pain (78.1%), headache (75%), fever (65.6%), dry cough (65.2%) and both diarrhoea and asthenia (59.4%). A malacological survey of the area, where 22 specimens of Biomphalaria glabrata were collected, revealed three (13.6%) specimens eliminating Schistosoma cercariae. This investigation re-confirms a recently described pattern of schistosomiasis infection, resulting in the acute form of the disease and connected to rural tourism, which contributes to the spread of the disease among the middle-class and into non-endemic areas. The lack of specific knowledge about acute schistosomiasis among health services causes an increased number of unnecessary diagnostic procedures and delays in accurate diagnosis and treatment, resulting in considerable discomfort for the patients.

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.