Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia.Methods: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models.Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia

Identificando hábitats de nidificación potencial para el águila real aplicadas al diseño de 'áreas de importancia para las aves'

Geographic information systems (GIS)-based habitat-suitability modelling is becoming an essential tool in conservation biology. A multi-scale approach has been proposed as a particularly useful way to identify different factors affecting habitat preferences. In this paper, we developed predictive models of potentially suitable habitat for golden eagles Aquila chrysaetos at three spatial scales in a representative Mediterranean area on the Iberian Peninsula. We used logistic regression through a generalized linear model (GLM) to model golden eagle breeding habitat preferences. The best-occurrence GLM models were those that involved topographic factors as independent predictors. Golden eagles seemed to prefer rugged and higher places of the study area for nesting. Climatic factors identified cold temperatures in January and temperate ones in July as the best predictors of eagles’ occurrence. This was also higher in places with less agricultural areas and higher surface of pine forests. The distribution of potentially suitable area matches the distribution of mountain ranges, mainly in inner sectors of the study area. In contrast, potentially suitable nest sites in coastland areas remain unoccupied by golden eagles. Avoidance of coastland places for nesting may be due to the synergistic effects of human avoidance and the occurrence of potential competitors, like the endangered Bonelli’s eagle Hieraaetus fasciatus. When mapped at a fine spatial resolution, the best GLM model identified large areas that fall outside the current network of protected areas. We therefore propose three new important bird areas for the region.Fundación Terra Natur