The modern condition of Crimean-Congo hаemorrhagic fever natural focus in the Russian Federation

Aim.To determine the boundaries of the Crimean-Congo haemorrhagic fever (CCHF) natural focus in the Russian Federation at the current stage, to clarify the range of the main reservoirs and vectors of CCHF pathogen, to assess the epidemiological capacity of the natural focus.Materials and methods.The materials of epidemiological and epizootological monitoring of the CCHF natural focus, methods of epidemiological and epizootological analysis, molecular-genetic and cartographic methods were used in the work. The findings have been treated using by software package Microsoft Office Excel 2010.Results.The unified integrity of the CCHF semi-desert-steppe natural focus, which occupies vast territory of the southern part of the Russian Federation of 831 thousand square kilometres, is science-based. Expanding the geographic area of the CCHF agent with the involvement new administrative district can be seen. The trend of shifting of the CCHF natural focus borders in a northerly direction has been established. An increasing of epidemiological capacity of the CCHF natural focus has been noted.Hyalomma marginatumticks are the main reservoirs and vectors of CCHF virus. The genotype «Europe-1» is predominant genotype in the natural focus.Conclusion.It is necessary to improve the tactics of CCHF epidemiological surveillance using modern science-based approaches. For example, automated forecasting-modeling system, using results of multifactorial risk analysis, which have an impact on the intensity of CCHF epidemic appearances, allows to quantitative forecast epidemiological situation on this infection in the aggregate and for certain subjects of the south of the Russian Federation.

Automated data-intensive forecasting of plant phenology throughout the United States

Phenology - the timing of cyclical and seasonal natural phenomena such as flowering and leaf out - is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there has been no automated species-level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here we describe a new near-term phenology forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large-scale near-term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology.