Approach to prediction and receiver operating characteristic analysis of a regression model for assessing the severity of the course Lyme borreliosis in children

Abstract Introduction: Lyme borreliosis (LB) is a multisystemic zoonotic disease transmitted by the bite of in- fected tick vectors. The aim of the study is to develop a mathematical model for predicting the risk of severity of Lyme disease by the risk factor of the disseminated form of LB in children who have had a tick attack. To test the effectiveness of the formula for predicting the development of the disseminated stage of LB, we built a receiver operating characteristic (ROC) curve and determined the specificity and sensitivity of our model. The results of the examination of 122 patients with the confirmed local and disseminated stages of LB were taken as a basis. Material and methods: To build a prognostic model for prediction of the risk of the developing of the stage in LB predicting the risk of severity of course in Lyme borreliosis (PRSCLB), 122 children (aged 13 ±3 years) with LB were examined using multivariate regression analysis, including 52 boys and 70 girls. Groups of patients: 79 children with erythema migrans, 16 with Lyme arthritis, and 27 with nervous system involvement by LB. The quality of the prognostic model was checked by the Nagelkerke R Square (Nagelkerke R2) and the acceptability of this model was assessed using ROC analysis. Results: The method of multivariate regression analysis for predicting severe course and organ and system damage in LB in children, taking into account the factors and variants of the disease itself, makes it possible to develop a mathematical model for predicting the relative response factors (RRF) of severe forms of Lyme disease and will improve the effectiveness of treatment. This will create all the prerequisites for high-quality preventive measures and reduce the relative response factors rate. The initial data for predicting the severity of LB were 28 factors. According to the results of regres- sion analysis, 24 factors were included in the model for predicting the severity of LB. Conclusions: The results of the study showed that the multifactorial model predicts the severity and organ and system damage in LB in children with an accuracy of 95%. The ROC curve, which was built on the basis of the results, has an area under the curve of 0.94, which indicates the high efficiency of the model. Key words: Lyme borreliosis, Lyme disease, children, regression model, receiver operating charac- teristic analysis

Environmental costs as an indicator of sustainable development

One of the priorities of the modern economy is the optimal use of natural resources in economic activity. This is because the organization and management of production impacts the environment, which in turn affects both the well-being of society and the indicators of economic development. Which is why methodology and practice of public non-financial reporting reflecting indicators related to the conservation and assessment of natural resources, pollution control, waste management and recycling, and creating emission standards are actively developing. Environmental costs are becoming an important tool for making informed management decisions aimed at harmonizing the economy and the environment. At present, despite a large number of methodical developments, there is no solid theoretical basis for the formation of environmental indicators that adequately characterize the interaction of the economy and the environment and economic decision-making at all levels. The article clarifies the content, classification, performance evaluation system, environmental cost assessment methods for their application in management activities for deeper analysis, modelling and predicting economic phenomena and processes within the framework of the concept of sustainable development. The authors show approaches to the valuation of anthropogenic damage to nature, determined by the disproportionateness of natural and value indicators; the lack of prices of non-market goods, great uncertainty about the true value; the duration of the effects of man-made impacts and the long-term investment in environmental protection. Modern approaches to modelling and interpretation of results are generalized, as well as the possibilities of developing new (or improving existing) models for optimizing environmental costs. The direction of analysis of environmental indicators in the existing management system has been defined, in particular, through the study of non-financial reporting, which acts as a basis for calculating resource utilization, environmental quality and sustainability

Approach to prediction and receiver operating characteristic analysis of a regression model for assessing the severity of the course Lyme borreliosis in children

Introduction Lyme borreliosis (LB) is a multisystemic zoonotic disease transmitted by the bite of infected tick vectors. The aim of the study is to develop a mathematical model for predicting the risk of severity of Lyme disease by the risk factor of the disseminated form of LB in children who have had a tick attack. To test the effectiveness of the formula for predicting the development of the disseminated stage of LB, we built a receiver operating characteristic (ROC) curve and determined the specificity and sensitivity of our model. The results of the examination of 122 patients with the confirmed local and disseminated stages of LB were taken as a basis. Material and methods To build a prognostic model for prediction of the risk of the developing of the stage in LB predicting the risk of severity of course in Lyme borreliosis (PRSCLB), 122 children (aged 13 ±3 years) with LB were examined using multivariate regression analysis, including 52 boys and 70 girls. Groups of patients: 79 children with erythema migrans, 16 with Lyme arthritis, and 27 with nervous system involvement by LB. The quality of the prognostic model was checked by the Nagelkerke R Square (Nagelkerke R2) and the acceptability of this model was assessed using ROC analysis. Results The method of multivariate regression analysis for predicting severe course and organ and system damage in LB in children, taking into account the factors and variants of the disease itself, makes it possible to develop a mathematical model for predicting the relative response factors (RRF) of severe forms of Lyme disease and will improve the effectiveness of treatment. This will create all the prerequisites for high-quality preventive measures and reduce the relative response factors rate. The initial data for predicting the severity of LB were 28 factors. According to the results of regression analysis, 24 factors were included in the model for predicting the severity of LB. Conclusions The results of the study showed that the multifactorial model predicts the severity and organ and system damage in LB in children with an accuracy of 95%. The ROC curve, which was built on the basis of the results, has an area under the curve of 0.94, which indicates the high efficiency of the model

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities