Abstract
West Nile fever is an important viral transmissible natural focal disease in the world. Protecting the population from infection with this virus is organized considering the epizootic and epidemiological situation, assessment and analysis of which is based on the standards on epidemiological surveillance of transmissible infections. Information database includes characteristics of circulating strains; range of reservoirs; sources and vectors of the virus; hydrological, climatic and geographical description; data of monitoring of incidence and prevalence of the disease; and the risks of human infection. The estimation of the latter requires conducting of targeted descriptive-evaluative and analytical epidemiological research. Aim. To perform statistical processing and analysis of epidemiologically important information on the results of survey of respondents with determination of epidemiologically important risk factors of human infection with West Nile virus; to establish the nature of the relationship of identified risk factors in case of their combined impact. Materials and Methods. Systematization of epidemiological information of 120 respondents was performed by creating a computer database. Statistical analysis of the data was conducted using nonparametric mathematical methods: multivariate linear regression analysis, logistic regression, discriminant analysis, neural networks method (neural networks software package Statistica 8.0) Results and Discussion. Grouping and systematization of data are based on a hypothesis about the factors' impact on the likelihood of infection with the virus. Epidemiological information is divided into semantic units to differentiate the type of risk: behavioral, occupational, recreational, etc. It is determined that the estimated parameter t has to equal 1.979280117 and more (degrees of freedom k (124) 95% (P<0.05)). Evaluation of reliability of the discrepancies of alternative features detected 11 signs with significant difference (P<0.05). The study of combined factors' influence on human infection with West Nile virus using linear and logistic models showed no such effect. The use of neural networks showed unacceptable levels of initial approximation of the observed data (coefficient of determination R2 <0.3). Conclusions. Eleven epidemiologically significant risk factors for human infection with West Nile virus were found. Multivariate analysis denied their combined effect on the infection of people: each of the features is independent, and the presence of only one of them has a high risk of human infection. The results showed that the area of research is the active natural focus of West Nile fever.
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