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Morbidity Rate Forecasting for 2014 as Regards Tick-Borne Viral Encephalitis in the Territory of the Russian Federation Based on Multi-Factor Regression Models

Authors: V. P. Il’In; E. I. Andaev; S. V. Balakhonov; N. D. Pakskina;

Morbidity Rate Forecasting for 2014 as Regards Tick-Borne Viral Encephalitis in the Territory of the Russian Federation Based on Multi-Factor Regression Models

Abstract

Morbidity forecasting as regards tick-borne viral encephalitis has been carried out using three various methods. Demonstrated is the fact that variability consistency among the actual values accounting the numbers of seeking medical aid because of tick bites and estimated values within the current quasi-cycle is 31.4 %. Developed have been multi-factor regression models for medical aid encounters on the occasion of tick bites and for tick-borne viral encephalitis morbidity. Identified are the most significant factors which characterize specific prophylaxis, acaricide treatment, and abundance of infected ticks; estimated is the impact of the factors on the forecasting. Maximum level of interdependency has reached 78 %: in case with seroprevention it has come up to 64 %, vaccination - 11.5 %, acaricide treatments - 4 %, and abundance of infected ticks - 3%. It has been outlined that a 20 % extension of areas for acaricide treatments leads to a 5% decrement in medical aid encounters, while a 20% reduction - to an almost 7 % rise, respectively. Thus evaluation of the significance of the preventive and curative interventions is of a crucial importance while developing Territorial prevention programmes if undertaken along with consistent application of acaricide treatments.

Keywords

most reporting values, tick-borne viral encephalitis morbidity, прогноз, наиболее информативные показатели, forecasting, Infectious and parasitic diseases, RC109-216, preventive interventions, заболеваемость клещевым вирусным энцефалитом, logarithmical and multi-factor regression models, профилактические мероприятия, логарифмическая и многофакторная регрессионные модели

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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