Проблемы особо опасных инфекций (Oct 2018)

Forecasting of epizootic Activity of the Central Caucasian natural High-Mountain Plague Focus

  • V. M. Dubyansky,
  • E. V. Gerasimenko,
  • N. A. Davydova,
  • G. P. Shkarlet,
  • G. A. Mozloev,
  • V. A. Belogrudov,
  • A. A. Vlasov,
  • N. V. Tsapko,
  • L. I. Belyavtseva,
  • D. M. Bammatov

DOI
https://doi.org/10.21055/0370-1069-2018-3-50-53
Journal volume & issue
Vol. 0, no. 3
pp. 50 – 53

Abstract

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Central-Caucasian natural plague focus was permanently epizootically active since its discovering in 1971 till 2007. Inter-epizootic period has been in progress since 2008. It was not possible to isolate agent strains from field material. Therefore a forecast for focus activation is a relevant task, especially against the background of registered plague cases in humans in 2014–2016. Objective of the study was to create a forecasting model for quantitative prediction of possible activation or maintenance of inter-epizootic period. Materials and methods. We used archival data of Kabardino-Balkar Plague Control Station: journals of rodents’ autopsy, annual reports on epizootiological surveillance, meteorological data from meteostation “Kislovodsk” over the period of 1989–2017, and our epidemiological data for the period 2010 to 2017. We applied Spearman nonparametric correlation analysis, regression analysis, including principal component method, quarterly analysis, and inhomogeneous sequential pattern recognition procedures for statistical processing. Results and discussion. We have designed statistical model which provides for forecasting of plague focus epizootic activity proactively, a year in advance and 99 % probability or higher. The model was tested on retrospective data over the course of 7 years. All predictions were correct. The operational forecasts from 2015 to 2017 proved right too. However there is a possibility of fast changes in the ecology system conditions of the Central-Caucasian natural plague focus because of the global warming. Thereby the forecasting model will be annually checked for informative value of the predictors and, if necessary, adjusted accordingly.

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