Журнал инфектологии (Dec 2021)

Methodological approaches to morbidity forecasting in military educational organizations

  • A. E. Zobov,
  • A. A. Kuzin,
  • R. G. Makiev,
  • A. A. Zobova

DOI
https://doi.org/10.22625/2072-6732-2021-13-4-100-105
Journal volume & issue
Vol. 13, no. 4
pp. 100 – 105

Abstract

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The article discusses aspects of the application of extrapolation and factor approaches to epidemiological forecasting, outlines the limitations and features of their application in relation to the prediction of morbidity.It is shown that when using an extrapolation approach, it becomes possible to predict the most likely numerical characteristics of morbidity in a certain time perspective. At the same time, the accuracy of the obtained forecast depends on the length of the time series and the type of long-term dynamics of morbidity. In turn, the trends formed by the results of forecasting artificially level the critical levels of morbidity that characterize individual periods of time and are fundamentally important for understanding the real picture.The factor approach is based on the prediction of morbidity levels using a certain set of factors. The difficulty of using the factor approach is noted due to the stochasticity of the epidemic process.Based on the results of a retrospective epidemiological analysis of the personalized morbidity of cadets of the Military Medical Academy, the heterogeneity of military contingents in susceptibility to acute respiratory infections of the upper respiratory tract is shown.From the standpoint of the academician V.D .Belyakov’s et al. theory of the parasitic systems self-regulation, the conclusion is made about the expediency of using a factor approach for epidemiological forecasting of morbidity in organized collectives. It is proposed to use the state of individual resistance as one of the main factors determining the epidemic well-being of organized collectives.The results of the development and testing of an electronic database that allows epidemiological surveillance of the morbidity of trainees and its linear prediction are presented.

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