Abstract
Introduction. The study focuses on methods providing mathematical substantiation of discrepancies between actual incidence rates of Astrakhan rickettsial fever (ARF) and Crimean-Congo hemorrhagic fever (CCHF) and predicted rates due to the indirect impact of weather conditions during the current epidemic season.
The purpose of the study was to develop explanatory models for ARF and CCHF incidence using satellite monitoring (remote sensing) data and to present the results of their practical evaluation in the Stavropol Territory and Astrakhan Region.
Materials and methods. The materials included climate data provided by the Space Research Institute of the Russian Academy of Sciences as well as epidemiological data on CCHF and ARF incidence from 2005 to 2021. The explanatory models incorporated the Bayes theorem and Wald sequential analysis. All the calculations were completed using the Microsoft Excel 2010-based program developed by the authors.
Results. It has been found that the greatest indirect effect on development of the CCHF epidemiological situation is produced by the normalized difference vegetation index and relative air humidity in June-July in the Stavropol Territory and by the maximum, minimum and average air temperature in October as well as the minimum air temperature in July in the Astrakhan Region. ARF incidence rates depend on the indirect effect of the annual average and average annual maximum temperature, maximum temperature and the normalized difference vegetation index in April-July. The match between explanatory model-based results and prediction model-based results ranged within 46.2-100%.
Discussion. In addition to projecting incidence rates, which could be reached with the observed values of climatic factors in the current year, the explanatory models can be used for indirect verification of prediction models and for identification of factors causing differences in results.
Conclusion. The practical evaluation of explanatory models confirms the prospects and benefits of the study that should be continued, involving other regions highly endemic for tick-borne infections.
Publisher
Central Research Institute for Epidemiology
Subject
General Medicine,Medicine (miscellaneous)
Reference31 articles.
1. Ugleva S.V., Akimkin V.G., Ponezheva Zh.B., Akhmerova R.R., Spirenkova A.E., Chernikova Yu.Yu., et al. Epidemiological community and differences of transmissive infections in Astrakhan region. Epidemiologiya i vaktsinoprofilaktika. 2021; 20(6): 63–71. https://doi.org/10.31631/2073304620212066371 (in Russian)
2. Il’in V.P., Andaev E.I., Balakhonov S.V., Pakskina N.D. Morbidity rate forecasting for 2014 as regards tick-borne viral encephalitis in the territory of the Russian Federation based on multi-factor regression models. Problemy osobo opasnykh infektsiy. 2014; (2): 48–52. https://doi.org/10.21055/0370-1069-2014-2-48-52 (in Russian)
3. Berikov V.B., Lbov G.S., Polyakova G.L., Bakhvalova V.N., Panov V.V., Shchuchinova L.D., et al. Analysis of factors influencing the incidence of tick-borne encephalitis, using logical-and-probabilistic and correlation-regression models. Epidemiologiya i vaktsinoprofilaktika. 2011; (6): 25–34. (in Russian)
4. Shirokostup S.V., Shevchenko V.V., Luk’yanenko N.V. Results of evaluation of risk factors influence on disease incidence of tick-borne viral encephalitis and ixodic tick-borne borreliosis in combined foci of these infections in altai territory. Meditsinskiy al’manakh. 2014; (4): 67–70. (in Russain)
5. Haemig P.D., Sjöstedt de Luna S., Grafström A., Lithner S., Lundkvist Å., Waldenström J., et al. Forecasting risk of tick-borne encephalitis (TBE): using data from wildlife and climate to predict next year’s number of human victims. Scand. J. Infect. Dis. 2011; (5): 366–72. https://doi.org/10.3109/00365548.2011.552072
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