Morbidity with tick-borne viral encephalitis in some regions in Uralskiy Federal District with predictive estimate of short-term epidemiologic situation

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616.99: 578.426

V.A. Mishchenko1,2, O.V. Ladygin1, I.P. Bykov1, J.A. Zakharova1, A.G. Sergeev1,3, I.A. Kshnyasev2


1Rospotrebnadzor's Yekaterinburg Research Institute of Viral Infections, 23 Letnyaya Str., Yekaterinburg, 620030, Russian Federation
2Institute of Plant and Animal Ecology of the Urals Department of Russian Academy of Science, 202 8 Marta Str., URAN, Yekaterinburg, 620144, Russian Federation
3Ural State Medical University of the RF Public healthcare Ministry, 3 Repina Str., 630028, Yekaterinburg, Russian Federation


Extrapolation prediction of epidemic situation as per tick-borne viral encephalitis (TVE) on endemic territories that is based on analyzing time rows of morbidity is a promising approach to be applied in predictive medical-ecological and epidemiologic research.

The authors examined long-term dynamics showing both number of people who suffered from tick bites and morbidity with tick-borne viral encephalitis (TVE) in 4 regions in the Ural Federal District over 2007–2017.

We applied a sum of harmonic functions as a mathematic model; parameters of the functions were detected with Le-venberg–Marquardt procedure for non-linear estimates. The technique is flexible and it allows both to apply parameters of harmonic fluctuation that are common for all 4 regions and to estimate parameters that differ in various regions and are of special interest (average long-term values and other fluctuation parameters). One of the research goals was to estimate dynamics in number of people who suffered from tick bites and morbidity with TVE in the Ural Federal District regions over the examined period and to predict epidemiologic situation for the coming years. To do that, we built several harmonic regression models with different number of estimated parameters. To compare and rank the models, we applied Akaike consistent information criterion that determines optimality as a compromise between a model accuracy and complexity.

Our analysis of morbidity with TVE over 2007–2017 in Sverdlovsk, Chelyabinsk, Tyumen, and Kurgan region allowed us to quantify discrepancies in average long-term parameters between these Ural Federal District regions. The highest average long-term morbidity was fixed in Kurgan region; the lowest one, in Sverdlovsk and Chelyabinsk region. But a number of people who suffered from tick bites was higher in Sverdlovsk, Chelyabinsk, and Tyumen region than in Kurgan region over the same period. We showed that long-term fluctuations in ticks activity in the Ural Federal District can be considered in-phase and it can possibly mean there is regional synchronization. We detected quasi-periods of cycles both for number of people bitten by ticks and morbidity with TVE and built a short-term prediction for epidemic situation as per TVE in the region on the basis of the proposed harmonic model for a period up to 2022; a probable TVE morbidity peak can be reached in 2020–2021.

tick-borne viral encephalitis, morbidity, number of victims, modeling, prediction, selection of models, cyclic fluctuations, parameters
Mishchenko V.A., Ladygin O.V., Bykov I.P., Zakharova J.A., Sergeev A.G., Kshnyasev I.A. Morbidity with tick-borne viral encephalitis in some regions in uralskiy federal district with predictive estimate of short-term epidemiologic situation. Health Risk Analysis, 2019, no. 1, pp. 68–77. DOI: 10.21668/health.risk/2019.1.07.eng
  1. Ammosov А.D. Kleshchevoi entsefalit [Tick-borne encephalitis]. Kol'tsovo, Vektor-Best Publ., 2006, 115 p. (in Russian).
  2. Korenberg E.I., Pomelova V.G., Osin N.S. Prirodnoochagovye infektsii, peredayushchiesya iksodovymi kleshchami [Infections with natural focality transmitted by ixodid ticks]. Moscow, Kommentarii Publ., 2013, 464 p. (in Russian).
  3. Volkova L.I., Kovtun O.P., Tereschuk M.A. Klinicheskie osobennosti khronicheskogo kleshchevogo entsefalita i epilepsii Kozhevnikova na Srednem Urale [Clinical characteristics of chronical tickborne encephalitis and Kozhevnikov's epilepsia partialis continua in the Middle Urals]. Russkii zhurnal detskoi nevrologii, 2011, vol. 6, no. 2, pp. 3–10 (in Russian).
  4. Zlobin V.I. Kleshchevoi entsefalit v Rossiiskoi Federatsii: etiologiya, epidemiologiya i strategiya profilaktiki [Tick-borne encephalitis in the Russian Federation: etiology, epidemiology and prevention strategy.]. Terra Medica, 2010, no. 2, pp. 13–21 (in Russian).
  5. Luchinina S.V., Stepanova O.N., Pogodina V.V., Sten'ko E.A., Chirkova G.G. [et al.]. Sovremennaya epidemiologicheskaya situatsiya po kleshchevomu virusnomu entsefalitu v Chelyabinskoi oblasti [Modern Epidemiological Situation of Tick-Borne Encephalitis in Chelyabinsk Region, Russia]. Epidemiologiya i vaktsinoprofilaktika, 2014, vol. 75, no. 2, pp. 32–37 (in Russian).
  6. Konkova-Reydman A.B., Ter-Bagdasaryan L.V. Sovremennye aspekty epidemiologii infektsii, peredayushchikhsya iksodovymi kleshchami [Modern aspects of epidemiology of ticks transmitted infections]. Epidemiologiya i infektsionnye bolezni, 2014, vol. 19, no. 5, pp. 26–31 (in Russian).
  7. Yasyukevich V.V., Titkina S.N., Popov I.O., Davidovich E.A., Yasyukevich N.V. Klimatozavisimye zabolevaniya i chlenistonogie perenoschiki: vozmozhnoe vliyanie nablyudaemogo na territorii Rossii izmeneniya klimata [Climate-dependant diseases and arthropod vectors: possible influence of climate change observed in Russia]. Problemy ekologicheskogo monitoringa i modelirovaniya ekosistem, 2013, vol. 25, pp. 314–359 (in Russian).
  8. Yastrebov V.K., Rudakov N.V., Shpynov S.N. Transmissivnye kleshchevye prirodno-ochagovye infektsii v Rossiiskoi Federatsii: tendentsii epidemicheskogo protsessa, aktual'nye voprosy profilaktiki [Transmissive tick-borne natural focal infections in the Russian Federation: trends of the epidemiological process, topical prophylaxis issues]. Sibirskii meditsinskii zhurnal, 2012, vol. 111, no. 4, pp. 91–93 (in Russian).
  9. Il’in V.P., Andaev E.I., Balakhonov S.V., Pakskina N.D. Prognozirovanie zabolevaemosti kleshchevym virusnym entsefalitom v Rossiiskoi Federatsii v 2014 g., osnovannoe na mnogofaktornykh regressionnykh modelyakh [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 infektsii, 2014, no. 2, pp. 48–52 (in Russian).
  10. Noskov A.K., Il’in V.P., Andaev E.I., Pakskina N.D., Verigina E.V., Balakhonov S.V. Zabolevaemost' kleshchevym virusnym entsefalitom v Rossiiskoi Federatsii i po federal'nym okrugam v 2009–2013 gg., epidemiologicheskaya situatsiya v 2014 g. i prognoz na 2015 g. [Incidence of Tick-Borne Viral Encephalitis in the Russian Federation and across Federal Districts in 2009–2013. Epidemiological Situation in 2014 and Prognosis for 2015]. Problemy osobo opasnykh infektsii, 2015, no. 1, pp. 46–50 (in Russian).
  11. Kiffner C., Zucchini W., Schomaker P., Vor T., Hagedorn P., Niedrig M., Rühe F. Determinants of tick-borne encephalitis in counties of southern Germany, 2001–2008. International Journal of Health Geographics, 2010, vol. 9, pp. 1–10. DOI: 10.1186/1476-072X-9-42
  12. Heinz F.X., Stiasny K., Holzmann H. [et al.]. Vaccination and Tick-borne Encephalitis, Central Europe. Emerging Infectious Diseases, 2013, vol. 19, no. 1, pp. 69–76. DOI: 10.3201/eid1901.120458
  13. Romanenko V.V., Kilyachina A.S., Yesyunina M.S., Аnkudinova A.V., Pimenova T.A. Effektivnost' programmy massovoi immunoprofilaktiki kleshchevogo entsefalita [Efficiency of the program of mass immunoprophylaxis of Tick-Borne Encephalitis]. Biopreparaty. Profilaktika, diagnostika, lechenie, 2008, no. 2, pp. 9–14 (in Russian).
  14. Yesyunina M.S., Romanenko V.V., Kilyachina A.S. Dlitel'nost' sokhraneniya postprivivochnogo immuniteta k virusu kleshchevogo entsefalita posle revaktsinatsii [Duration of post-vaccination immunity against tick-borne encephalitis following booster doses]. Trudy Instituta poliomielita i virusnykh entsefalitov imeni M.P. Chumakova RАMN. Meditsinskaya virusologiya, 2015, vol. 29, no. 2, p. 132 (in Russian).
  15. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974, vol. 19, pp. 716–723.
  16. Anderson, D.R., Burnham, K.P., White, G.C. Comparison of Akaike information criterion and consistent Akaike in-formation criterion for model selection and statistical inference from capture-recapture studies. Journal of Applied Statistics, 1998, vol. 25, pp. 263–282.
  17. Burnham K.P., Anderson D.R. Model selection and multimodel inference: a practical information-theoretic approach. New York, Springer Verlag Publ., 2002, 496 p.
  18. Yang Y. Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation. Biometrika, 2005, vol. 92, no. 4, pp. 937–950.
  19. Tsokova T.N., Kozlov L.B. Razrabotka matematicheskoi modeli prognozirovaniya zabolevaemosti kleshchevym entsefalitom [Development of mathematical model of forecasting of desease the virus of tick-born encephalitis]. Uspekhi sovremennogo estestvoznaniya, 2008, no. 6, pp. 12–16 (in Russian).
  20. Stefanoff P., Rubikowska B., Bratkowski J., Ustrnul Z., Vanwambeke S.O., Rosinska M. A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012. International Journal of Environmental Research and Public Health, 2018, vol. 15, no. 4, pp. 1–17. DOI: 10.3390/ijerph15040677

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