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

• Main Authors: V.A. Mishchenko, O.V. Ladygin, I.P. Bykov, J.A. Zakharova, A.G. Sergeev, I.A. Kshnyasev
• Format: Article
• Language: English
• Published: FBSI “Federal Scientific Center for Medical and Preventive Health Risk Management Technologies” 2019-03-01
• Series: Analiz Riska Zdorovʹû
• Subjects: tick-borne viral encephalitis; morbidity; number of victims; modeling; prediction; selection of models; cyclic fluctuations; parameters
• Online Access: http://journal.fcrisk.ru/eng/2019/1/7
• ISSN: 2308-1155; 2308-1163

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.