Tick-borne infections in the south of Russia: modern epidemiological situation, new approach to create “forecasting” and “explaining” morbidity models (in astrakhan rickettsiosis fever and crimean-congo hemorrhagic fever)

Abstract

The article presents a description of the current tick-borne infection epidemiological situation in the south of Russia from the years 2013 to 2022, proposes a new approach to develop forecasting models for morbidity dynamics of Astrakhan rickettsial fever (ARF) and Crimean hemorrhagic fever (СCHF) in the Astrakhan region and presents data assessing 2022 explaining models for the Stavropol Territory and Astrakhan Region. Materials and methods. A comprehensive research was performed using epidemiological analysis and non-parametric statistical methods. The data assessing tick-borne infections epidemic process manifestations were retrieved from ARF and CCHF morbidity databases (developed as a project) and documents of infectious disease focus epidemiological examination provided by the departments of Rospotrebnadzor in the subjects of the Southern and North Caucasian Federal Districts. Morbidity models were developed using the Bayes theorem and Walds sequential statistical analysis, with a preliminary calculation of indicators informativeness by the Kullback method. The values of climatic factors from the database of the Center for Collective Use IKI-monitoring of the Space Research Institute of the Russian Academy of Sciences were used. Results. The results of the study indicate persistence of serious epidemiological situation regarding rickettsiosis of the tick-borne spotted fever group, Q fever, tick-borne borreliosis and CCHF in the south of Russia. Almost all tick-borne infections nosological forms in children under 14 years (including young children and infants) were widely involved in the epidemic process, which belong to patients at risk for a complicated disease course due to complicated diagnostics and treatment. The annual registration of tick-borne infections cases in the resort areas, with the subsequent occurrence of imported cases in other, including non-endemic regions poses a serious problem. The proposed forecasting models allow to predict the CСHF and ARF morbidity for each administrative district of the Astrakhan region with up to 91.7% accuracy. The explaining models CСHF accuracy for the Stavropol Territory and Astrakhan Region, when tested in 2022, was 88.5 and 83.3%, respectively, for ARF 91.7%. Conclusions. The further continuation of forecasting and explaining models verification for planning preventive measures and propose similar steps for tick-borne borreliosis and Q fever to epidemiological tick-borne infections to stabilize situation in the south of Russia.