On the basis of the analysis of process of becoming (institutionalization) of social institute as a phenomenon the classical concept of five basic social institutes (family, education, religion, economic and political institutes) is broadened. The society in the course of its historical development forms new spectrum of needs hence the necessity in both reconsidering changes of functional tasks, limits, normative value and status role systems of social institutes in function and recognizing existence of new social institutes. At the modern stage, considering actual functional requirements of society, the social institute of medicine is a new social institute. The article considers process of transformation of medicine into independent social institute at the turn of XX-XXI centuries. At that it is noted that evolutionary process of institutionalization of medicine requires a new in-depth comprehension accounting actual social transforming processes in the health care system. Simultaneously, another comprehension is required related to altering situation in health care system itself, transformation of views of patient on process of rendering of medical care and expectations from health care system, alterations of concepts of patient-physician relationships, reorganization of models of interaction of a single individual with the state, society, social institutes and groups.

Background Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. Methods For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast algorithms. Results Findings show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: σ(I) = 0.0129 and σ(R) = 0.0058, respectively. Conclusions The present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time.

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