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Purpose The purpose of this paper is to propose the basis for the unification of unmanned aerial vehicle (UAV) group control protocols for the fast deployment of communication network on territories unsuitable for stationary nodes placement. Design/methodology/approach The paper proposes the application of active data (AD) conception in which the data exist in a form of executable code allowing data packets to control its own propagation through network. The implementation is illustrated for some scenarios of UAV data communication network deployment, i.e., transmission of the AD using navigation functions and dynamic reconfiguration of the nodes. Findings The conception of AD expands the range of possible UAV group operations due to on-the-fly adaptation abilities to changes in existing or forthcoming group behavior protocols. This allows the real-time change of data transmission formats, frequency ranges, modulation types, radio network topologies which, in turn, provides the ability to dynamically form the special data transmission networks from a general purpose device temporarily reconfiguring them for data transmission task between transmitter and receiver beyond radio visibility range. Practical implications The paper includes use cases for some situation of UAV data communication network deployment. Originality/value The paper aims to expand the UAV group control principles by implementing by rapid adaptation to changes in existing or forthcoming group behavior protocols.

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The paper presents the results of statistical data from open sources on the development of the COVID-19 epidemic processing and a study сarried out to determine the place and time of its beginning in Russia. An overview of the existing models of the processes of the epidemic development and methods for solving direct and inverse problems of its analysis is given. A model for the development of the COVID-19 epidemic via a transport network of nine Russian cities is proposed: Moscow, St. Petersburg, Nizhny Novgorod, Rostov-on-Don, Krasnodar, Yekaterinburg, Novosibirsk, Khabarovsk and Vladivostok. The cities are selected both by geographic location and by the number of population. The model consists of twenty seven differential equations. An algorithm for reverse analysis of the epidemic model has been developed. The initial data for solving the problem were the data on the population, the intensity of process transitions from one state to another, as well as data on the infection rate of the population at given time moments. The paper also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by type of model (basic SEIR model, SIRD model, adaptive behavioral model, modified SEIR models), and by country (in Poland, France, Spain, Greece and others) and an overview of the applications that can be solved using epidemic spread modeling. Additional environmental parameters that affect the modeling of the spread of epidemics and can be taken into account to improve the accuracy of the results are considered. Based on the results of the modeling, the most likely source cities of the epidemic beginning in Russia, as well as the moment of its beginning, have been identified. The reliability of the estimates obtained is largely determined by the reliability of the statistics used on the development of COVID-19 and the available data on transportation network, which are in the public domain.

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