Epidemiological Informing of the Population in Cities: Models and Their Application

With an increase of population density and contacts between people, the emergence of new biological viruses, the threat of various epidemics is growing. Countering these threats involves the implementation of large-scale preventive, therapeutic and other measures, both before the start and during the epidemic. Epidemiological informing of the population plays an important role in such counteraction. The currently used models of epidemiological informing the population of cities largely do not meet the needs of practice. This negatively affects the effectiveness of the response to epidemics. The purpose of the study is to develop new models and justify their applicability for understanding the processes in public health, the impact of epidemics on the economy and business. For the quantitative substantiation of programs (scenarios), such epidemiological informing, a method based on new models of epidemic development in related cities is proposed. The method is characterized by a new objective function that links economic efficiency with the state of health of the population in an epidemic. The models differ from the known solutions both in the space of the selected states of the processes under study and in the connections between them. Using the developed method, seven possible programs of epidemiological informing the population of related cities were analyzed and the best of them was found for specific conditions. New regularities have been established between the parameters of the programs being implemented and the results of the impact on the health and performance capability of the population. It is shown how an epidemic can develop in cities that are differently connected to each other by vehicles. The proposed method allows quickly find the best epidemiological informing programs for the population. The models underlying this method make it possible to predict public health and the impact of epidemics on the economy and business, depending on the planned measures to counteract epidemics. They are also applicable to determine the sources and time of infections’ onset. The obtained simulation results are in good agreement with the known facts. The method can be applied in advanced information systems to support the adoption of far-sighted decisions to counteract epidemics.