02220nas a2200145   4500008004100000245010000041210006900141300001100210490000900221520172400230100002101954700002301975700002801998856004802026       2020                        eng d00aForecasting of Population Narcotization under the Implementation of a Drug Use Reduction Policy0 aForecasting of Population Narcotization under the Implementation  a1–140 v20203 aIn this paper, we present an approach to drug addiction simulation and forecasting in the medium and long terms in cities having a high population density and a high rate of social communication. Drug addiction forecasting is one of the basic components of the antidrug policy, giving informational and analytic support both at the regional and at the governmental level. However, views on the drug consumption problem vary in different regions, and as a consequence, several approaches to antidrug policy implementation exist. Thereby, notwithstanding the fact that the phenomenology of the population narcotization process is similar in the different regions, approaches to the modeling of drug addiction may also substantially differ for different kinds of antidrug policies. This paper presents a survey of the available antidrug policies and the corresponding approaches to the simulation of population narcotization. This article considers the approach to the construction of the regression model of anesthesia on the main components formed on the basis of indicators of social and economic development. The substantiation of the chosen method is given, which is associated with a significant correlation of indicators, which characterizes the presence of a small number of superfactors. This allows us to form a conclusion about the general level of development of the region as the main factor determining the drug addiction. A new model is proposed for one of the most widespread antidrug policies, namely, the drug use reduction policy. The model helps determine the significant factors of population narcotization and allows to estimate its damage. The model is tested successfully using St. Petersburg data.1 aMityagin, Sergey1 aGershenson, Carlos1 aBoukhanovsky, Alexander  uhttp://turing.iimas.unam.mx/sos/?q=node/188