Modeling the epidemic process of HIV infection in populations with different natural population growth

Abstract

Aim: to assess the incidence, prevalence of HIV infection and population dynamics in populations with negative, zero and positive natural increase.Materials and Methods: Computer probabilistic modeling by the Monte Carlo method of the epidemic process of HIV infection in populations with different natural population growths was carried out. The parameters that are minimally necessary for such a description are used – population, birth rate, mortality, HIV prevalence, probability of HIV transmission.Results and discussion: When modeling the epidemic process, two main scenarios of the spread of HIV infection in a population without diagnostic, therapeutic and preventive effects were established. With a negative or zero population growth and a random distribution of healthy and infected individuals in the simulated space, the population density gradually decreases. At some point, the probability of contact of the infected and healthy person becomes less than the probability of an infected person to live up to this event. Under such conditions, the complete elimination of the virus from the population is theoretically possible. With a positive natural increase, it is possible to form a stable state in which the periods of increase in population size, incidence and prevalence of HIV infection change in a cyclical decline. HIV prevalence in countries such as Swaziland, Botswana and Lesotho has been at the level of 25–30% for many years. In this case, the population steadily grows. Based on the conducted simulation studies, it can be assumed that in these territories an evolutionarily stable balance was formed between the share of healthy and HIV-infected people in which the prevalence does not occur above 30%.Conclusions. The development of the epidemic process of HIV infection is determined by the dynamics of natural population growth. With a negative or zero natural growth and a random distribution of individuals in the simulated space, a complete elimination of the virus from the population is theoretically possible. With a positive natural increase, it is possible to form an evolutionarily stable cyclic balance between the proportion of healthy and HIV-infected people.

Keywords

• hiv infection
• mathematical model
• stochastic process
• epidemiological factors
• epidemiological studies
• demographic forecast
• epidemiological monitoring