Development of probabilistic mathematical model of forecast measles epidemy in 2020

Khalturina E.O., Poliker E.E., Zemskich B.L.

I.M. Sechenov First Moscow State Medical University, Moscow, Russia

Measles is a highly contagious disease of viral etiology. To date, measles remains one of the leading causes of death in young children and a steadily increasing incidence among adults. For many years, measles has been a vaccine-controlled infection, which has helped to reduce the incidence rate down to sporadic. However, recently, the epidemiological situation in terms of measles incidence has changed significantly - there is a steady increase in the incidence over the past few years. The aim of our study was to create a probabilistic mathematical model for calculating and evaluating the parameters of the possibility of a stochastic measles epidemic and to predict the vector of morbidity.
Materials and methods: in this study, a probabilistic mathematical model was developed, taking into account the relationship between infected, susceptible and unresponsive individuals, as well as the aggressive influence of external and internal risk factors integrated into the model of morbidity growth. The model introduced new parameters that are external and internal risk factors that aggressively affect the growth of measles incidence-migration of the population and the circulation of the pathogen.
Results: according to the calculations, in accordance with the incubation period of the disease, at the beginning of 2020, 29 cases of measles are expected in the city of Moscow. The forecast took into account factors such as the influx of labor migrants, the birth rate (children under 1 year of life), the contact coefficient of residents of a large metropolis, equal to 0.2. Within three months, presumably, the number of infected people can be reduced to zero.
Conclusions: thus, the use of a mathematical model to assess the prognosis of measles incidence, which integrates the parameters that affect the spread of the disease, allows us to identify the most significant of them and form an algorithm to reduce the incidence and / or elimination of measles in the Russian Federation by 2020.