UDC 614
DOI: 10.36871/2618-9976.2023.10.006
Authors
Alexey O. Nedosekin,
Doctor of Economics Sciences, PhD in Technical Sciences, Academician of IAELPS, Institute of Financial
Technologies, Saint-Petersburg,
Russia
Zinaida I. Abdulaeva,
North-Western
State Medical University named after I.I. Mechnikov
Daniil E. Murashev,
North-Western
State Medical University named after I.I. Mechnikov
Abstract
Target. Obtain an analytical form of an approximate solution for
a system of firstorder
nonlinear differential equations that describes
the dynamics of the epidemic in the SI model of two cohorts:
healthy – infected.
Methodology. The solution is sought in the form of a logistic
curve using one of three forms: normal distribution Ф, lognormal
distribution L and risk function Risk.
Results. During fuzzy optimization, the parameters of logistic
curves are identified both during the solution of a system of differential
equations with fixed parameters, and when constructing
a predictive model based on the presented short section of
historical data. Epidemic statistics are best interpreted by a possibilistic
process, in the cross section of which there is a fuzzy
number of a general form.
Conclusion. The solution for the SI model is well approximated
by logistic curves of type Ф. In a more complex case, both curves
of type Ф and profile curves of Risk, determined on the basis of
four parameters, are involved in the approximation.
Keywords
Possibilistic process, Fuzzy number of general form (GFN), Logistic curve
