Brief description

Predicting the spread of infectious diseases is an urgent task, since it allows for an assessment of the current situation and making informed decisions on the disease stemming measures to be taken. However, predictive models need constant adjustment and validation of the data obtained according to current data on infection spread dynamics. The present research aims to select and integrate a calibration method for the epidemiological Kermak-McKendrick SEIR model with additional factors. This paper provides an overview and analysis of calibration algorithms for the required parameters of the epidemiological model, as well as numerical experiments comparing the accuracy of the results. The resulting calibration method is the least squares method, since it allows considering boundary values and searching for a local minimum, spending the least amount of time compared to other algorithms.Automatic calibration of the model parameters allows for up-to-date predictions on the spread of infectious diseases with minimal time resources in response to changes in disease data and various quarantine measures. The developed solution can be tailored to other infection spread models.