## Abstract

A new combined numerical algorithm for solving inverse problems of epidemiology is described in this paper. The combined algorithm consists of optimization and iterative methods, and determines the parameters specific to a particular population by using the statistical information for a few previous years. The coefficients of the epidemiology model describe particular qualities of the population and the development of the disease. The inverse problem of parameter identification in a mathematical model is reduced to the problem of minimizing an objective function characterizing the square deviation of the statistical data from the experimental data. The combination of statistical and optimization algorithms demonstrates the identification of parameters with an appropriate relative accuracy of 30%. The results can be used by public health organizations to predict the infectious disease epidemic in a given region by comparing the data of simulation with historical data.

Original language | English |
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Pages (from-to) | 121-131 |

Number of pages | 11 |

Journal | Eurasian Journal of Mathematical and Computer Applications |

Volume | 26 |

Issue number | 1 |

DOIs | |

Publication status | Published - 1 Feb 2018 |

## Keywords

- 65L09
- reconstruction of model parameters
- simulated annealing method
- numerical method
- Model of tuberculosis transmission
- gradient descent method
- system of ordinary differential equations
- parameter identification
- CONTROL STRATEGIES
- SENSITIVITY
- IMPACT
- DYNAMICS
- EPIDEMIOLOGY