A Method for Parameters Estimation in a Dynamical Model of Ebola Virus Transmission in Sierra Leone

• Main Authors: Li Li, Li-Xia Du, Ziheng Yan, Jie Zhang, Yong-Ping Wu
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2020-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2020/9172835

Ebola is an infectious virus that causes Ebola hemorrhagic fever in primates and humans, which was first found in 1976. The Ebola virus outbreak in West Africa in 2014 was the largest ever. A lot of researchers use mathematical models to analyze the characteristics of infectious diseases. However, many parameters in the model cannot be estimated completely. To ease the difficulty, we proposed an approach to estimate the parameter based on genetic algorithm (GA). GA uses the natural selection method of the fittest to find the optimal solution of the model. The least residual squares sum is used as fitness function to measure the performance of GA in parameter estimation. Moreover, we used a dynamical model and the real data of Ebola in Sierra Leone to verify the validity of GA. The experimental results indicate that the GA has strong competitiveness compared with the classical method, and it is a feasible method for estimating the parameters of infectious disease models.