Prediction of an epidemic spread based on the adaptive genetic algorithm - Chen, Bolun; Han, Shuai; Liu, Xiaoluan; Li, Zhe; Chen, Ting; Ji, Min (Frontiers in Physics, 2023)
In recent years, Corona Virus Disease 2019 (COVID-19) has ravaged the world, causing huge losses to the lives and property of people around the world. How to simulate the spread of an epidemic with a reasonable mathematical model and use it to analyze and to predict its development trend has attracted the attention of scholars in different fields. Based on the SIR (Susceptible Infected Recovered) propagation model, this thesis proposes the SEIRD (Susceptible Exposed Infected Recovered Dead) model by introducing specific medium such as self-healing rate, lethality rate, re-positive rate and considering the possibility of virus propagation through objects. Finally, this thesis simulates and analyzes the propagation process of nodes in different states in this model, and compares the model prediction results optimized by the Adaptive Genetic Algorithm with the real data. The experimental results show that the SEIRD model can effectively reflect the real epidemic spreading process and provide theoretical support for the relevant prevention and control departments.