Sergio Abimael R, Schimit Pedro H T
Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, Rua Vergueiro, 235/249, São Paulo 01525-000, Brazil.
Entropy (Basel). 2024 Aug 3;26(8):661. doi: 10.3390/e26080661.
This paper explores the application of complex network models and genetic algorithms in epidemiological modeling. By considering the small-world and Barabási-Albert network models, we aim to replicate the dynamics of disease spread in urban environments. This study emphasizes the importance of accurately mapping individual contacts and social networks to forecast disease progression. Using a genetic algorithm, we estimate the input parameters for network construction, thereby simulating disease transmission within these networks. Our results demonstrate the networks' resemblance to real social interactions, highlighting their potential in predicting disease spread. This study underscores the significance of complex network models and genetic algorithms in understanding and managing public health crises.
本文探讨了复杂网络模型和遗传算法在流行病学建模中的应用。通过考虑小世界网络模型和巴拉巴西-阿尔伯特网络模型，我们旨在复制城市环境中疾病传播的动态过程。本研究强调了准确描绘个体接触和社会网络以预测疾病进展的重要性。使用遗传算法，我们估计网络构建的输入参数，从而模拟这些网络内的疾病传播。我们的结果表明这些网络与真实社会互动相似，突出了它们在预测疾病传播方面的潜力。本研究强调了复杂网络模型和遗传算法在理解和管理公共卫生危机中的重要性。
