Ma Baoshan, Qi Jishuang, Wu Yiming, Wang Pengcheng, Li Di, Liu Shuxin
School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China.
Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA.
Digit Signal Process. 2022 Jul;127:103577. doi: 10.1016/j.dsp.2022.103577. Epub 2022 May 4.
The outbreak of coronavirus disease (COVID-19) and its accompanying pandemic have created an unprecedented challenge worldwide. Parametric modeling and analyses of the COVID-19 play a critical role in providing vital information about the character and relevant guidance for controlling the pandemic. However, the epidemiological utility of the results obtained from the COVID-19 transmission model largely depends on accurately identifying parameters. This paper extends the susceptible-exposed-infectious-recovered (SEIR) model and proposes an improved quantum-behaved particle swarm optimization (QPSO) algorithm to estimate its parameters. A new strategy is developed to update the weighting factor of the mean best position by the reciprocal of multiplying the fitness of each best particle with the average fitness of all best particles, which can enhance the global search capacity. To increase the particle diversity, a probability function is designed to generate new particles in the updating iteration. When compared to the state-of-the-art estimation algorithms on the epidemic datasets of China, Italy and the US, the proposed method achieves good accuracy and convergence at a comparable computational complexity. The developed framework would be beneficial for experts to understand the characteristics of epidemic development and formulate epidemic prevention and control measures.
冠状病毒病（COVID-19）的爆发及其随之而来的大流行给全球带来了前所未有的挑战。对COVID-19进行参数建模和分析对于提供有关该疾病特征的重要信息以及控制大流行的相关指导起着关键作用。然而，从COVID-19传播模型获得的结果的流行病学效用在很大程度上取决于准确识别参数。本文扩展了易感-暴露-感染-康复（SEIR）模型，并提出了一种改进的量子行为粒子群优化（QPSO）算法来估计其参数。开发了一种新策略，通过将每个最优粒子的适应度与所有最优粒子的平均适应度相乘的倒数来更新平均最优位置的加权因子，这可以增强全局搜索能力。为了增加粒子多样性，设计了一个概率函数在更新迭代中生成新粒子。与中国、意大利和美国的疫情数据集上的现有估计算法相比，该方法在可比的计算复杂度下实现了良好的准确性和收敛性。所开发的框架将有助于专家了解疫情发展的特征并制定疫情防控措施。
