Tajmirriahi Mahnoosh, Amini Zahra, Kafieh Rahele, Rabbani Hossein, Mirzazadeh Ali, Javanmard Shaghayegh Haghjooy
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Epidemiology and Biostatistics, Institute for Global Health Sciences, University of California, San Francisco, California, United States.
J Med Signals Sens. 2022 May 12;12(2):95-107. doi: 10.4103/jmss.jmss_134_21. eCollection 2022 Apr-Jun.
The world is experiencing another pandemic called COVID-19. Several mathematical models have been proposed to examine the impact of health interventions in controlling pandemic growth.
In this study, we propose a fractional order distributed delay dynamic system, namely, EQIR model. In order to predict the outbreak, the proposed model incorporates changes in transmission rate, isolation rate, and identification of infected people through time varying deterministic and stochastic parameters. Furthermore, proposed stochastic model considers fluctuations in population behavior and simulates different scenarios of outbreak at the same time. Main novelty of this model is its ability to incorporate changes in transmission rate, latent periods, and rate of quarantine through time varying deterministic and stochastic assumptions. This model can exactly follow the disease trend from its beginning to current situation and predict outbreak future for various situations.
Parameters of this model were identified during fitting process to real data of Iran, USA, and South Korea. We calculated the reproduction number using a Laplace transform-based method. Results of numerical simulation verify the effectiveness and accuracy of proposed deterministic and stochastic models in current outbreak.
Justifying of parameters of the model emphasizes that, although stricter deterrent interventions can prevent another peak and control the current outbreak, the consecutive screening schemes of COVID-19 plays more important role. This means that the more diagnostic tests performed on people, the faster the disease will be controlled.
世界正在经历另一场名为 COVID-19 的大流行。已经提出了几种数学模型来研究卫生干预措施对控制大流行增长的影响。
在本研究中，我们提出了一个分数阶分布延迟动态系统，即 EQIR 模型。为了预测疫情爆发，该模型通过随时间变化的确定性和随机参数纳入了传播率、隔离率以及感染者识别方面的变化。此外，所提出的随机模型考虑了人群行为的波动，并同时模拟了不同的疫情爆发场景。该模型的主要新颖之处在于其能够通过随时间变化的确定性和随机假设纳入传播率、潜伏期和隔离率的变化。此模型能够准确追踪疾病从开始到当前状况的发展趋势，并预测各种情况下疫情爆发的未来情况。
在将该模型与伊朗、美国和韩国的实际数据进行拟合的过程中确定了其参数。我们使用基于拉普拉斯变换的方法计算了再生数。数值模拟结果验证了所提出的确定性和随机模型在当前疫情爆发中的有效性和准确性。
对模型参数的论证强调，尽管更严格的威慑性干预措施可以防止再次出现高峰并控制当前疫情爆发，但 COVID-19 的连续筛查方案起着更重要的作用。这意味着对人们进行的诊断测试越多，疾病就能越快得到控制。
