Jdid Touria, Benbrahim Mohammed, Kabbaj Mohammed Nabil, Naji Mohamed
Laboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Laboratory of Applied Physics Informatics and Statistics (LPAIS), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Heliyon. 2024 Sep 23;10(19):e38204. doi: 10.1016/j.heliyon.2024.e38204. eCollection 2024 Oct 15.
Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number ( ) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.
compartmental模型已成为强大的计算框架，并在抗击新冠疫情中取得了显著成功。本研究提出了一种基于疫苗接种的新冠病毒传播动力学 compartmental模型。该模型反映了新冠病毒感染的特定阶段，并整合了疫苗接种策略，能够全面分析疫苗接种率如何影响疾病传播。我们采用哈密顿蒙特卡洛（HMC）算法，通过贝叶斯推理方法，将该模型拟合到2021年6月4日至11月26日美国田纳西州的每日新冠确诊病例数据。首先，在模型中排除疫苗接种动态，我们估计了感染、康复和疾病致死率等关键流行病学参数。该分析得出基本再生数（ ）为1.5。其次，我们纳入疫苗接种动态，估计了三种疫苗的接种率：辉瑞和莫德纳疫苗均为每天0.0051，杨森疫苗为每天0.0059。拟合曲线显示，三种疫苗的疫情峰值均有所降低。辉瑞和莫德纳疫苗将峰值从8029例感染病例降至5616例感染病例，而杨森疫苗则将其降至6493例感染病例。通过改变疫苗接种率和疫苗效力对模型进行了模拟。一种效力为95%的高效疫苗，每日接种率为0.006，可使新冠病毒感染病例减半，从8029例降至约4000例。结果还表明，随着用于训练模型的观测数据数量增加，模型对新观测值的预测准确性提高。
