Oshinubi Kayode, Fougère Cécile, Demongeot Jacques
Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France.
Infect Dis Rep. 2022 Apr 25;14(3):321-340. doi: 10.3390/idr14030038.
The end of the acute phase of the COVID-19 pandemic is near in some countries as declared by World Health Organization (WHO) in January 2022 based on some studies in Europe and South Africa despite unequal distribution of vaccines to combat the disease spread globally. The heterogeneity in individual age and the reaction to biological and environmental changes that has been observed in COVID-19 dynamics in terms of different reaction to vaccination by age group, severity of infection per age group, hospitalization and Intensive Care Unit (ICU) records show different patterns, and hence, it is important to improve mathematical models for COVID-19 pandemic prediction to account for different proportions of ages in the population, which is a major factor in epidemic history. We aim in this paper to estimate, using the Usher model, the lifespan loss due to viral infection and ageing which could result in pathological events such as infectious diseases. Exploiting epidemiology and demographic data firstly from Cameroon and then from some other countries, we described the ageing in the COVID-19 outbreak in human populations and performed a graphical representation of the proportion of sensitivity of some of the model parameters which we varied. The result shows a coherence between the orders of magnitude of the calculated and observed incidence numbers during the epidemic wave, which constitutes a semi-quantitative validation of the mathematical modelling approach at the population level. To conclude, the age heterogeneity of the populations involved in the COVID-19 outbreak needs the consideration of models in age groups with specific susceptibilities to infection.
世界卫生组织(WHO)于2022年1月根据欧洲和南非的一些研究宣布,在一些国家,新冠疫情急性期即将结束,尽管全球抗击疫情的疫苗分配不均。在新冠疫情动态中,个体年龄的异质性以及对生物和环境变化的反应,表现为不同年龄组对疫苗接种的不同反应、各年龄组的感染严重程度、住院情况和重症监护病房(ICU)记录呈现出不同模式。因此,改进新冠疫情预测的数学模型以考虑人口中不同年龄比例非常重要,这是疫情历史中的一个主要因素。在本文中,我们旨在使用厄舍模型估计病毒感染和衰老导致的寿命损失,这可能会引发诸如传染病等病理事件。我们首先利用喀麦隆的流行病学和人口统计数据,然后利用其他一些国家的数据,描述了新冠疫情爆发中人群的老龄化情况,并对我们所改变的一些模型参数的敏感性比例进行了图形表示。结果表明,疫情波期间计算出的发病率与观察到的发病率在数量级上具有一致性,这构成了在人群层面上对数学建模方法的半定量验证。总之,新冠疫情爆发所涉及人群的年龄异质性需要考虑具有特定感染易感性的年龄组模型。