Markovič Rene, Šterk Marko, Marhl Marko, Perc Matjaž, Gosak Marko
Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia.
Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia.
Results Phys. 2021 Jul;26:104433. doi: 10.1016/j.rinp.2021.104433. Epub 2021 Jun 8.
We propose and study an epidemiological model on a social network that takes into account heterogeneity of the population and different vaccination strategies. In particular, we study how the COVID-19 epidemics evolves and how it is contained by different vaccination scenarios by taking into account data showing that older people, as well as individuals with comorbidities and poor metabolic health, and people coming from economically depressed areas with lower quality of life in general, are more likely to develop severe COVID-19 symptoms, and quicker loss of immunity and are therefore more prone to reinfection. Our results reveal that the structure and the spatial arrangement of subpopulations are important epidemiological determinants. In a healthier society the disease spreads more rapidly but the consequences are less disastrous as in a society with more prevalent chronic comorbidities. If individuals with poor health are segregated within one community, the epidemic outcome is less favorable. Moreover, we show that, contrary to currently widely adopted vaccination policies, prioritizing elderly and other higher-risk groups is beneficial only if the supply of vaccine is high. If, however, the vaccination availability is limited, and if the demographic distribution across the social network is homogeneous, better epidemic outcomes are achieved if healthy people are vaccinated first. Only when higher-risk groups are segregated, like in elderly homes, their prioritization will lead to lower COVID-19 related deaths. Accordingly, young and healthy individuals should view vaccine uptake as not only protecting them, but perhaps even more so protecting the more vulnerable socio-demographic groups.
我们提出并研究了一个基于社交网络的流行病学模型，该模型考虑了人群的异质性和不同的疫苗接种策略。具体而言，我们通过考虑以下数据来研究新冠疫情的演变以及不同疫苗接种方案如何控制疫情：老年人、患有合并症和代谢健康状况不佳的个体，以及一般来自经济萧条地区、生活质量较低的人群，更有可能出现严重的新冠症状，免疫力丧失更快，因此更容易再次感染。我们的结果表明，亚人群的结构和空间分布是重要的流行病学决定因素。在一个更健康的社会中，疾病传播得更快，但后果不像在慢性合并症更普遍的社会中那样灾难性。如果健康状况不佳的个体被隔离在一个社区内，疫情结果就不太有利。此外，我们表明，与目前广泛采用的疫苗接种政策相反，只有在疫苗供应充足的情况下，优先考虑老年人和其他高风险群体才是有益的。然而，如果疫苗供应有限，并且社交网络中的人口分布是均匀的，那么首先为健康人群接种疫苗会取得更好的疫情防控效果。只有当高风险群体被隔离时，比如在养老院中，优先为他们接种疫苗才会导致与新冠相关的死亡人数减少。因此，年轻健康的个体应该将接种疫苗视为不仅是保护自己，而且可能更是保护更脆弱的社会人口群体。
