Pina V Miró, Nava-Trejo J, Tóbiás A, Nzabarushimana E, González-Casanova A, González-Casanova I
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain.
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
medRxiv. 2021 May 25:2021.03.11.21253348. doi: 10.1101/2021.03.11.21253348.
Preventive and modelling approaches to address the COVID-19 pandemic have been primarily based on the age or occupation, and often disregard the importance of heterogeneity in population contact structure and individual connectivity. To address this gap, we developed models based on Erdős-Rényi and a power law degree distribution that first incorporate the role of heterogeneity and connectivity and then can be expanded to make assumptions about demographic characteristics. Results demonstrate that variations in the number of connections of individuals within a population modify the impact of public health interventions such as lockdown or vaccination approaches. We conclude that the most effective strategy will vary depending on the underlying contact structure of individuals within a population and on timing of the interventions.
应对新冠疫情的预防和建模方法主要基于年龄或职业，往往忽视了人口接触结构和个体连通性异质性的重要性。为了弥补这一差距，我们基于厄多斯-雷尼模型和幂律度分布开发了模型，这些模型首先纳入了异质性和连通性的作用，然后可以扩展以对人口特征做出假设。结果表明，人群中个体连接数量的变化会改变封锁或疫苗接种等公共卫生干预措施的影响。我们得出结论，最有效的策略将因人群中个体的潜在接触结构以及干预措施的时机而异。
