Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
PLoS One. 2022 Sep 1;17(9):e0273906. doi: 10.1371/journal.pone.0273906. eCollection 2022.
Preventive and modeling 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.
预防和建模方法主要基于年龄或职业来应对 COVID-19 大流行，而经常忽略人口接触结构和个体连接性中的异质性的重要性。为了解决这一差距，我们开发了基于 Erdős-Rényi 和幂律度分布的模型，这些模型首先纳入了异质性和连接性的作用，然后可以扩展到对人口统计学特征做出假设。结果表明，人群中个体连接数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。我们的结论是，最有效的策略将取决于人群中个体的基本接触结构和干预的时间。
