Caetano Constantino, Angeli Leonardo, Varela-Lasheras Irma, Coletti Pietro, Morgado Luisa, Lima Pedro, Willem Lander, Nunes Baltazar, Hens Niel
Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, 1600-609, Lisbon, Portugal.
Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, 1049-001, Lisbon, Portugal.
Sci Rep. 2024 Dec 28;14(1):30689. doi: 10.1038/s41598-024-76604-6.
In this study, we employed a modeling approach to describe how changes in age-specific epidemiological characteristics, such as behaviour, i.e. contact patterns, susceptibility and infectivity, influence the basic reproduction number , while accounting for heterogeneity in transmission. We computed sensitivity measures related to , that describe the relative contribution of each age group towards overall transmission. Additionally, we proposed a new indicator that provides the expected relative change in the number of new infections, given a public health intervention. Studying the outbreak of COVID-19 in Portugal during March 2020, our results show that the main drivers of transmission were individuals 30-59 years old. Furthermore, by studying the impact of imposed changes in susceptibility and infectivity, our results demonstrate that a 10% decrease in susceptibility for the 30-39 years old results in a incidence reduction after 3 generations of approximately 17% in this age group and 4-6% reduction as an indirect effect in the remaining age groups. The presented methodology provides tools to inform the allocation strategy of mitigation measures in an outbreak of an infectious disease. Its inherent versatility enables the easy incorporation of data specific to various populations, facilitating a comparative analysis of epidemic control effects across different countries.
在本研究中,我们采用一种建模方法来描述特定年龄层的流行病学特征变化,如行为(即接触模式)、易感性和传染性,如何在考虑传播异质性的情况下影响基本再生数 。我们计算了与 相关的敏感性指标,以描述每个年龄组对总体传播的相对贡献。此外,我们提出了一个新指标,该指标给出了在公共卫生干预下新感染病例数的预期相对变化。通过研究2020年3月葡萄牙的新冠疫情爆发情况,我们的结果表明,传播的主要驱动因素是30至59岁的人群。此外,通过研究易感性和传染性的人为改变所产生的影响,我们的结果表明,30至39岁人群的易感性降低10%,会导致该年龄组在三代之后发病率降低约17%,其余年龄组作为间接影响发病率降低4 - 6%。本文提出的方法为传染病爆发时缓解措施的分配策略提供了参考工具。其固有的通用性能够轻松纳入不同人群的特定数据,便于对不同国家的疫情控制效果进行比较分析。