Guo Xiaohao, Guo Yichao, Zhao Zeyu, Yang Shiting, Su Yanhua, Zhao Benhua, Chen Tianmu
State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China.
Université de Montpellier, CIRAD, Intertryp, IES, Université de Montpellier-CNRS, Montpellier, France.
Infect Dis Model. 2022 May 24;7(2):196-210. doi: 10.1016/j.idm.2022.05.004. eCollection 2022 Jun.
Computing the basic reproduction number ( ) in deterministic dynamical models is a hot topic and is frequently demanded by researchers in public health. The next-generation methods (NGM) are widely used for such computation, however, the results of NGM are usually not to be the true but only a threshold quantity with little interpretation. In this paper, a definition-based method (DBM) is proposed to solve such a problem.
Start with the definition of , consider different states that one infected individual may develop into, and take expectations. A comparison with NGM has proceeded. Numerical verification is performed using parameters fitted by data of COVID-19 in Hunan Province.
DBM and NGM give identical expressions for single-host models with single-group and interactive of single-host models with multi-groups, while difference arises for models partitioned into subgroups. Numerical verification showed the consistencies and differences between DBM and NGM, which supports the conclusion that derived by DBM with true epidemiological interpretations are better.
DBM is more suitable for single-host models, especially for models partitioned into subgroups. However, for multi-host dynamic models where the true is failed to define, we may turn to the NGM for the threshold .
在确定性动力学模型中计算基本再生数（ ）是一个热门话题，也是公共卫生领域研究人员经常要求的。下一代方法（NGM）被广泛用于此类计算，然而，NGM的结果通常不是真正的 ，而只是一个几乎没有解释意义的阈值量。本文提出了一种基于定义的方法（DBM）来解决此类问题。
从 的定义出发，考虑一个感染个体可能发展成的不同状态，并取期望值。与NGM进行了比较。使用湖南省新冠肺炎数据拟合的参数进行了数值验证。
对于单宿主单组模型和单宿主多组交互模型，DBM和NGM给出了相同的表达式，而对于划分为子组的模型则出现了差异。数值验证显示了DBM和NGM之间的一致性和差异，支持了由DBM得出的具有真实流行病学解释的 更好的结论。
DBM更适用于单宿主模型，特别是对于划分为子组的模型。然而，对于无法定义真正的 的多宿主动态模型，我们可以转向NGM来获取阈值 。
