School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China.
Department of Mathematics, Purdue University, West Lafayette, IN, 47907, USA.
Bull Math Biol. 2017 Sep;79(9):2149-2173. doi: 10.1007/s11538-017-0324-z. Epub 2017 Jul 18.
Many mathematical models for the disease transmission dynamics of Ebola have been developed and studied, particularly during and after the 2014 outbreak in West Africa. Most of these models are systems of ordinary differential equations (ODEs). One of the common assumptions made in these ODE models is that the duration of disease stages, such as latent and infectious periods, follows an exponential distribution. Gamma distributions have also been used in some of these models. It has been demonstrated that, when the models are used to evaluate disease control strategies such as quarantine or isolation, the models with exponential and Gamma distribution assumptions may generate contradictory results (Feng et al. in Bull Math Biol 69(5):1511-1536, 2007). Several Ebola models are considered in this paper with various stage distributions, including exponential, Gamma and arbitrary distributions. These models are used to evaluate control strategies such as isolation (or hospitalization) and timely burial and to identify potential discrepancies between the results from models with exponential and Gamma distributions.
已经开发和研究了许多用于埃博拉疾病传播动力学的数学模型,特别是在 2014 年西非疫情期间和之后。这些模型大多数是常微分方程(ODE)系统。在这些 ODE 模型中,常见的假设之一是疾病阶段(如潜伏期和传染期)的持续时间遵循指数分布。在这些模型中的一些模型中也使用了伽马分布。已经证明,当使用这些模型评估疾病控制策略(如隔离或隔离)时,具有指数和伽马分布假设的模型可能会产生矛盾的结果(Feng 等人,Bull Math Biol 69(5):1511-1536, 2007)。本文考虑了几种具有不同阶段分布的埃博拉模型,包括指数、伽马和任意分布。这些模型用于评估隔离(或住院)和及时埋葬等控制策略,并确定具有指数和伽马分布的模型的结果之间的潜在差异。