Chen C Y, Ward J P, Xie W B
Department of Applied Mathematics, National University of Kaohsiung, Kaohsiung, Taiwan.
Department of Mathematical Sciences, Loughborough University, Loughborough, UK.
Theor Popul Biol. 2019 Apr;126:1-18. doi: 10.1016/j.tpb.2018.08.002. Epub 2018 Aug 27.
In-host mutation of a cross-species infectious disease to a form that is transmissible between humans has resulted with devastating global pandemics in the past. We use simple mathematical models to describe this process with the aim to better understand the emergence of an epidemic resulting from such a mutation and the extent of measures that are needed to control it. The feared outbreak of a human-human transmissible form of avian influenza leading to a global epidemic is the paradigm for this study. We extend the SIR approach to derive a deterministic and a stochastic formulation to describe the evolution of two classes of susceptible and infected states and a removed state, leading to a system of ordinary differential equations and a stochastic equivalent based on a Markov process. For the deterministic model, the contrasting timescale of the mutation process and disease infectiousness is exploited in two limits using asymptotic analysis in order to determine, in terms of the model parameters, necessary conditions for an epidemic to take place and timescales for the onset of the epidemic, the size and duration of the epidemic and the maximum level of the infected individuals at one time. Furthermore, the basic reproduction number R is determined from asymptotic analysis of a distinguished limit. Comparisons between the deterministic and stochastic model demonstrate that stochasticity has little effect on most aspects of an epidemic, but does have significant impact on its onset particularly for smaller populations and lower mutation rates for representatively large populations. The deterministic model is extended to investigate a range of quarantine and vaccination programmes, whereby in the two asymptotic limits analysed, quantitative estimates on the outcomes and effectiveness of these control measures are established.
跨物种传染病在宿主体内突变为可在人类之间传播的形式,在过去曾引发毁灭性的全球大流行。我们使用简单的数学模型来描述这一过程,旨在更好地理解由这种突变导致的流行病的出现以及控制它所需措施的程度。令人担忧的可在人际传播的禽流感形式引发全球大流行,就是本研究的范例。我们扩展了SIR方法,推导出确定性和随机性公式,以描述两类易感和感染状态以及一个移除状态的演变,从而得到一个常微分方程组和一个基于马尔可夫过程的随机等价模型。对于确定性模型,利用渐近分析在两个极限情况下探讨突变过程和疾病传染性的对比时间尺度,以便根据模型参数确定流行病发生的必要条件、流行病开始的时间尺度、流行病的规模和持续时间以及某一时刻感染个体的最大数量。此外,通过对一个特殊极限的渐近分析确定基本再生数R。确定性模型与随机模型的比较表明,随机性对流行病的大多数方面影响不大,但对其开始有显著影响,特别是对于较小规模的人群以及代表性大规模人群中较低的突变率。扩展确定性模型以研究一系列检疫和疫苗接种计划,由此在分析的两个渐近极限情况下,对这些控制措施的结果和有效性进行定量估计。