Harris Catriona M, Travis Justin M J, Harwood John
Sea Mammal Research Unit, University of St. Andrews, St Andrews, Fife, United Kingdom.
PLoS One. 2008 Jul 16;3(7):e2710. doi: 10.1371/journal.pone.0002710.
Outbreaks of phocine distemper virus (PDV) in Europe during 1988 and 2002 were responsible for the death of around 23,000 and 30,000 harbour seals, respectively. These epidemics, particularly the one in 2002, provided an unusual opportunity to estimate epidemic parameters for a wildlife disease. There were marked regional differences in the values of some parameters both within and between epidemics.
We used an individual-based model of seal movement that allowed us to incorporate realistic representations of space, time and animal behaviour into a traditional epidemiological modelling framework. We explored the potential influence of a range of ecological (foraging trip duration, time of epidemic onset, population size) and epidemiological (length of infectious period, contact rate between infectious and susceptible individuals, case mortality) parameters on four readily-measurable epidemic characteristics (number of dead individuals, duration of epidemic, peak mortality date and prevalence) and on the probability that an epidemic would occur in a particular region. We analysed the outputs as if they were the results of a series of virtual experiments, using Generalised Linear Modelling. All six variables had a significant effect on the probability that an epidemic would be recognised as an unusual mortality event by human observers.
Regional and temporal variation in contact rate was the most likely cause of the observed differences between the two epidemics. This variation could be a consequence of differences in the way individuals divide their time between land and sea at different times of the year.
1988年和2002年欧洲爆发的海豹瘟热病毒(PDV)分别导致约23,000只和30,000只港湾海豹死亡。这些疫情,尤其是2002年的疫情,为估计野生动物疾病的流行参数提供了一个难得的机会。在疫情内部和之间,某些参数的值存在明显的区域差异。
我们使用了一个基于个体的海豹移动模型,使我们能够将空间、时间和动物行为的真实表征纳入传统的流行病学建模框架。我们探讨了一系列生态参数(觅食行程持续时间、疫情开始时间、种群规模)和流行病学参数(感染期长度、感染个体与易感个体之间的接触率、病例死亡率)对四个易于测量的流行特征(死亡个体数量、疫情持续时间、死亡峰值日期和患病率)以及特定区域发生疫情的概率的潜在影响。我们使用广义线性模型分析这些输出,就好像它们是一系列虚拟实验的结果一样。所有六个变量对疫情被人类观察者识别为异常死亡事件的概率都有显著影响。
接触率的区域和时间变化最有可能是观察到的两次疫情之间差异的原因。这种变化可能是由于个体在一年中不同时间在陆地和海洋之间分配时间的方式不同所致。