Department Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany.
Friedrich-Loeffler-Institute, Institute of Epidemiology, Greifswald, Germany.
J Anim Ecol. 2019 Nov;88(11):1812-1824. doi: 10.1111/1365-2656.13070. Epub 2019 Aug 21.
Understanding the drivers underlying disease dynamics is still a major challenge in disease ecology, especially in the case of long-term disease persistence. Even though there is a strong consensus that density-dependent factors play an important role for the spread of diseases, the main drivers are still discussed and, more importantly, might differ between invasion and persistence periods. Here, we analysed long-term outbreak data of classical swine fever, an important disease in both wild boar and livestock, prevalent in the wild boar population from 1993 to 2000 in Mecklenburg-Vorpommern, Germany. We report outbreak characteristics and results from generalized linear mixed models to reveal what factors affected infection risk on both the landscape and the individual level. Spatiotemporal outbreak dynamics showed an initial wave-like spread with high incidence during the invasion period followed by a drop of incidence and an increase in seroprevalence during the persistence period. Velocity of spread increased with time during the first year of outbreak and decreased linearly afterwards, being on average 7.6 km per quarter. Landscape- and individual-level analyses of infection risk indicate contrasting seasonal patterns. During the persistence period, infection risk on the landscape level was highest during autumn and winter seasons, probably related to spatial behaviour such as increased long-distance movements and contacts induced by rutting and escaping movements. In contrast, individual-level infection risk peaked in spring, probably related to the concurrent birth season leading to higher densities, and was significantly higher in piglets than in reproductive animals. Our findings highlight that it is important to investigate both individual- and landscape-level patterns of infection risk to understand long-term persistence of wildlife diseases and to guide respective management actions. Furthermore, we highlight that exploring different temporal aggregation of the data helps to reveal important seasonal patterns, which might be masked otherwise.
了解疾病动态的驱动因素仍然是疾病生态学中的一个主要挑战，尤其是在长期疾病持续存在的情况下。尽管人们强烈认为密度依赖因素对疾病的传播起着重要作用，但主要驱动因素仍在讨论中，更重要的是，它们在入侵期和持续期可能不同。在这里，我们分析了 1993 年至 2000 年期间德国梅克伦堡-前波美拉尼亚野猪种群中流行的重要疾病——经典猪瘟的长期爆发数据。我们报告了爆发特征和广义线性混合模型的结果，以揭示哪些因素在景观和个体层面上影响了感染风险。时空爆发动态显示出初始的波浪状传播，入侵期发病率高，随后发病率下降，持续期血清阳性率增加。在爆发的第一年，传播速度随时间增加，此后线性下降，平均每季度 7.6 公里。感染风险的景观和个体水平分析表明存在相反的季节性模式。在持续期，景观水平的感染风险在秋季和冬季最高，可能与空间行为有关，如发情和逃避行为引起的长距离移动和接触增加。相比之下，个体水平的感染风险在春季达到峰值，可能与同期的繁殖季节导致密度增加有关，仔猪的感染风险明显高于繁殖动物。我们的研究结果强调，重要的是要调查个体和景观水平的感染风险模式，以了解野生动物疾病的长期持续存在，并指导相应的管理措施。此外，我们强调探索数据的不同时间聚集有助于揭示重要的季节性模式，否则这些模式可能会被掩盖。
