Hudson Emily G, Brookes Victoria J, Dürr Salome, Ward Michael P
Sydney School of Veterinary Science, The University of Sydney, Camden, Australia.
Sydney School of Veterinary Science, The University of Sydney, Camden, Australia.
Prev Vet Med. 2017 Oct 1;146:52-60. doi: 10.1016/j.prevetmed.2017.07.010. Epub 2017 Jul 20.
Although Australia is canine rabies free, the Northern Peninsula Area (NPA), Queensland and other northern Australian communities are at risk of an incursion due to proximity to rabies infected islands of Indonesia and existing disease spread pathways. Northern Australia also has large populations of free-roaming domestic dogs, presenting a risk of rabies establishment and maintenance should an incursion occur. Agent-based rabies spread models are being used to predict potential outbreak size and identify effective control strategies to aid incursion preparedness. A key component of these models is knowledge of dog roaming patterns to inform contact rates. However, a comprehensive understanding of how dogs utilise their environment and the heterogeneity of their movements to estimate contact rates is lacking. Using a novel simulation approach - and GPS data collected from 21 free-roaming domestic dogs in the NPA in 2014 and 2016 - we characterised the roaming patterns within this dog population. Multiple subsets from each individual dog's GPS dataset were selected representing different monitoring durations and a utilisation distribution (UD) and derived core (50%) and extended (95%) home ranges (HR) were estimated for each duration. Three roaming patterns were identified, based on changes in mean HR over increased monitoring durations, supported by assessment of maps of daily UDs of each dog. Stay-at-home dogs consolidated their HR around their owner's residence, resulting in a decrease in mean HR (both core and extended) as monitoring duration increased (median peak core and extended HR 0.336 and 3.696ha, respectively). Roamer dogs consolidated their core HR but their extended HR increased with longer monitoring durations, suggesting that their roaming patterns based on place of residence were more variable (median peak core and extended HR 0.391 and 6.049ha, respectively). Explorer dogs demonstrated large variability in their roaming patterns, with both core and extended HR increasing as monitoring duration increased (median peak core and extended HR 0.650 and 9.520ha, respectively). These findings are likely driven by multiple factors that have not been further investigated within this study. Different roaming patterns suggest heterogeneous contact rates between dogs in this population. These findings will be incorporated into disease-spread modelling to more realistically represent roaming patterns and improve model predictions.
尽管澳大利亚没有犬类狂犬病，但由于靠近印度尼西亚有狂犬病感染的岛屿以及现有的疾病传播途径，昆士兰州的北半岛地区（NPA）和澳大利亚北部的其他社区面临狂犬病入侵的风险。澳大利亚北部还有大量自由放养的家犬，如果发生狂犬病入侵，就存在狂犬病传播和持续存在的风险。基于主体的狂犬病传播模型正被用于预测潜在疫情规模，并确定有效的控制策略，以帮助做好应对狂犬病入侵的准备。这些模型的一个关键组成部分是了解犬类的漫游模式，以便确定接触率。然而，目前缺乏对犬类如何利用其环境以及其活动的异质性来估计接触率的全面理解。我们采用一种新颖的模拟方法，并利用2014年和2016年从NPA的21只自由放养家犬收集的GPS数据，对该犬类群体的漫游模式进行了特征描述。从每只犬的GPS数据集中选择多个子集，代表不同的监测持续时间，并估计每个持续时间的利用分布（UD）以及核心（50%）和扩展（95%）家域（HR）。根据平均家域在监测持续时间增加时的变化，确定了三种漫游模式，并通过对每只犬的每日利用分布地图的评估得到支持。居家犬将其家域集中在主人住所周围，导致随着监测持续时间的增加，平均家域（核心和扩展家域）减小（核心和扩展家域的峰值中位数分别为0.336公顷和3.696公顷）。漫游犬巩固了其核心家域，但随着监测持续时间延长，其扩展家域增加，这表明它们基于居住地的漫游模式更具变异性（核心和扩展家域的峰值中位数分别为0.391公顷和6.049公顷）。探索犬的漫游模式表现出很大的变异性, 随着监测持续时间的增加，核心和扩展家域均增加（核心和扩展家域的峰值中位数分别为0.650公顷和9.520公顷）。这些发现可能是由多种因素驱动的，本研究未对这些因素进行进一步调查。不同的漫游模式表明该群体中犬类之间的接触率存在异质性。这些发现将被纳入疾病传播模型，以更真实地反映漫游模式并改善模型预测。
