Massey University School of Veterinary Science, Palmerston North, 4442, New Zealand.
Diagnostic and Surveillance Services Directorate, Ministry for Primary Industries, Wellington, 6140, New Zealand.
Prev Vet Med. 2021 May;190:105327. doi: 10.1016/j.prevetmed.2021.105327. Epub 2021 Mar 11.
The movements of backyard poultry and wild bird populations are known to pose a disease risk to the commercial poultry industry. However, it is often difficult to estimate this risk due to the lack of accurate data on the numbers, locations, and movement patterns of these populations. The main aim of this study was to evaluate the use of three different data sources when investigating disease transmission risk between poultry populations in New Zealand including (1) cross-sectional survey data looking at the movement of goods and services within the commercial poultry industry, (2) backyard poultry sales data from the online auction site TradeMe®, and (3) citizen science data from the wild bird monitoring project eBird. The cross-sectional survey data and backyard poultry sales data were transformed into network graphs showing the connectivity of commercial and backyard poultry producers across different geographical regions. The backyard poultry network was also used to parameterise a Susceptible-Infectious (SI) simulation model to explore the behaviour of potential disease outbreaks. The citizen science data was used to create an additional map showing the spatial distribution of wild bird observations across New Zealand. To explore the potential for diseases to spread between each population, maps were combined into bivariate choropleth maps showing the overlap between movements within the commercial poultry industry, backyard poultry trades and, wild bird observations. Network analysis revealed that the commercial poultry network was highly connected with geographical clustering around the urban centres of Auckland, New Plymouth and Christchurch. The backyard poultry network was also a highly active trade network and displayed similar geographic clustering to the commercial network. In the disease simulation models, the high connectivity resulted in all suburbs becoming infected in 96.4 % of the SI simulations. Analysis of the eBird data included reports of over 80 species; the majority of which were identified as coastal seabirds or wading birds that showed little overlap with either backyard or commercial poultry. Overall, our study findings highlight how the spatial patterns of trading activity within the commercial poultry industry, alongside the movement of backyard poultry and wild birds, have the potential to contribute significantly to the spread of diseases between these populations. However, it is clear that in order to fully understand this risk landscape, further data integration is needed; including the use of additional datasets that have further information on critical variables such as environmental factors.
后院家禽和野生鸟类的活动已知会对商业家禽业构成疾病风险。然而，由于缺乏关于这些种群数量、位置和活动模式的准确数据，通常很难估计这种风险。本研究的主要目的是评估在新西兰调查家禽种群之间疾病传播风险时使用三种不同数据源的情况，包括（1）横断面调查数据，调查商业家禽业内部货物和服务的流动情况，（2）在线拍卖网站 TradeMe®的后院家禽销售数据，以及（3）野生鸟类监测项目 eBird 的公民科学数据。横断面调查数据和后院家禽销售数据被转化为网络图，显示了不同地理区域内商业和后院家禽生产者的连接性。后院家禽网络还被用于参数化一个易感-感染（SI）模拟模型，以探索潜在疾病爆发的行为。公民科学数据被用于创建一个额外的地图，显示新西兰各地野生鸟类观测的空间分布。为了探索疾病在每个种群之间传播的潜力，将地图组合成双变量专题地图，显示商业家禽行业内的运动、后院家禽交易和野生鸟类观察之间的重叠。网络分析表明，商业家禽网络高度连接，围绕奥克兰、新普利茅斯和克赖斯特彻奇等城市中心呈地理聚集。后院家禽网络也是一个高度活跃的贸易网络，显示出与商业网络相似的地理聚集。在疾病模拟模型中，高连接性导致在 96.4%的 SI 模拟中所有郊区都被感染。对 eBird 数据的分析包括超过 80 个物种的报告；其中大多数被确定为沿海海鸟或涉禽，与后院或商业家禽几乎没有重叠。总的来说，我们的研究结果强调了商业家禽业内部贸易活动的空间模式，以及后院家禽和野生鸟类的移动，如何有可能显著促进这些种群之间的疾病传播。然而，很明显，为了充分了解这种风险状况，需要进一步进行数据整合；包括使用具有更多关于环境因素等关键变量信息的额外数据集。
