Using multiple data sources to explore disease transmission risk between commercial poultry, backyard poultry, and wild birds in New Zealand.

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.