Abstract
The importance of considering coupled interactions across multiple population scales has not previously been studied for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry. By simulating the within-flock transmission of HPAI using a deterministic S-E-I-R model, and by incorporating an additional environmental class representing infectious faeces, we tracked the build-up of infectious faeces within a poultry house over time. A measure of the transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with data describing the daily on-farm visit schedules for a major British catching company. Larger flocks tended to have greater levels of these catching-team visits. However, where density-dependent contact was assumed, faster outbreak detection (according to an assumed mortality threshold) led to a decreased opportunity for catching-team visits to coincide with an outbreak. For this reason, maximum TR-levels were found for mid-range flock sizes (~25,000–35,000 birds). When assessing all factors simultaneously using multivariable linear regression on the simulated outputs, those related to the pattern of catching-team visits had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network analysis on a further database to inform a measure of between-farm connectivity, we identified a large fraction of farms (28%) that had both a high TR and a high potential impact at the between farm level. Our results have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone, and together with further knowledge of the relative importance of transmission risk and impact, could have implications for improved targeting of control measures.
Highlights
In social network analysis, when applied to epidemiology, it has typically been assumed that links between network nodes are static and represent fixed, persistent contacts during an individual’s infectious period (Huerta and Tsimring, 2002; Keeling, 1999; Meyers et al, 2005)
Using temporally explicit data describing the daily on-farm visit schedules for a major poultry catching company located in England (Dent et al, 2011), we explored the interaction between the within-flock transmission dynamics of highly pathogenic avian influenza (HPAI), measured in continuous time, and temporally explicit catching-team visits, measured in discrete time
As the likely MT triggering HPAI detection in British poultry farms is not known we considered a range of MTs and present results for an intermediate threshold of 0.5%, corresponding to the Dutch recommendation
Summary
In social network analysis, when applied to epidemiology, it has typically been assumed that links between network nodes are static and represent fixed, persistent contacts during an individual’s infectious period (Huerta and Tsimring, 2002; Keeling, 1999; Meyers et al, 2005). Whether it is important to consider these factors in unison depends on the relative rates of change at the farm and network levels (Kao et al, 2007; Ochab and Gora, 2011; Volz and Meyers, 2009). For diseases such as highly pathogenic avian influenza (HPAI), which spreads rapidly at the farm level (i.e. node dynamics) (Bos et al, 2010; Elbers et al, 2004; Yoon et al, 2005), the opportunity for onward spread via epidemiologically relevant industry movements will depend on the timing of these movements
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