[Source: Epidemics, full text: (LINK). Abstract, edited.]
Epidemics - Available online 15 March 2013
Implications of within-farm transmission for network dynamics: consequences for the spread of avian influenza
Sema Nickbakhsha, Louise Matthewsa, Jennifer E. Dentb, Giles T. Innocentc, Mark E. Arnoldd, Stuart W.J. Reide, Rowland R. Kaoa
a Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Bearsden Road, Scotland, G61 1QH, UK.b Population Health Research Centre, Division of Population Health Sciences and Education, St. George's University of London, Cranmer Terrace, London, SW17 0RE, UK.c Biomathematics & Statistics Scotland (BioSS), The King's Buildings, Edinburgh, EH9 3JZ, UK.d AHVLA Sutton Bonington, The Elms, College Road, Sutton Bonington, Loughborough, LE12 5RB, UK.e Royal Veterinary College, University of London, Hawkshead Lane, North Mimms, Hatfield, Hertforshire, AL9 7TA, UK
Corresponding author. Tel.: ++44 1413305717.
Received 19 September 2012 - Revised 21 February 2013 - Accepted 4 March 2013 - Available online 15 March 2013
The importance of considering coupled interactions across multiple population scales for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry has not previously been studied. 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.
Keywords: Mathematical modelling; Social network data; Poultry