ArticlePreventive veterinary medicine2012
Adding the spatial dimension to the social network analysis of an epidemic: investigation of the 2007 outbreak of equine influenza in Australia.
Article in Preventive veterinary medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Attitudes Toward the Ethics of Research Using Social Media: A Systematic Review.Journal of medical Internet research · 2017Pooled it
- What happened after the epidemic? Equine influenza surveillance sheds light on sources and seasonal risk in the United Kingdom.Equine veterinary journal · 2026Article
- Identification of persistent clusters and temporal trends of glanders in horses throughout Brazil.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2025Article
- Social Network Analysis of Ebola Virus Disease During the 2014 Outbreak in Sukudu, Sierra Leone.Open forum infectious diseases · 2022Article
- Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.Transboundary and emerging diseases · 2022Article
- Analyzing Spatial Dependency of the 2016-2017 Korean HPAI Outbreak to Determine the Effective Culling Radius.International journal of environmental research and public health · 2021Article
- Effects of Spatial Characteristics on the Spread of the Highly Pathogenic Avian Influenza (HPAI) in Korea.International journal of environmental research and public health · 2021Article
- Social structure defines spatial transmission of African swine fever in wild boar.Journal of the Royal Society, Interface · 2021Article
- Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020).Journal of environmental health science & engineering · 2020Article
- Quantitative Risk Assessment for the Introduction of Bovine Leukemia Virus-Infected Cattle Using a Cattle Movement Network Analysis.Pathogens (Basel, Switzerland) · 2020Article
- The Relationship between the Migrant Population's Migration Network and the Risk of COVID-19 Transmission in China-Empirical Analysis and Prediction in Prefecture-Level Cities.International journal of environmental research and public health · 2020Article
- Epidemic investigations within an arm's reach - role of google maps during an epidemic outbreak.Health and technology · 2020Article
- Descriptive analysis of horse movement networks during the 2015 equestrian season in Ontario, Canada.PloS one · 2019Article
- VetCompass Australia: A National Big Data Collection System for Veterinary Science.Animals : an open access journal from MDPI · 2017Article
- Using heterogeneity in the population structure of U.S. swine farms to compare transmission models for porcine epidemic diarrhoea.Scientific reports · 2016Article
- Spatial and temporal epidemiological analysis in the Big Data era.Preventive veterinary medicine · 2015Review
- Flying-fox species density--a spatial risk factor for Hendra virus infection in horses in eastern Australia.PloS one · 2014Article
- Risk map and spatial determinants of pancreas disease in the marine phase of Norwegian Atlantic salmon farming sites.BMC veterinary research · 2012Article
- The influence of meteorology on the spread of influenza: survival analysis of an equine influenza (A/H3N8) outbreak.PloS one · 2012Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Equine influenza is a highly contagious and widespread viral respiratory disease of horses and other equid species, characterised by fever and a harsh dry cough. In 2007, in the first reported outbreak in Australia, the virus spread through the horse populations of two states within 4 months. Most of the geographic spread occurred within the first 10 days and was associated with the movement of infected horses prior to the implementation of movement controls. This study applies social network analysis to describe spread of equine influenza between horse premises infected in the early outbreak period, identifying spread occurring through a contact network and secondary local spatial spread. Social networks were constructed by combining contact-tracing data on horse movements with a distance matrix between all premises holding horses infected within the first 10 days of the outbreak. These networks were analysed to provide a description of the epidemic, identify premises that were central to disease spread and to estimate the relative proportion of premises infected through infected horse movements and through local spatial spread. We then explored the effect of distance on disease spread by estimating the range of local spread (through direct contact, transmission on fomites and windborne transmission) based on the level of fragmentation in the network and also by directly estimating the shape of the outbreak's spatial transmission kernel. During the first 10 days of this epidemic, 197 horse premises were infected; 70 of these were included in the contact-traced network. Most local spread occurred within 5 km. Local spread was estimated to have occurred up to a distance of 15.3 km - based on the contact-and-proximity network - and at a very low incidence beyond this distance based on the transmission kernel estimate. Of the 70 premises in the contact network, spread to 14 premises (95% CI: 9, 20 premises) was likely to have occurred through local spatial spread from nearby infected premises, suggesting that 28.3% of spread in the early epidemic period was 'network-associated' (95% CI: 25.6, 31.0%). By constructing a 'maximal network' of contact and proximity (based on a distance cut-off of 15.3 km), 44 spatial clusters were described, and the horse movements that initiated infection in these locations were identified. Characteristics of the combined network, incorporating both spatial and underlying contact relationships between infected premises, explained the high rate of spread, the sequence of cluster formation and the widespread dispersal experienced in the early phase of this epidemic. These results can inform outbreak control planning by guiding the imposition of appropriate control zone diameters around infected premises and the targeting of surveillance and interventions.
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