Recent network evolution increases the potential for large epidemics in the British cattle population

Robinson, Susan E.; Everett, Martin G.; Christley, Robert M.

doi:10.1098/rsif.2007.0214

Cited by 91 publications

(116 citation statements)

References 18 publications

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“…Interface 13: 20160166 variation in the degree distribution, such as small-world networks, may have insufficient variation to discern differences in connectivity among infected and uninfected nodes. Given that many real-world networks have small-world properties [40,[51][52][53][54], it is important to take into account network structure and pathogen prevalence when selecting appropriate statistical methods. Our results suggest that the k-test performs well across a diversity of scenarios.…”

Section: Discussionmentioning

confidence: 99%

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

VanderWaal

Enns

Picasso

et al. 2016

J. R. Soc. Interface.

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Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.

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Section: Discussionmentioning

confidence: 99%

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

VanderWaal

Enns

Picasso

et al. 2016

J. R. Soc. Interface.

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“…Canada, Britain, Denmark, Sweden and some other countries' research results showed that livestock and poultry movement plays an important role in the spread of animal disease. The researchers also suggested that in the management of animal movements we should pay attention to the following points: 1) Mixed degree, which means the mixed level of directly infected animals and indirectly infected animals; 2) Dispersion degree, that is, one buys infected animals from another seller, then the infected animals involved in trading become scattered in the whole group of animals; 3) Complicated sources degree, namely, the buyer will buy animals from a number of sources [1][2][3][4][5][6][7][8]. However, the relevant research in China is scarce [9].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the lack of professional knowledge and effective means to assess disease risks in the process of cattle movements, most farmers can't catch the key points in disease prevention, and constantly introduce new disease or exogenous disease, which makes a serious threat to livestock health and huge loss to production [1,7,9,[10][11][12]. In order to reduce the risks for spread of disease in cattle movements, through describing the cattle markets and analyzing the risks in the process of cattle movements, this article wanted to provide the advice about the prevention of animal disease to farmers, practitioners and other related staffs in cattle movements and the reference to establish the disease spread mathematical model and dynamic model [3].…”

Section: Introductionmentioning

confidence: 99%

The patterns and risks for disease spreading of cattle movement in China

Sun¹,

Liu²,

Wang³

et al. 2013

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In order to reduce the risks for the spread of disease in cattle movements, we investigated China's cattle breeding and movement pattern, analyzed risk factors of disease infection caused by long-distance movement, explored the relevant risk management measures and conjectured the direction of cattle movement based on the regional distribution of cattle inventory and beef price. We also constructed a market value chain in live cattle movements and qualitatively analyzed the risks for unapparent infection in the process of movement. Meanwhile, we put forward a long-term policy of reducing the risks for unapparent infection and animal health supervision measures.

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“…From the Cattle Tracing System (18) we have individual records of the movement of cattle between the 150,000 farms, markets, and slaughterhouses in Great Britain (SI Text). Both of these movement datasets can be conceptualized as relatively sparse networks; the analysis of such networks yields a wide range of important information on the pattern of movements and the structure of the populations (19,20). Here, however, we are interested in how such movements lead to the percolation of infection through the population, and we show that great care is needed if realistic rates of spread are to be predicted.…”

mentioning

confidence: 99%

Individual identity and movement networks for disease metapopulations

Keeling

Danon

Vernon

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

138

151

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The theory of networks has had a huge impact in both the physical and life sciences, shaping our understanding of the interaction between multiple elements in complex systems. In particular, networks have been extensively used in predicting the spread of infectious diseases where individuals, or populations of individuals, interact with a limited set of others-defining the network through which the disease can spread. Here for such disease models we consider three assumptions for capturing the network of movements between populations, and focus on two applied problems supported by detailed data from Great Britain: the commuter movement of workers between local areas (wards) and the permanent movement of cattle between farms. For such metapopulation networks, we show that the identity of individuals responsible for making network connections can have a significant impact on the infection dynamics, with clear implications for detailed public health and veterinary applications.etworks are now a well-understood and powerful scientific tool. When the number of interactions between elements or individuals is relatively low, then networks offer an intuitive means of capturing and describing the structure of such interactions. Examples abound, from computer and Internet connections (1) to metabolic networks (2) and food webs (3), from transportation patterns (4) to actors within the same movies (5)-in each of these, the theory of networks has provided valuable insights into how such interactions are structured and has hinted at deeper underlying patterns. When interactions describe connections between people, then network theory is often used to explore the implications of disease spread through the population (6). However, data on human-to-human contacts through which infections can spread are rare, generally being isolated to small populations that have been sampled with detailed questionnaires-of these, networks of sexual encounters, such as the studies in Colorado Springs (7) and Manitoba (8), are probably the best examples. Another common source of network interaction comes from the movement of individuals between populations, who could potentially carry infection with them. With concerns over novel or reemerging infections, such networks of movements often form the core of mathematical models that examine the spatiotemporal spread of infections and the associated public health implications. Examples include work movements for the spread of seasonal influenza in the United States (9, 10); aviation traffic for the spread of smallpox (11), sudden acute respiratory syndrome (12), and general epidemics (13); commuting movements for deliberate release of smallpox (14); and trade for the 1918-1919 influenza pandemic (15); a more systematic review of techniques and applications is available from Riley (16). The sensitivity of the epidemic to the topological structure of the movement network has already been shown (13, 17); in contrast here, through detailed simulation, we address how such movement networks should be model...

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Recent network evolution increases the potential for large epidemics in the British cattle population

Cited by 91 publications

References 18 publications

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

The patterns and risks for disease spreading of cattle movement in China

Individual identity and movement networks for disease metapopulations

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