scholarly journals The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread

2006 ◽  
Vol 3 (10) ◽  
pp. 669-677 ◽  
Author(s):  
Istvan Z Kiss ◽  
Darren M Green ◽  
Rowland R Kao
Keyword(s):  
Great Britain ◽  
Disease Transmission ◽  
Foot And Mouth Disease ◽  
Small World ◽  
Disease Spread ◽  
Contact Networks ◽  
Geographical Regions ◽  
Network Properties ◽  
Path Lengths ◽  
Intense Activity

During the 2001 foot and mouth disease epidemic in the UK, initial dissemination of the disease to widespread geographical regions was attributed to livestock movement, especially of sheep. In response, recording schemes to provide accurate data describing the movement of large livestock in Great Britain (GB) were introduced. Using these data, we reconstruct directed contact networks within the sheep industry and identify key epidemiological properties of these networks. There is clear seasonality in sheep movements, with a peak of intense activity in August and September and an associated high risk of a large epidemic. The high correlation between the in and out degree of nodes favours disease transmission. However, the contact networks were largely dissasortative: highly connected nodes mostly connect to nodes with few contacts, effectively slowing the spread of disease. This is a result of bipartite-like network properties, with most links occurring between highly active markets and less active farms. When comparing sheep movement networks (SMNs) to randomly generated networks with the same number of nodes and node degrees, despite structural differences (such as disassortativity and higher frequency of even path lengths in the SMNs), the characteristic path lengths within the SMNs are close to values computed from the corresponding random networks, showing that SMNs have ‘small-world’-like properties. Using the network properties, we show that targeted biosecurity or surveillance at highly connected nodes would be highly effective in preventing a large and widespread epidemic.

scholarly journals Identification of effective spreaders in contact networks using dynamical influence

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruaridh A. Clark ◽  
Malcolm Macdonald
Keyword(s):  
Disease Transmission ◽  
Average Rate ◽  
Laplacian Matrix ◽  
Disease Spread ◽  
Eigenvector Centrality ◽  
Contact Networks ◽  
Human Contact ◽  
Spread Dynamics ◽  
Definition Of ◽  
High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

scholarly journals Using network properties to predict disease dynamics on human contact networks

2011 ◽  
Vol 278 (1724) ◽  
pp. 3544-3550 ◽  
Author(s):  
Gregory M. Ames ◽  
Dylan B. George ◽  
Christian P. Hampson ◽  
Andrew R. Kanarek ◽  
Cayla D. McBee ◽  
...  
Keyword(s):  
Degree Distribution ◽  
Stochastic Simulations ◽  
Disease Spread ◽  
Contact Structures ◽  
Contact Network ◽  
Contact Networks ◽  
Human Contact ◽  
Differential Equation Models ◽  
Network Properties ◽  
Intermediate Density

Recent studies have increasingly turned to graph theory to model more realistic contact structures that characterize disease spread. Because of the computational demands of these methods, many researchers have sought to use measures of network structure to modify analytically tractable differential equation models. Several of these studies have focused on the degree distribution of the contact network as the basis for their modifications. We show that although degree distribution is sufficient to predict disease behaviour on very sparse or very dense human contact networks, for intermediate density networks we must include information on clustering and path length to accurately predict disease behaviour. Using these three metrics, we were able to explain more than 98 per cent of the variation in endemic disease levels in our stochastic simulations.

scholarly journals Infectious disease transmission and contact networks in wildlife and livestock

2015 ◽  
Vol 370 (1669) ◽  
pp. 20140107 ◽  
Author(s):  
Meggan E. Craft
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Wild Animal ◽  
Disease Spread ◽  
Farmed Fish ◽  
Infectious Disease Transmission ◽  
Network Modelling ◽  
Flexible Tool ◽  
Contact Networks ◽  
Contact Data

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.

scholarly journals Revealing mechanisms of infectious disease transmission through empirical contact networks

2017 ◽  
Author(s):  
Pratha Sah ◽  
Michael Otterstatter ◽  
Stephan T. Leu ◽  
Sivan Leviyang ◽  
Shweta Bansal
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Network Models ◽  
Pathogen Transmission ◽  
Disease Spread ◽  
Contact Networks ◽  
Transmission Mechanisms ◽  
Disease Information ◽  
Crithidia Bombi ◽  
Transmission Pathways

AbstractThe spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling infectious disease dynamics through contact networks is sometimes challenging, however, due to a limited understanding of pathogen transmission routes and infectivity. We developed a novel tool, INoDS (Identifying Network models of infectious Disease Spread) that estimates the predictive power of empirical contact networks to explain observed patterns of infectious disease spread. We show that our method is robust to partially sampled contact networks, incomplete disease information, and enables hypothesis testing on transmission mechanisms. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumble bee colonies and Salmonella in wild Australian sleepy lizard populations. The performance of INoDS in synthetic and complex empirical systems highlights its role in identifying transmission pathways of novel or neglected pathogens, as an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

scholarly journals Predictors of free-roaming domestic dogs' contact network centrality and their relevance for rabies control

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Charlotte Warembourg ◽  
Guillaume Fournié ◽  
Mahamat Fayiz Abakar ◽  
Danilo Alvarez ◽  
Monica Berger-González ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Small World ◽  
Domestic Dogs ◽  
Contact Network ◽  
Potential Contribution ◽  
Dog Ownership ◽  
Contact Patterns ◽  
Rabies Control ◽  
The Social ◽  
Study Sites

AbstractFree roaming domestic dogs (FRDD) are the main vectors for rabies transmission to humans worldwide. To eradicate rabies from a dog population, current recommendations focus on random vaccination with at least 70% coverage. Studies suggest that targeting high-risk subpopulations could reduce the required vaccination coverage, and increase the likelihood of success of elimination campaigns. The centrality of a dog in a contact network can be used as a measure of its potential contribution to disease transmission. Our objectives were to investigate social networks of FRDD in eleven study sites in Chad, Guatemala, Indonesia and Uganda, and to identify characteristics of dogs, and their owners, associated with their centrality in the networks. In all study sites, networks had small-world properties and right-skewed degree distributions, suggesting that vaccinating highly connected dogs would be more effective than random vaccination. Dogs were more connected in rural than urban settings, and the likelihood of contacts was negatively correlated with the distance between dogs’ households. While heterogeneity in dog's connectedness was observed in all networks, factors predicting centrality and likelihood of contacts varied across networks and countries. We therefore hypothesize that the investigated dog and owner characteristics resulted in different contact patterns depending on the social, cultural and economic context. We suggest to invest into understanding of the sociocultural structures impacting dog ownership and thus driving dog ecology, a requirement to assess the potential of targeted vaccination in dog populations.

scholarly journals Livestock movement informs the risk of disease spread in traditional production systems in East Africa

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Divine Ekwem ◽  
Thomas A. Morrison ◽  
Richard Reeve ◽  
Jessica Enright ◽  
Joram Buza ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Production Systems ◽  
Control Strategies ◽  
Herd Size ◽  
Transmission Risk ◽  
Disease Spread ◽  
Transmission Potential ◽  
Spatial And Temporal Scales ◽  
Transmission Distance ◽  
Risk Of Disease

AbstractIn Africa, livestock are important to local and national economies, but their productivity is constrained by infectious diseases. Comprehensive information on livestock movements and contacts is required to devise appropriate disease control strategies; yet, understanding contact risk in systems where herds mix extensively, and where different pathogens can be transmitted at different spatial and temporal scales, remains a major challenge. We deployed Global Positioning System collars on cattle in 52 herds in a traditional agropastoral system in western Serengeti, Tanzania, to understand fine-scale movements and between-herd contacts, and to identify locations of greatest interaction between herds. We examined contact across spatiotemporal scales relevant to different disease transmission scenarios. Daily cattle movements increased with herd size and rainfall. Generally, contact between herds was greatest away from households, during periods with low rainfall and in locations close to dipping points. We demonstrate how movements and contacts affect the risk of disease spread. For example, transmission risk is relatively sensitive to the survival time of different pathogens in the environment, and less sensitive to transmission distance, at least over the range of the spatiotemporal definitions of contacts that we explored. We identify times and locations of greatest disease transmission potential and that could be targeted through tailored control strategies.

scholarly journals Sheep scab spatial distribution: the roles of transmission pathways

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Emily Joanne Nixon ◽  
Ellen Brooks-Pollock ◽  
Richard Wall
Keyword(s):  
Disease Transmission ◽  
Movement Control ◽  
Cost Effective ◽  
Infected Sheep ◽  
Disease Spread ◽  
Psoroptes Ovis ◽  
Long Distance ◽  
Sheep Scab ◽  
The Uk ◽  
The Impact

Abstract Background Ovine psoroptic mange (sheep scab) is a highly pathogenic contagious infection caused by the mite Psoroptes ovis. Following 21 years in which scab was eradicated in the UK, it was inadvertently reintroduced in 1972 and, despite the implementation of a range of control methods, its prevalence increased steadily thereafter. Recent reports of resistance to macrocyclic lactone treatments may further exacerbate control problems. A better understanding of the factors that facilitate its transmission are required to allow improved management of this disease. Transmission of infection occurs within and between contiguous sheep farms via infected sheep-to-sheep or sheep–environment contact and through long-distance movements of infected sheep, such as through markets. Methods A stochastic metapopulation model was used to investigate the impact of different transmission routes on the spatial pattern of outbreaks. A range of model scenarios were considered following the initial infection of a cluster of highly connected contiguous farms. Results Scab spreads between clusters of neighbouring contiguous farms after introduction but when long-distance movements are excluded, infection then self-limits spatially at boundaries where farm connectivity is low. Inclusion of long-distance movements is required to generate the national patterns of disease spread observed. Conclusions Preventing the movement of scab infested sheep through sales and markets is essential for any national management programme. If effective movement control can be implemented, regional control in geographic areas where farm densities are high would allow more focussed cost-effective scab management. Graphical Abstract

scholarly journals Disease transmission by cannibalism: rare event or common occurrence?

2007 ◽  
Vol 274 (1614) ◽  
pp. 1205-1210 ◽  
Author(s):  
Volker H.W Rudolf ◽  
Janis Antonovics
Keyword(s):  
Disease Transmission ◽  
Rare Event ◽  
Disease Spread ◽  
Necessary Condition ◽  
Transmission Route ◽  
Review Of The Literature ◽  
Common Occurrence ◽  
Endemic Diseases ◽  
Pathogen Dynamics ◽  
Transmission Modes

Cannibalism has been documented as a possible disease transmission route in several species, including humans. However, the dynamics resulting from this type of disease transmission are not well understood. Using a theoretical model, we explore how cannibalism (i.e. killing and consumption of dead conspecifics) and intraspecific necrophagy (i.e. consumption of dead conspecifics) affect host–pathogen dynamics. We show that group cannibalism, i.e. shared consumption of victims, is a necessary condition for disease spread by cannibalism in the absence of alternative transmission modes. Thus, endemic diseases transmitted predominantly by cannibalism are likely to be rare, except in social organisms that share conspecific prey. These results are consistent with a review of the literature showing that diseases transmitted by cannibalism are infrequent in animals, even though both cannibalism and trophic transmission are very common.

scholarly journals Human-induced changes in habitat preference by capybaras (Hydrochoerus hydrochaeris) and their potential effect on zoonotic disease transmission

2020 ◽  
Author(s):  
Thiago C. Dias ◽  
Jared A. Stabach ◽  
Qiongyu Huang ◽  
Marcelo B. Labruna ◽  
Peter Leimgruber ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Disease Transmission ◽  
Resource Selection ◽  
Spotted Fever ◽  
Space Use ◽  
Zoonotic Disease ◽  
Disease Spread ◽  
Selection Strategies ◽  
Hydrochoerus Hydrochaeris ◽  
Natural Landscapes

AbstractHuman activities are changing landscape structure and function globally, affecting wildlife space use, and ultimately increasing human-wildlife conflicts and zoonotic disease spread. Capybara (Hydrochoerus hydrochaeris) is a conflict species that has been implicated in the spread and amplification of the most lethal tick-borne disease in the world, the Brazilian spotted fever (BSF). Even though essential to understand the link between capybaras, ticks and the BSF, many knowledge gaps still exist regarding the effects of human disturbance in capybara space use. Here, we analyzed diurnal and nocturnal habitat selection strategies of capybaras across natural and human-modified landscapes using resource selection functions (RSF). Selection for forested habitats was high across human- modified landscapes, mainly during day- periods. Across natural landscapes, capybaras avoided forests during both day- and night periods. Water was consistently selected across both landscapes, during day- and nighttime. This variable was also the most important in predicting capybara habitat selection across natural landscapes. Capybaras showed slightly higher preferences for areas near grasses/shrubs across natural landscapes, and this variable was the most important in predicting capybara habitat selection across human-modified landscapes. Our results demonstrate human-driven variation in habitat selection strategies by capybaras. This behavioral adjustment across human-modified landscapes may be related to BSF epidemiology.

scholarly journals Emergent properties in microbiome networks reveal the anthropogenic disturbance of farming practices in vineyard soil fungal communities

2020 ◽  
Author(s):  
Rüdiger Ortiz-Álvarez ◽  
Hector Ortega-Arranz ◽  
Vicente J. Ontiveros ◽  
Charles Ravarani ◽  
Alberto Acedo ◽  
...  
Keyword(s):  
Small World ◽  
Fungal Communities ◽  
Local Network ◽  
Emergent Properties ◽  
Farming Practices ◽  
Natural Systems ◽  
Vineyard Soil ◽  
Global Agriculture ◽  
Network Properties ◽  
The Impact

AbstractAgro-ecosystems are human-managed natural systems, and therefore are subject to generalized ecological rules. A deeper understanding of the factors impacting on the biotic component of ecosystem stability is needed for promoting the sustainability and productivity of global agriculture. Here we propose a method to determine ecological emergent properties through the inference of network properties in local microbial communities, and to use them as biomarkers of the anthropogenic impact of different farming practices on vineyard soil ecosystem functioning. In a dataset of 350 vineyard soil samples from USA and Spain we observed that fungal communities ranged from random to small-world network arrangements with differential levels of niche specialization. Some of the network properties studied were strongly correlated, defining patterns of ecological emergent properties that are influenced by the intensification level of the crop management. Low-intervention practices (from organic to biodynamic approaches) promoted densely clustered networks, describing an equilibrium state based on mixed (generalist-collaborative) communities. Contrary, in conventionally managed vineyards, we observed highly modular (niche-specialized) low clustered communities, supported by a higher degree of selection (more co-exclusion proportion). We also found that, although geographic factors can explain the different fungal community arrangements in both countries, the relationship between network properties in local fungal communities better capture the impact of farming practices regardless of the location. Thus, we hypothesize that local network properties can be globally used to evaluate the effect of ecosystem disturbances in crops, but also in when evaluating the effect of clinical interventions or to compare microbiomes of healthy vs. disturbed conditions.

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