scholarly journals Contact chains of cattle farms in Great Britain

2019 ◽  
Vol 6 (2) ◽  
pp. 180719 ◽  
Author(s):  
Helen R. Fielding ◽  
Trevelyan J. McKinley ◽  
Matthew J. Silk ◽  
Richard J. Delahay ◽  
Robbie A. McDonald
Keyword(s):  
Great Britain ◽  
Bovine Tuberculosis ◽  
Bimodal Distribution ◽  
Disease Spread ◽  
Scale Free ◽  
Network Analyses ◽  
Directed Paths ◽  
System Data ◽  
Epidemiological Characteristics ◽  
Cattle Farms

Network analyses can assist in predicting the course of epidemics. Time-directed paths or ‘contact chains' provide a measure of host-connectedness across specified timeframes, and so represent potential pathways for spread of infections with different epidemiological characteristics. We analysed networks and contact chains of cattle farms in Great Britain using Cattle Tracing System data from 2001 to 2015. We focused on the potential for between-farm transmission of bovine tuberculosis, a chronic infection with potential for hidden spread through the network. Networks were characterized by scale-free type properties, where individual farms were found to be influential ‘hubs' in the network. We found a markedly bimodal distribution of farms with either small or very large ingoing and outgoing contact chains (ICCs and OCCs). As a result of their cattle purchases within 12-month periods, 47% of British farms were connected by ICCs to more than 1000 other farms and 16% were connected to more than 10 000 other farms. As a result of their cattle sales within 12-month periods, 66% of farms had OCCs that reached more than 1000 other farms and 15% reached more than 10 000 other farms. Over 19 000 farms had both ICCs and OCCs reaching more than 10 000 farms for two or more years. While farms with more contacts in their ICCs or OCCs might play an important role in disease spread, farms with extensive ICCs and OCCs might be particularly important by being at higher risk of both acquiring and disseminating infections.

scholarly journals Effects of trading networks on the risk of bovine tuberculosis incidents on cattle farms in Great Britain

2020 ◽  
Vol 7 (4) ◽  
pp. 191806
Author(s):  
Helen R. Fielding ◽  
Trevelyan J. McKinley ◽  
Richard J. Delahay ◽  
Matthew J. Silk ◽  
Robbie A. McDonald
Keyword(s):  
Great Britain ◽  
Bovine Tuberculosis ◽  
Disease Risk ◽  
Herd Size ◽  
Risk Area ◽  
High Risk Area ◽  
Trading Patterns ◽  
Additional Approach ◽  
Trading Networks ◽  
Cattle Farms

Trading animals between farms and via markets can provide a conduit for spread of infections. By studying trading networks, we might better understand the dynamics of livestock diseases. We constructed ingoing contact chains of cattle farms in Great Britain that were linked by trading, to elucidate potential pathways for the transmission of infection and to evaluate their effect on the risk of a farm experiencing a bovine tuberculosis (bTB) incident. Our findings are consistent with variation in bTB risk associated with region, herd size, disease risk area and history of previous bTB incidents on the root farm and nearby farms. However, we also identified effects of both direct and indirect trading patterns, such that connections to more farms in the England High-Risk Area up to three movements away from the root farm increased the odds of a bTB incident, while connections with more farms in the England Low-Risk Area up to eight movements away decreased the odds. Relative to other risk factors for bTB, trading behaviours are arguably more amenable to change, and consideration of risks associated with indirect trading, as well direct trading, might therefore offer an additional approach to bTB control in Great Britain.

scholarly journals Evaluating the Bovine Tuberculosis Eradication Mechanism and Its Risk Factors in England’s Cattle Farms

2021 ◽  
Vol 18 (7) ◽  
pp. 3451
Author(s):  
Tabassom Sedighi ◽  
Liz Varga
Keyword(s):  
Risk Factors ◽  
Bovine Tuberculosis ◽  
Animal Health ◽  
Dynamic Bayesian Network ◽  
Policy Makers ◽  
Domain Experts ◽  
Actual Experience ◽  
Complex Evaluation ◽  
Risk Areas ◽  
Cattle Farms

Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms.

scholarly journals Modelling the bimodal distribution of indoor gamma-ray dose-rates in Great Britain

2018 ◽  
Vol 57 (4) ◽  
pp. 321-347 ◽  
Author(s):  
G. M. Kendall ◽  
P. Chernyavskiy ◽  
J. D. Appleton ◽  
J. C. H. Miles ◽  
R. Wakeford ◽  
...  
Keyword(s):  
Great Britain ◽  
Gamma Ray ◽  
Bimodal Distribution ◽  
Dose Rates

Words, constructions and corpora: Network representations of constructional semantics for Mandarin space particles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alvin Cheng-Hsien Chen
Keyword(s):  
Clustering Coefficient ◽  
Linguistic Knowledge ◽  
Scale Free ◽  
Network Analyses ◽  
Semantic Fields ◽  
Text Corpora ◽  
Quantitative Properties ◽  
Local Clustering Coefficient ◽  
Linguistic Units

AbstractIn this study, we aim to demonstrate the effectiveness of network science in exploring the emergence of constructional semantics from the connectedness and relationships between linguistic units. With Mandarin locative constructions (MLCs) as a case study, we extracted constructional tokens from a representative corpus, including their respective space particles (SPs) and the head nouns of the landmarks (LMs), which constitute the nodes of the network. We computed edges based on the lexical similarities of word embeddings learned from large text corpora and the SP-LM contingency from collostructional analysis. We address three issues: (1) For each LM, how prototypical is it of the meaning of the SP? (2) For each SP, how semantically cohesive are its LM exemplars? (3) What are the emerging semantic fields from the constructional network of MLCs? We address these questions by examining the quantitative properties of the network at three levels: microscopic (i.e., node centrality and local clustering coefficient), mesoscopic (i.e., community) and macroscopic properties (i.e., small-worldness and scale-free). Our network analyses bring to the foreground the importance of repeated language experiences in the shaping and entrenchment of linguistic knowledge.

scholarly journals The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

2022 ◽  
Vol 8 ◽  
Author(s):  
Orapun Arjkumpa ◽  
Minta Suwannaboon ◽  
Manoch Boonrod ◽  
Issara Punyawan ◽  
Supawadee Liangchaisiri ◽  
...  
Keyword(s):  
Skin Disease ◽  
Temporal Analysis ◽  
Space Time ◽  
Effective Control ◽  
Outbreak Response ◽  
Lumpy Skin Disease ◽  
Epidemiological Features ◽  
Spatio Temporal ◽  
Epidemiological Characteristics ◽  
Cattle Farms

The first outbreak of lumpy skin disease (LSD) in Thailand was reported in March 2021, but information on the epidemiological characteristics of the outbreak is very limited. The objectives of this study were to describe the epidemiological features of LSD outbreaks and to identify the outbreak spatio-temporal clusters. The LSD-affected farms located in Roi Et province were investigated by veterinary authorities under the outbreak response program. A designed questionnaire was used to obtain the data. Space-time permutation (STP) and Poisson space-time (Poisson ST) models were used to detect areas of high LSD incidence. The authorities identified 293 LSD outbreak farms located in four different districts during the period of March and the first week of April 2021. The overall morbidity and mortality of the affected cattle were 40.5 and 1.2%, respectively. The STP defined seven statistically significant clusters whereas only one cluster was identified by the Poisson ST model. Most of the clusters (n = 6) from the STP had a radius <7 km, and the number of LSD cases in those clusters varied in range of 3–51. On the other hand, the most likely cluster from the Poisson ST included LSD cases (n = 361) from 198 cattle farms with a radius of 17.07 km. This is the first report to provide an epidemiological overview and determine spatio-temporal clusters of the first LSD outbreak in cattle farms in Thailand. The findings from this study may serve as a baseline information for future epidemiological studies and support authorities to establish effective control programs for LSD in Thailand.

scholarly journals Changing travel patterns in China during the early stages of the COVID-19 pandemic

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamish Gibbs ◽  
◽  
Yang Liu ◽  
Carl A. B. Pearson ◽  
Christopher I. Jarvis ◽  
...  
Keyword(s):  
Public Health ◽  
Human Mobility ◽  
Transportation Network ◽  
Mainland China ◽  
Disease Spread ◽  
Travel Patterns ◽  
Network Analyses ◽  
Early Stages ◽  
Travel Restrictions ◽  
Spatio Temporal

Abstract Understanding changes in human mobility in the early stages of the COVID-19 pandemic is crucial for assessing the impacts of travel restrictions designed to reduce disease spread. Here, relying on data from mainland China, we investigate the spatio-temporal characteristics of human mobility between 1st January and 1st March 2020, and discuss their public health implications. An outbound travel surge from Wuhan before travel restrictions were implemented was also observed across China due to the Lunar New Year, indicating that holiday travel may have played a larger role in mobility changes compared to impending travel restrictions. Holiday travel also shifted healthcare pressure related to COVID-19 towards locations with lower healthcare capacity. Network analyses showed no sign of major changes in the transportation network after Lunar New Year. Changes observed were temporary and did not lead to structural reorganisation of the transportation network during the study period.

URBAN TRANSIT SYSTEM AS A SCALE-FREE NETWORK

2004 ◽  
Vol 18 (19n20) ◽  
pp. 1043-1049 ◽  
Author(s):  
JIANJUN WU ◽  
ZIYOU GAO ◽  
HUIJUN SUN ◽  
HAIJUN HUANG
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Scale Free Network ◽  
Urban Transit ◽  
Transit System ◽  
Transit Network ◽  
Scale Free ◽  
Free Network ◽  
System Data ◽  
Random Failures

Many systems can be represented by networks as a set of nodes joined together by links indicating interaction. Recently studies have suggested that a lot of real networks are scale-free, such as the WWW, social networks, etc. In this paper, discoveries of scale-free characteristics are reported on the network constructed from the real urban transit system data in Beijing. It is shown that the connectivity distribution of the transit network decays as a power-law, and the exponent λ is about equal to 2.24 from the simulation graph. Based on the scale-free network topology structure of the transit network, if only transit "hub nodes" are controlled well, the transit network can resist random failures (such as traffic congestion, traffic accidents, etc.) successfully.

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