Social network analysis and risk assessment: An example of introducing an exotic animal disease in Italy

Maurella, Cristiana; Mastrantonio, Gianluca; Bertolini, Silvia; Crescio, Maria Ines; Ingravalle, Francesco; Adkin, Amie; Simons, Robin; Nardi, Marco De; Estrada-Peña, Augustin; Horigan, Verity; Ru, Giuseppe

doi:10.1016/j.mran.2019.04.001

Cited by 7 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In epidemiology, network approaches revealed also very powerful, e.g. to model patterns of infectious disease spread, risk of infectious disease introduction, estimate outbreak sizes 3,12,18,[21][22][23][30][31][32][33][34][35] , develop and evaluate disease surveillance and mitigation strategies 29,36,37 , or facilitate policy development 6,31,38 .…”

Section: Introductionmentioning

confidence: 99%

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Puspitarani

Fuchs

et al. 2023

Preprint

View full text Add to dashboard Cite

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

show abstract

Section: Introductionmentioning

confidence: 99%

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Puspitarani

Fuchs

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Characterizing the structure of these networks therefore plays a crucial role in understanding transmission patterns of infectious diseases in livestock and, consequently, for informing disease risk assessments and control strategies. This may involve the use of disease transmission models which explicitly account for contact network structure [3,[7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Simulating contact networks for livestock disease epidemiology: a systematic review

Leung

Rudge

Fournié

2023

J. R. Soc. Interface.

View full text Add to dashboard Cite

Contact structure among livestock populations influences the transmission of infectious agents among them. Models simulating realistic contact networks therefore have important applications for generating insights relevant to livestock diseases. This systematic review identifies and compares such models, their applications, data sources and how their validity was assessed. From 52 publications, 37 models were identified comprising seven model frameworks. These included mathematical models ( n = 8; including generalized random graphs, scale-free, Watts–Strogatz and spatial models), agent-based models ( n = 8), radiation models ( n = 1) (collectively, considered ‘mechanistic’), gravity models ( n = 4), exponential random graph models ( n = 9), other forms of statistical model ( n = 6) (statistical) and random forests ( n = 1) (machine learning). Overall, nearly half of the models were used as inputs for network-based epidemiological models. In all models, edges represented livestock movements, sometimes alongside other forms of contact. Statistical models were often applied to infer factors associated with network formation ( n = 12). Mechanistic models were commonly applied to assess the interaction between network structure and disease dissemination ( n = 6). Mechanistic, statistical and machine learning models were all applied to generate networks given limited data ( n = 13). There was considerable variation in the approaches used for model validation. Finally, we discuss the relative strengths and weaknesses of model frameworks in different use cases.

show abstract

“…In epidemiology, network approaches revealed also very powerful, e.g. to model patterns of infectious disease spread, risk of infectious disease introduction, estimate outbreak sizes 3 , 12 , 18 , 21 – 23 , 30 – 35 , develop and evaluate disease surveillance and mitigation strategies 29 , 36 , 37 , or facilitate policy development 6 , 31 , 38 .…”

Section: Introductionmentioning

confidence: 99%

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Puspitarani

Fuchs

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015–2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

show abstract

Social network analysis and risk assessment: An example of introducing an exotic animal disease in Italy

Cited by 7 publications

References 25 publications

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Simulating contact networks for livestock disease epidemiology: a systematic review

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Contact Info

Product

Resources

About