scholarly journals Recent advances in the analysis of behavioural organization and interpretation as indicators of animal welfare

2009 ◽  
Vol 6 (41) ◽  
pp. 1103-1119 ◽  
Author(s):  
Lucy Asher ◽  
Lisa M. Collins ◽  
Angel Ortiz-Pelaez ◽  
Julian A. Drewe ◽  
Christine J. Nicol ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Animal Welfare ◽  
Life Sciences ◽  
Mathematical Methods ◽  
Abnormal Behaviour ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Potential Applications

While the incorporation of mathematical and engineering methods has greatly advanced in other areas of the life sciences, they have been under-utilized in the field of animal welfare. Exceptions are beginning to emerge and share a common motivation to quantify ‘hidden’ aspects in the structure of the behaviour of an individual, or group of animals. Such analyses have the potential to quantify behavioural markers of pain and stress and quantify abnormal behaviour objectively. This review seeks to explore the scope of such analytical methods as behavioural indicators of welfare. We outline four classes of analyses that can be used to quantify aspects of behavioural organization. The underlying principles, possible applications and limitations are described for: fractal analysis, temporal methods, social network analysis, and agent-based modelling and simulation. We hope to encourage further application of analyses of behavioural organization by highlighting potential applications in the assessment of animal welfare, and increasing awareness of the scope for the development of new mathematical methods in this area.

scholarly journals Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

2020 ◽  
Vol 2 ◽  
pp. 16325 ◽  
Author(s):  
Meike Will ◽  
Jürgen Groeneveld ◽  
Karin Frank ◽  
Birgit Müller
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Human Interaction ◽  
Extensive Literature ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
The Impact

Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.

scholarly journals Prioritizing Healthcare Worker Vaccinations on the Basis of Social Network Analysis

2010 ◽  
Vol 31 (9) ◽  
pp. 893-900 ◽  
Author(s):  
Philip M. Polgreen ◽  
Troy Leo Tassier ◽  
Sriram Venkata Pemmaraju ◽  
Alberto Maria Segre
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Healthcare Workers ◽  
Computational Simulation ◽  
Vaccination Strategy ◽  
Occupational Therapists ◽  
Agent Based ◽  
X Ray ◽  
Vaccination Rates

Objective.To use social network analysis to design more effective strategies for vaccinating healthcare workers against influenza.Design.An agent-based simulation.Setting.A simulation based on a 700-bed hospital.Methods.We first observed human contacts (defined as approach within approximately 0.9 m) performed by 15 categories of healthcare workers (eg, floor nurses, intensive care unit nurses, staff physicians, phlebotomists, and respiratory therapists). We then constructed a series of contact graphs to represent the social network of the hospital and used these graphs to run agent-based simulations to model the spread of influenza. A targeted vaccination strategy that preferentially vaccinated more “connected” healthcare workers was compared with other vaccination strategies during simulations with various base vaccination rates, vaccine effectiveness, probability of transmission, duration of infection, and patient length of stay.Results.We recorded 6,654 contacts by 148 workers during 606 hours of observations from January through December 2006. Unit clerks, X-ray technicians, residents and fellows, transporters, and physical and occupational therapists had the most contacts. When repeated contacts with the same individual were excluded, transporters, unit clerks, X-ray technicians, physical and occupational therapists, and social workers had the most contacts. Preferentially vaccinating healthcare workers in more connected job categories yielded a substantially lower attack rate and fewer infections than a random vaccination strategy for all simulation parameters.Conclusions.Social network models can be used to derive more effective vaccination policies, which are crucial during vaccine shortages or in facilities with low vaccination rates. Local vaccination priorities can be determined in any healthcare facility with only a modest investment in collection of observational data on different types of healthcare workers. Our findings and methods (ie, social network analysis and computational simulation) have implications for the design of effective interventions to control a broad range of healthcare-associated infections.

scholarly journals Social Network Analysis in Farm Animals: Sensor-Based Approaches

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 434
Author(s):  
Suresh Neethirajan ◽  
Bas Kemp
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Collection ◽  
Animal Welfare ◽  
Farm Management ◽  
Animal Behaviour ◽  
Farm Animal ◽  
Farm Animals ◽  
Management Processes

Natural social systems within animal groups are an essential aspect of agricultural optimization and livestock management strategy. Assessing elements of animal behaviour under domesticated conditions in comparison to natural behaviours found in wild settings has the potential to address issues of animal welfare effectively, such as focusing on reproduction and production success. This review discusses and evaluates to what extent social network analysis (SNA) can be incorporated with sensor-based data collection methods, and what impact the results may have concerning welfare assessment and future farm management processes. The effectiveness and critical features of automated sensor-based technologies deployed in farms include tools for measuring animal social group interactions and the monitoring and recording of farm animal behaviour using SNA. Comparative analyses between the quality of sensor-collected data and traditional observational methods provide an enhanced understanding of the behavioural dynamics of farm animals. The effectiveness of sensor-based approaches in data collection for farm animal behaviour measurement offers unique opportunities for social network research. Sensor-enabled data in livestock SNA addresses the biological aspects of animal behaviour via remote real-time data collection, and the results both directly and indirectly influence welfare assessments, and farm management processes. Finally, we conclude with potential implications of SNA on modern animal farming for improvement of animal welfare.

scholarly journals Combining social network analysis and agent-based model for enabling nature-based solution implementation: The case of Medina del Campo (Spain)

2021 ◽  
pp. 149734
Author(s):  
R. Giordano ◽  
M. Manez-Costa ◽  
A. Pagano ◽  
B. Mayor Rodriguez ◽  
P. Zorrilla-Miras ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Agent Based Model ◽  
Agent Based
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