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

PRIOR KNOWLEDGE VERSUS CONSTRUCTED KNOWLEDGE: WHAT IMPACT ON LEARNING?

2008 ◽  
Vol 11 (02) ◽  
pp. 289-302 ◽  
Author(s):  
WIDAD GUECHTOULI
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Prior Knowledge ◽  
Learning Process ◽  
Agent Based Model ◽  
Agent Based ◽  
Congestion Effect ◽  
Over Time

The aim of this paper is to model the process of learning within a social network and compare the levels of learning in two different situations: one where individuals know others' competencies as given data and interact on this basis; and one where individuals know nothing about others' competencies but rather build this knowledge over time, according to their past interactions. For this purpose, we build an agent-based model, and model these two scenarios of simulations. Results are partly studied using network analysis, and they show that in the second type of simulations agents are able to identify the most competent agents in the network and increase their competencies. Results also show that learning is easier when there is no prior knowledge of others' competencies. Otherwise, agents deal with a congestion effect that slows down the learning process.

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 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.

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