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Introduction
Most of infectious diseases are transmitted via direct contacts,
therefore, animal trade and other contacts between animals play an
essential role in the disease spread.
Social network analysis (SNA) is a powerful tool to explore the
interactions between agents in a contact network and obtain additional
information regarding the structure and dynamic of a community. The
agents or nodes could be defined as the farms that belong to a trade
network, wildlife animals tracked by GPS collars, or animals observed in
a group. Using SNA methodologies we can identify individuals that could
have a bigger role in disease spread when a disease is introduced into
the community.
In this workshop we will use data regarding the movement of production animals in a contact network to demonstrate the applications of spatio-temporal network analysis. These methodologies can be applied to other settings such as wildlife monitoring, or to explore hierarchical relationships between animal groups, among others. The first part of the workshop will focus on the description of static networks and the second part will be using dynamic network analysis, which includes temporal dynamics of the movements. Both parts will also include the spatial component.
This workshop is aimed for students, researchers and other people interested in disease transmission and population dynamics.
Objectives
- Analyze and describe static and dynamic networks.
- Advantages and disadvantages of static and dynamic networks and when we can use them.
- Incorporate the spatial relationships in the analysis of networks.
- Visualize and present networks using spatial and non spatial approaches.
- Incorporate the transmission and potential spread of diseases in a network.
- Incorporate the results from network analysis with other statistical methods.
Requisites
Please make sure you have R version >3.5
This course has been developed with contributions from: Jose Pablo Gomez-Vazquez,
Jerome
Baron and Beatriz
Martinez-Lopez.
Feel free to use these training materials for your own research and
teaching. When using the materials we would appreciate using the proper
credits. If you would be interested in a training session, please
contact: jpgo@ucdavis.edu
Data
The data used for this workshop is contained in the package
STNet. To install STNet we need the package
devtools and use the command
devtools::install_github("jpablo91/STNet").
Pre-workshop materials
An introduction to R and spatial data can be found in the following links:
Other Links
- Shared
folder, we will use this folder to provide some workshop materials
such as code, data, or other resources.
- visNetwork, package for interactive network visualization.
Recommended reading
- Martínez-López B, Perez AM, Sánchez-Vizcaíno JM. Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transbound Emerg Dis. 2009 May;56(4):109-20. doi: 10.1111/j.1865-1682.2009.01073.x. PMID: 19341388.
- Enright J, Kao RR. Epidemics on dynamic networks. Epidemics. 2018 Sep;24:88-97. doi: 10.1016/j.epidem.2018.04.003. Epub 2018 Apr 28. PMID: 29907403.
- Silk, M.J., Croft, D.P., Delahay, R.J., Hodgson, D.J., Weber, N., Boots, M. and McDonald, R.A. (2017), The application of statistical network models in disease research. Methods Ecol Evol, 8: 1026-1041. https://doi.org/10.1111/2041-210X.12770
- Farine, DR. When to choose dynamic vs. static social network analysis. J Anim Ecol. 2018; 87: 128– 138. https://doi.org/10.1111/1365-2656.12764
Tentative schedule
| Time | Topic | Format |
|---|---|---|
| 10:00-10:15 | Introduction | Lecture |
| 10:15-10:45 | Part I | Lecture |
| 10:45-12:00 | Lab 1 | Lab |
| 12:00-13:00 | Lunch | Lunch |
| 13:00-14:00 | Lab 2 | Lab |
| 14:00-14:30 | Part II | Lecture |
| 14:30-14:40 | Break | Break |
| 14:40-15:10 | Lab 3 | Lab |
| 15:20-16:00 | Lab 4 | Lab |
Post workshop survey
We would appreciate if you take a minute to fill a quick anonymous survey for feedback. To go to the survey follow THIS LINK