IDEAS home Printed from https://ideas.repec.org/p/ags/aaae13/159704.html
   My bibliography  Save this paper

Insights from social network analysis are helping to build understanding of African Swine Fever epidemiology

Author

Listed:
  • Lichoti, Jacqueline K.
  • Davies, Jocelyn
  • Okoth, Edward
  • Maru, Yiheyis
  • Bishop, Richard

Abstract

Pig movements are likely to play a signficant role in the spread of important infectious diseases such as the African Swine Fever. Characterization of movement networks from farm-to-farm and through other types of farm or household operations can provide useful information on the role that networks play in acquiring and spreading infectious diseases. Analysis of social networks that underpin these pig movements can also reveal structures that are important in the transmission of disease, trade of commodities, the spread of knowledge and norms of social behavior. Our study assessed pig movements among pig keeping households within Kenya and Uganda and across the Kenya-Uganda border to help understand within country and trans-boundary pig movements.Villages were sampled using randomized cluster design. Data was collected through interviews in 2012/13 of 683 smallholder pig-keeping households in 38 villages. NodeXL software was used to analyze pig movement networks at village level. Movement of pigs occurred through agistment, sow service, restocking of household pigs and sale of finished pigs for slaughter. Most sow services occurred within the same villages or villages that were close by. Cross-border boar service between Uganda and Kenya was also recorded. Internal and unmonitored trade in both directions was prevalent. Most pig sales during ASF outbreak were to traders or other farmers who were most likely not coming from the same village. Close social relationships between actors in pig movement networks indicate the potential for possible interventions to develop shared norms amongst smallholder pig keepers to manage risk of ASF contraction and transmission.

Suggested Citation

  • Lichoti, Jacqueline K. & Davies, Jocelyn & Okoth, Edward & Maru, Yiheyis & Bishop, Richard, 2013. "Insights from social network analysis are helping to build understanding of African Swine Fever epidemiology," 2013 Fourth International Conference, September 22-25, 2013, Hammamet, Tunisia 159704, African Association of Agricultural Economists (AAAE).
  • Handle: RePEc:ags:aaae13:159704
    DOI: 10.22004/ag.econ.159704
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/159704/files/Lichoti.et.al%20AAAE.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.159704?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Klovdahl, A.S. & Potterat, J.J. & Woodhouse, D.E. & Muth, J.B. & Muth, S.Q. & Darrow, W.W., 1994. "Social networks and infectious disease: The Colorado Springs study," Social Science & Medicine, Elsevier, vol. 38(1), pages 79-88, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    2. Jonas, Adam B. & Young, April M. & Oser, Carrie B. & Leukefeld, Carl G. & Havens, Jennifer R., 2012. "OxyContin® as currency: OxyContin® use and increased social capital among rural Appalachian drug users," Social Science & Medicine, Elsevier, vol. 74(10), pages 1602-1609.
    3. Kyle Vincent & Steve Thompson, 2017. "Estimating Population Size With Link-Tracing Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1286-1295, July.
    4. Berry, George & Cameron, Christopher John, 2017. "A new method to reduce overestimation of thresholds with observational network data," SocArXiv ctjd6, Center for Open Science.
    5. Matt J Keeling & Thomas House & Alison J Cooper & Lorenzo Pellis, 2016. "Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-18, December.
    6. Samuel F Rosenblatt & Jeffrey A Smith & G Robin Gauthier & Laurent Hébert-Dufresne, 2020. "Immunization strategies in networks with missing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
    7. Hollm-Delgado, Maria-Graciela, 2009. "Molecular epidemiology of tuberculosis transmission: Contextualizing the evidence through social network theory," Social Science & Medicine, Elsevier, vol. 69(5), pages 747-753, September.
    8. Tyler H. McCormick & Tian Zheng, 2015. "Latent Surface Models for Networks Using Aggregated Relational Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1684-1695, December.
    9. Petter Holme & Nelly Litvak, 2017. "Cost-efficient vaccination protocols for network epidemiology," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-18, September.
    10. Papachristos, Andrew V. & Wildeman, Christopher & Roberto, Elizabeth, 2015. "Tragic, but not random: The social contagion of nonfatal gunshot injuries," Social Science & Medicine, Elsevier, vol. 125(C), pages 139-150.
    11. John Roberts & Devon Brewer, 2001. "Measures and tests of heaping in discrete quantitative distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(7), pages 887-896.
    12. Benjamin Armbruster & Margaret Brandeau, 2007. "Contact tracing to control infectious disease: when enough is enough," Health Care Management Science, Springer, vol. 10(4), pages 341-355, December.
    13. Stephane Helleringer & Hans-Peter Kohler & Agnes Chimbiri & Praise Chatonda & James Mkandawire, 2009. "The Likoma Network Study: Context, data collection and initial results," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 21(15), pages 427-468.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaae13:159704. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaaeaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.