Application of exponential random graph models to determine nomadic herders' movements in Senegal

Transbound Emerg Dis. 2019 Jul;66(4):1642-1652. doi: 10.1111/tbed.13198. Epub 2019 Jun 1.


Understanding human and animal mobility patterns is a key to predict local and global disease spread. We analysed the nomad herds' movement network in a pilot area of northern Senegal and used exponential random graph models (ERGM) to investigate the reasons behind these movements. We interviewed 132 nomadic herders to collect information about nomad herd structures, movements, and reasons for taking specific routes or gathering in certain areas. We constructed a spatially explicit network with villages as the nodes and nomad herds' movements as the connecting edges. The final ERGM showed that node and edge attributes such as presence of cattle in the herd (odds ratio = 12, CI: 5.3, 27.3), morbidity (odds ratio = 3.6, CI: 2.3, 5.7), and lack of water (odds ratio = 2, CI: 1.3, 3.1) were important predictors of nomad herds' movements. This study not only provides valuable information for monitoring important livestock diseases such as Rift Valley Fever in Senegal, but also helps implement outreach, education, and intervention programs for other emerging and endemic diseases affecting nomadic herds.

Keywords: ERGM; Senegal; livestock; network analysis; nomadic; risk-based surveillance.

MeSH terms

  • Animal Diseases / transmission*
  • Animals
  • Human Migration*
  • Humans
  • Livestock*
  • Models, Theoretical
  • Population Dynamics
  • Senegal