Human mobility is important for infectious disease spread. However, little is known about how travel varies by demographic groups and how this heterogeneity influences infectious disease risk.
We analyzed ten years of survey data from 15 communities in a remote but rapidly changing region in rural Ecuador where road development in the past 15–20 years has dramatically changed travel. We identify determinants of travel and incorporate them into an infection transmission model.
Individuals living in communities more remote at baseline had lower travel rates compared with less remote villages (adjusted odds ratio [OR]=0.51, 95% CI 0.38, 0.67). Our model predicts that less remote villages are, therefore, at increased disease risk. Though road building and travel increased for all communities, this risk differential remained over 10 years of observation. Our transmission model also suggests that travelers and non-travelers have different roles in disease transmission. Adults travel more than children (adjusted OR=1.73, 95% CI 1.30, 2.31) and therefore disseminate infection from population centers to rural communities. Children are more likely than adults to be infected locally (attributable fraction=0.24 and 0.09, respectively) and were indirectly affected by adult travel patterns.
These results reinforce the importance of large population centers for regional transmission and show that children and adults may play different roles in disease spread. Changing transportation infrastructure and subsequent economic and social transitions are occurring worldwide, potentially causing increased regional risk of disease.
a aJ.T. and A.F.B. contributed equally to this work
Acknowledgements We would like to think the EcoDess field staff for their valuable contribution collecting the data used in this work.
Conflicts of Interest We have no competing interests.
Funding This work was funded by the National Institutes of Health (grant R01- AI050038), NIH MIDAS (grant U01GM110712) and the National Science Foundation Water Sustainability and Climate Program (grant 1360330).
Data and code used for this analysis is available upon request from the authors.
*Corresponding Author: Department of Epidemiology, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48104 (email: firstname.lastname@example.org)
Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.