Spatiotemporal network structure among “friends of friends” reveals contagious disease process
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Journal titlePLOS ONE
MetadataShow full item record
AbstractDisease transmission can be identified in a social network from the structural patterns of contact. However, it is difficult to separate contagious processes from those driven by homophily, and multiple pathways of transmission or inexact information on the timing of infection can obscure the detection of true transmission events. Here, we analyze the dynamic social network of a large, and near-complete population of 16,430 zoo birds tracked daily over 22 years to test a novel “friends-of-friends” strategy for detecting contagion in a social network. The results show that cases of avian mycobacteriosis were significantly clustered among pairs of birds that had been in direct contact. However, since these clusters might result due to correlated traits or a shared environment, we also analyzed pairs of birds that had never been in direct contact but were indirectly connected in the network via other birds. The disease was also significantly clustered among these friends of friends and a reverse-time placebo test shows that homophily could not be causing the clustering. These results provide empirical evidence that at least some avian mycobacteriosis infections are transmitted between birds, and provide new methods for detecting contagious processes in large-scale global network structures with indirect contacts, even when transmission pathways, timing of cases, or etiologic agents are unknown.
DescriptionSummary: This study introduces a novel strategy to test for contagious processes in a social network based on simple tests of spatial and temporal connectivity. Using indirect contacts (i.e., the “friends of friends”), rather than direct contacts, allowed us to isolate associations that could be attributed to contagious spread and test for other causes of spatial and temporal disease clustering. This was important for our disease of interest, avian mycobacteriosis, because there are multiple transmission routes and specific pathogens are often unknown. Thus, for the first time, we could begin to tease apart completely confounded transmission pathways. Our unique data, which include over 16,000 birds with complete movement tracking and health monitoring over time, demonstrate the power of a complete network to inform global disease processes. The method could be broadly applied to investigate diseases of humans and animals when complete networks are observed, but the disease epidemiology or pathways of transmission are unknown.
RightsCopyright: © 2020 Witte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as Copyright: © 2020 Witte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0/