Predicting the tripartite network of mosquito-borne disease

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Tad Dallas, Sadie Jane Ryan, Ben Bellekom, Anna Claire Fagre, Rebecca Christofferson, Colin J. Carlson


The potential for a pathogen to infect a host is mediated by traits of both the host and pathogen, as well as the complex interactions between them. Arthropod-borne viruses (arboviruses) require an intermediate vector, introducing an additional compatibility layer. Existing predictive models of host-virus networks rarely incorporate the unique aspects of vector transmission, instead treating vector biology as a hidden, unobserved layer. We explore two possible extensions to address this: first, we added vector traits into predictions of the bipartite host-virus network; and second, we used host, vector, and virus traits to predict the tripartite host-vector-virus network. We tested both approaches on mosquito-borne flaviviruses of mammals. Using host-virus models, we find that the inclusion of vector traits may improve inference in some cases, while viral traits proved to be the most important for model performance. Further, we found that it was possible, though quite difficult, to predict full tripartite (host-vector-virus) links. Both approaches are interesting avenues for further model development, but our results keenly underscore a need to collect more comprehensive datasets to characterize arbovirus ecology, across a wide and less biased geographic scope, especially outside of North America, and to better identify molecular traits that underpin host-vector-virus interactions.



Immunology and Infectious Disease, Life Sciences



Published: 2021-11-18 00:15


CC-By Attribution-ShareAlike 4.0 International

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