A Network Perspective on the Vectoring of Human Disease

Bellekom, Ben; Hackett, Talya D.; Lewis, Owen T.

doi:10.1016/j.pt.2020.12.001

Cited by 18 publications

(28 citation statements)

References 93 publications

(88 reference statements)

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“…Vector-virus combinations can be tested in the laboratory, including in model-experiment feedback designs that leverage existing predictions (e.g., [13]) much like model-guided fieldwork can be used to optimize viral discovery [25]. Similarly, further investigation of mosquito biting behavior will help resolve the host-vector component [15], highlighting the need for "basic" natural history research even on mosquitoes that are not known to be primary vectors of human disease.…”

Section: Discussionmentioning

confidence: 99%

“…[13] These data include 334 associations between 180 mosquito species and 37 flaviviruses. Host-vector association data were taken from a recent study of dipteran biting networks [15]. These data describe 1744 associations between 255 biting dipteran species and 214 hosts (including 67 mammals).…”

Section: Methodsmentioning

confidence: 99%

“…network Using the same data resource as used above on host-virus associations, we now considered the identity of the mosquito vector species, and the association between the vector and virus [13], and the feeding association between mosquito vector and mammal species [15]. While host and virus traits were largely the same as considered above, the mosquito vector traits consisted of a set of 33 mosquito vector traits from [13].…”

Section: Model 2: Modeling Mammal-mosquito-virus Associations As a Tripartitementioning

confidence: 99%

“…To address this, we developed two new approaches and tested them on mosquitoborne flaviviruses, a well-studied group that includes important zoonoses like dengue, West Nile, yellow fever, and Zika viruses. Through a synthesis of existing data sources, we combined data on mammal-virus associations [12], vectorflavivirus associations [13], and diptera-mammal biting preferences [15]. We combined these data into one mammal-mosquito-flavivirus network, which can also be reduced down to a mammal-flavivirus network where viruses' mosquito communities are represented as node metadata.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the tripartite network of mosquito-borne disease

Dallas¹,

Ryan²,

Bellekom³

et al. 2021

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Model 2: Modeling Mammal-mosquito-virus Associations As a Tripartitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting the tripartite network of mosquito-borne disease

Dallas¹,

Ryan²,

Bellekom³

et al. 2021

Preprint

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“…I have augmented this fieldwork with a literature-based analysis of published blood meal interaction data. This provides an in-depth opinion on the value of interaction networks in understanding host-insect-disease interactions and highlights their future applications in identifying and monitoring emerging diseases and unrecognized vectors 11 . Further, using a global data set extracted from the literature, I aim to examine structural variations of biting insect-host communities between distinct habitat types and latitudes.…”

Section: Using Interaction Network To Explore the Community Structurmentioning

confidence: 99%

Ecological and epidemiological insights from blood meals

Bellekom¹

2021

barbull

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nowledge of variations in insect biting behaviour and feeding patterns of insect disease vectors is important in understanding disease epidemiology and establishing effective control methods. Blood feeding insects are embedded within complex ecological communities and interact with other species acting as predator, prey, or competitor. These interactions are likely to impact the population dynamics of those co-occurring species 1 .

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