2017
DOI: 10.7554/elife.22053
|View full text |Cite
|
Sign up to set email alerts
|

Data-driven identification of potential Zika virus vectors

Abstract: Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model … Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
74
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
6
2
1
1

Relationship

1
9

Authors

Journals

citations
Cited by 72 publications
(75 citation statements)
references
References 257 publications
1
74
0
Order By: Relevance
“…Recent studies showed that diverse ecological and evolutionary features describing intrinsic organismal characteristics can be combined to predict species suitable for disease transmission (Olival et al ., ). For example, based on traits of species, recent studies used supervised machine learning algorithms to identify and prioritize rodent reservoirs of zoonotic diseases (Han et al ., ), bat species hosting filoviruses (Han et al ., ), mosquito vectors of Zika virus (Evans et al ., ), and suitable tick vectors from the genus Ixodes (Yang & Han, ). Future research should explore species traits (e.g.…”
Section: Ecological Modelling Of Cwd Spread Zoonotic Potential and mentioning
confidence: 99%
“…Recent studies showed that diverse ecological and evolutionary features describing intrinsic organismal characteristics can be combined to predict species suitable for disease transmission (Olival et al ., ). For example, based on traits of species, recent studies used supervised machine learning algorithms to identify and prioritize rodent reservoirs of zoonotic diseases (Han et al ., ), bat species hosting filoviruses (Han et al ., ), mosquito vectors of Zika virus (Evans et al ., ), and suitable tick vectors from the genus Ixodes (Yang & Han, ). Future research should explore species traits (e.g.…”
Section: Ecological Modelling Of Cwd Spread Zoonotic Potential and mentioning
confidence: 99%
“…Cx. quinquefasciatus has been identified by predictive models as a potential vector for Zika virus [132], as have Sabethes and Haemagogus spp . [133].…”
Section: Resultsmentioning
confidence: 99%
“…Furthermore, based on a data-driven model linking mosquito vector species and vector-virus traits, Evans et al (2017) have predicted that as many as 35 different mosquito species could be vectors for ZIKV.…”
Section: Keep An Open Mindmentioning
confidence: 99%