• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Monday, October 13, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

More mosquito species than previously thought may transmit Zika

Bioengineer by Bioengineer
February 28, 2017
in Science News
Reading Time: 3 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram
IMAGE

Credit: UGA

Athens, Ga. – Zika virus could be transmitted by more mosquito species than those currently known, according to a new predictive model created by ecologists at the University of Georgia and the Cary Institute of Ecosystem Studies. Their findings, published today in the journal eLife, offer a list of 26 additional potential candidate species–including seven that occur in the continental United States–that the authors suggest should be the first priority for further research.

"The biggest take home message is that these are the species that we need to prioritize," said lead author Michelle V. Evans, a UGA doctoral student in ecology and conservation. "Especially as we're in the slower part of the mosquito season, now is the time to catch up so we're prepared for the summer."

Targeting Zika's potential vectors–species that can transmit the virus from one host to another–is an urgent need, given its explosive spread and the devastating health effects associated with it. It's also time-consuming and expensive, requiring the collection of mosquitoes in affected areas, testing them to see which ones are carrying the virus, and conducting laboratory studies.

The new model could streamline the initial step of pinpointing Zika vectors.

"What we've done is to draw up a list of potential vector candidates based on the associations with viruses that they've had in the past as well as other traits that are specific to that species," said paper co-author Courtney C. Murdock, an assistant professor in the UGA School of Veterinary Medicine and Odum School of Ecology. "That allows us to have a predictive framework to effectively get a list of candidate species without having to search blindly."

The researchers developed their model using machine learning, a form of artificial intelligence that is particularly useful for finding patterns in large, complicated data sets. It builds on work done by co-author Barbara A. Han of the Cary Institute, who has used similar methods to predict bat and rodent reservoirs of disease based on life history traits.

Data used in the model consisted of information about the traits of flaviviruses–the family that includes Zika, yellow fever and dengue–and all the mosquito species that have ever been associated with them. For mosquito species, these included general traits like subgenus and geographic distribution as well as traits relevant to the ability of each species to transmit disease, such as proximity to human populations, whether they typically bite humans and how many different viruses they are known to transmit.

For viruses, traits included how many different mosquito species they infect, whether they have ever infected humans and the severity of the diseases they cause.

Analyzing known mosquito-virus pairs, the researchers found that certain traits were strong predictors of whether a linkage would form. The most important of these for mosquitoes were the subgenus, the continents it occurred on and the number of viruses it was able to transmit. For viruses, the most important trait was the number of mosquito species able to act as a vector.

Based on what they learned, they used the model to test the combination of Zika virus with all the mosquito species known to transmit at least one flavivirus. The model found 35 predicted Zika vectors, including 26 previously unsuspected possibilities.

Seven of those species occur in the continental U.S., with ranges that in some cases differ from those of the known vectors. Evans and Murdock cautioned strongly against assuming that this means that Zika will spread to all those areas.

"We're really solely looking at vector competence, which is only one small part of disease risk," Evans said. "It's one factor out of many, and not even the most important one. I want to stress that all of these are just predictions that need to be validated by empirical work. We are suggesting that people who are doing that work should focus on these species first," she said.

"Ecologists have long known that everything is connected to everything else, and are pretty good, I think, at sifting out where that matters from where it doesn't," said senior author John M. Drake, a professor in the Odum School and director of the UGA Center for the Ecology of Infectious Diseases. "This work highlights that ecological way of thinking and why it's important in understanding infectious diseases."

###

The paper's other co-author is Tad A. Dallas of the University of California-Davis.

The paper, "Data-driven identification of potential Zika virus vectors," is available online at http://dx.doi.org/10.7554/eLife.22053.

Media Contact

John M. Drake
[email protected]
706-583-5539
@universityofga

http://www.uga.edu

############

Story Source: Materials provided by Scienmag

Share12Tweet7Share2ShareShareShare1

Related Posts

Transformers Revolutionize Genome Language Model Breakthroughs

Transformers Revolutionize Genome Language Model Breakthroughs

October 13, 2025

mRNA Therapy Revives Sperm Production and Fertility in Mice

October 13, 2025

Activating Sperm Motility: A Breakthrough Offering New Hope for Male Infertility

October 13, 2025

Impact of Storage Time and Temperature on FFPE Proteomics

October 13, 2025
Please login to join discussion

POPULAR NEWS

  • Sperm MicroRNAs: Crucial Mediators of Paternal Exercise Capacity Transmission

    1233 shares
    Share 492 Tweet 308
  • New Study Reveals the Science Behind Exercise and Weight Loss

    104 shares
    Share 42 Tweet 26
  • New Study Indicates Children’s Risk of Long COVID Could Double Following a Second Infection – The Lancet Infectious Diseases

    101 shares
    Share 40 Tweet 25
  • Revolutionizing Optimization: Deep Learning for Complex Systems

    91 shares
    Share 36 Tweet 23

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Transformers Revolutionize Genome Language Model Breakthroughs

mRNA Therapy Revives Sperm Production and Fertility in Mice

Activating Sperm Motility: A Breakthrough Offering New Hope for Male Infertility

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 64 other subscribers
  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.