Data-driven identification of potential Zika virus vectors.

Area of Science:

• Arbovirology
• Vector-borne disease epidemiology
• Ecological network modeling

Background:

• Zika virus (ZIKV) is an emerging infectious disease with significant public health implications.
• Incomplete knowledge of Zika virus transmission dynamics hinders preparedness, particularly in non-endemic regions.
• Identifying potential mosquito vectors is crucial for predicting and controlling ZIKV spread.

Purpose of the Study:

• To develop a data-driven model for identifying unknown Zika virus vectors.
• To predict vector species capable of transmitting Zika virus based on ecological network traits.
• To assess the potential for Zika virus introduction and transmission in the continental United States.

Main Methods:

• Constructed an ecological network model linking flaviviruses and their mosquito vectors.
• Integrated vector-virus trait combinations to predict vector competence.
• Analyzed network associations to identify potential novel Zika virus vectors.

Main Results:

• The model predicted 35 mosquito species with the potential to transmit Zika virus.
• Seven of these potential vectors are present in the continental United States.
• Notable U.S. species include Culex quinquefasciatus and Culex pipiens.

Conclusions:

• The model provides a predictive framework for identifying potential Zika virus vectors.
• Prioritizing empirical studies on identified species, especially those in the U.S., is recommended.
• This research facilitates targeted surveillance and risk assessment for Zika virus transmission in the United States.