English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

An intensified and continuous West Nile virus (WNV) spread across northern Italy has been observed since 2008, which caused more than one hundred reported human infections until 2016. Veneto is one of the Italian regions where WNV is considered endemic, and the greatest intensity of circulation was observed during 2013 and 2016. By using entomological data collected across the region in those years, we calibrated a temperature-driven mathematical model through a Bayesian approach that simulates the WNV infection in an avian population with seasonal demography. We considered two alternative routes of life cycle re-activation of the virus at the beginning of each vector breeding season: in the first one the virus is maintained by infected birds, in the other by diapausing mosquitoes previously infected. Afterwards, we computed seasonal risk curves for human infection and quantified how they translate into reported symptomatic cases. According to our results, WNV is more likely to be re-activated each year via previously infected mosquitoes. The highest probability of human infection is expected to occur in August, consistently with observations. Our epidemiological estimates can be of particular interest for public health authorities, to support decisions in term of designing efficient surveillance plans and preventive measures.

West Nile virus transmission and human infection risk in Veneto (Italy): a modelling analysis