Epidemiology and ecology of Usutu virus infection and its global risk
distribution
Abstract
Usutu virus (USUV) is an emerging flavivirus transmitted by mosquitoes,
with an increasing incidence of human infection and a geographic
expansion over the past decade, thereby posing a significant threat to
public health. In this study, we conducted an extensive literature
search and established a comprehensive spatiotemporal database of USUV
infections in vectors, animals, and humans worldwide. Based on which, we
explored the distribution dynamics of USUV infections and
characteristics of human infections. By employing boosted regression
trees (BRT) models, we projected the distributions of three main vectors
(Culex pipiens, Aedes albopictus, and Culiseta longiareolata) and three
main hosts (Turdus merula, Passer domesticus, and Ardea cinerea) to
obtain the mosquito index and bird index. These indices were further
incorporated as predictors into the USUV infection models, which was
conducted by using three different machine learning models, BRT, random
forest (RF), and least absolute shrinkage and selection operator (LASSO)
logistic regression model. By selecting the best models for BRT, RF, and
(LASSO) logistic regression model, and integrating them into ensemble
learning model, we achieved a decent model performance with an area
under the curve (AUC) of 0.992. The mosquito index contributed
significantly, with relative contributions estimated at 25.51%. Our
estimations revealed a potential exposure area for USUV spanning 1.80
million kmĀ² globally with approximately 1.04 billion people at risk,
this would guide future surveillance efforts for USUV infections,
especially for countries located within high-risk areas and those that
have not yet conducted surveillance activities.