Background
Yellow fever virus (YFV) is an arbovirus that, despite the existence of a safe and effective vaccine, continues to cause outbreaks of varying dimensions in the Americas and Africa. Between 2017 and 2019, Brazil registered un unprecedented sylvatic YFV outbreak whose severity was the result of its spread into zones of the Atlantic Forest with no signals of viral circulation for nearly 80 years.
Conclusions
Together, our data suggest that entomological data and landscape composition analyses may help to predict areas permissive to yellow fever outbreaks, allowing protective measures to be taken to avoid human cases.
Methods
To investigate the influence of climatic, environmental, and ecological factors governing the dispersion and force of infection of YFV in a naïve area such as the landscape mosaic of Rio de Janeiro (RJ), we combined the analyses of a large set of data including entomological sampling performed before and during the 2017-2019 outbreak, with the geolocation of human and nonhuman primates (NHP) and mosquito infections.
Results
A greater abundance of Haemagogus mosquitoes combined with lower richness and diversity of mosquito fauna increased the probability of finding a YFV-infected mosquito. Furthermore, the analysis of functional traits showed that certain functional groups, composed mainly of Aedini mosquitoes which includes Aedes and Haemagogus mosquitoes, are also more representative in areas where infected mosquitoes were found. Human and NHP infections were more common in two types of landscapes: large and continuous forest, capable of harboring many YFV hosts, and patches of small forest fragments, where environmental imbalance can lead to a greater density of the primary vectors and high human exposure. In both, we show that most human infections (~ 62%) occurred within an 11-km radius of the finding of an infected NHP, which is in line with the flight range of the primary vectors. Conclusions: Together, our data suggest that entomological data and landscape composition analyses may help to predict areas permissive to yellow fever outbreaks, allowing protective measures to be taken to avoid human cases.