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. 2018 Dec 21;9(1):5425.
doi: 10.1038/s41467-018-07896-2.

Predicting wildlife reservoirs and global vulnerability to zoonotic Flaviviruses

Pranav S Pandit  1 , Megan M Doyle  2 , Katrina M Smart  3 , Cristin C W Young  2 , Gaylen W Drape  3 , Christine K Johnson  4
Affiliations

Predicting wildlife reservoirs and global vulnerability to zoonotic Flaviviruses

Pranav S Pandit et al. Nat Commun. .

Abstract

Flaviviruses continue to cause globally relevant epidemics and have emerged or re-emerged in regions that were previously unaffected. Factors determining emergence of flaviviruses and continuing circulation in sylvatic cycles are incompletely understood. Here we identify potential sylvatic reservoirs of flaviviruses and characterize the macro-ecological traits common to known wildlife hosts to predict the risk of sylvatic flavivirus transmission among wildlife and identify regions that could be vulnerable to outbreaks. We evaluate variability in wildlife hosts for zoonotic flaviviruses and find that flaviviruses group together in distinct clusters with similar hosts. Models incorporating ecological and climatic variables as well as life history traits shared by flaviviruses predict new host species with similar host characteristics. The combination of vector distribution data with models for flavivirus hosts allows for prediction of global vulnerability to flaviviruses and provides potential targets for disease surveillance in animals and humans.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Co-occurrence of avian and mammalian hosts among Flaviviruses. Cluster analysis of zoonotic flaviviruses according to known host taxonomic orders and their Bray-Curtis dissimilarity index. Green, red, cyan, purple, yellow, and black lines show clusters of viruses with similar host taxonomic orders (Bray-Curtis dissimilarity index < 0.4)
Fig. 2
Fig. 2
Macro-ecological traits of flavivirus hosts. Plot describing the ten most important biological and ecological host traits and their relative influence for all models predicting flavivirus hosts. YFV, yellow fever virus; ZIKV, Zika virus; WNV, West Nile virus; SLEV, St. Louis encephalitis virus; USUV, Usutu virus; TBEV, Tick-borne encephalitis virus; RBV, Rio Bravo virus; ENTV, Entebbe bat virus; DBV, Dakar bat virus; DENV, dengue virus; JEV, Japanese encephalitis virus
Fig. 3
Fig. 3
Geographical distribution of predicted flaviviral host richness. Overlapping geographical ranges of model predicted sylvatic hosts in the 95th percentile of probability for stratified models. a Yellow fever virus (YFV) and Zika virus (ZIKV). b West Nile virus (WNV), St. Louis encephalitis virus (SLEV) and Usutu virus (USUV). c Tick-borne encephalitis virus (TBEV). d Rio Bravo virus (RBV), Entebbe bat virus (ENTV) and Dakar bat virus (DBV). e Dengue virus (DENV). f Japanese encephalitis virus (JEV). Maps were generated using species distribution data from IUCN, and BirdLife International and NatureServe
Fig. 4
Fig. 4
Geographical distribution of predicted flaviviral host richness adjusted by the predicted probability. Overlapping geographical ranges accounting for associated probabilities for model predicted hosts in the 95th percentile of probability for each stratified model. a Yellow fever virus (YFV) and Zika virus (ZIKV). b West Nile virus (WNV), St. Louis encephalitis virus (SLEV) and Usutu virus (USUV). c Tick-borne encephalitis virus (TBEV). d Rio Bravo virus (RBV), Entebbe bat virus (ENTV) and Dakar bat virus (DBV). e Dengue virus (DENV). f Japanese encephalitis virus (JEV). Maps were generated using species distribution data from IUCN, and BirdLife International and NatureServe
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