Periodic shifts in viral load increase risk of spillover from bats

Author:

Lunn Tamika J.ORCID,Borremans BennyORCID,Jones Devin N.ORCID,Kessler Maureen K.ORCID,Dale Adrienne S.ORCID,Yinda Kwe C.ORCID,Ruiz-Aravena ManuelORCID,Falvo Caylee AORCID,Crowley Dan,Lloyd-Smith James O.ORCID,Munster Vincent J.ORCID,Eby PeggyORCID,McCallum HamishORCID,Hudson PeterORCID,Restif OlivierORCID,McGuire Liam P.ORCID,Smith Ina L.ORCID,Plowright Raina K.ORCID,Peel Alison J.ORCID,

Abstract

AbstractPrediction and management of zoonotic pathogen spillover requires an understanding of infection dynamics within reservoir host populations. Transmission risk is often assessed using prevalence of infected hosts, with infection status based on the presence of genomic material. However, detection of viral genomic material alone does not necessarily indicate the presence of infectious virus, which could decouple prevalence from transmission risk. We undertook a multi-faceted investigation of Hendra virus shedding inPteropusbats, combining insights from virus isolation, viral load proxies, viral prevalence, and longitudinal patterns of shedding, from 6,151 samples. In addition to seasonal and interannual fluctuation in prevalence, we found evidence for periodic shifts in the distribution of viral loads. The proportion of bats shedding high viral loads was higher during peak prevalence periods during which spillover events were observed, and lower during non-peak periods when there were no spillovers. We suggest that prolonged periods of low viral load and low prevalence reflect prolonged shedding of non-infectious RNA, or viral loads that are insufficient or unlikely to overcome dose barriers to spillover infection. These findings show that incorporating viral load (or proxies of viral load) into longitudinal studies of virus excretion will better inform predictions of spillover risk than prevalence alone.Significance statementWe present a comprehensive analysis of a high-profile bat-virus system (Hendra virus in Australian flying-foxes) to demonstrate that both prevalence and viral loads can shift systematically over time, resulting in concentrated periods of increased spillover risk when prevalence and viral loads are high. We further suggest that prolonged periods of low-prevalence, low-load shedding may not reflect excretion of infectious virus, resolving the outstanding puzzle of why spillovers have not been observed during periods of low off-season prevalence in subtropical Australia, or more frequently in tropical Australia despite consistent low-prevalence shedding. The consideration of viral loads (or proxies of viral load) along with prevalence may improve risk inference from longitudinal surveys of zoonotic viruses across wildlife reservoir hosts.

Publisher

Cold Spring Harbor Laboratory

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