Apathogenic proxies for transmission dynamics of a fatal virus

Abstract

Identifying drivers of transmission—especially of emerging pathogens—is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission—even based on analogously-transmitted, apathogenic agents—in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems.

Seasonal changes in network connectivity and consequences for pathogen transmission in a solitary carnivore

“…Alternatively, our simulated networks were predominantly single large components, rather than multiple smaller subcomponents, the latter of which was observed in our empirical panther networks and might be expected to limit the extent of outbreaks. Panthers located in the Everglades at the far southeastern extent of panther habitat can be somewhat isolated from the broader population, but pathogens from the broader population can still reach these individuals 22 . Thus, our simulated, single component networks are still representative of broader population connectivity patterns, and reinforce the findings of Sah et al 46 that the ability of modularity to mitigate epidemics appears to be strongest with extremely high subdivision of networks.…”

“…While recent research has devoted much effort to discerning drivers of transmission among panthers (e.g. 22 , 23 ), far less effort has been devoted to understanding how transmission dynamics among panthers may be subject to seasonal variations.…”