Transmission of one predicts another: Apathogenic proxies for transmission dynamics of a fatal virus

Abstract

Identifying drivers of transmission prior to an epidemic—especially of an emerging pathogen—is a formidable challenge for proactive disease management efforts. To overcome this gap, we tested a novel approach hypothesizing that an apathogenic virus could elucidate drivers of transmission processes, and thereby predict transmission dynamics of an analogously transmitted virulent pathogen. We evaluated this hypothesis in a model system, the Florida panther (Puma concolor coryi), using apathogenic feline immunodeficiency virus (FIV) to predict transmission dynamics for another retrovirus, pathogenic feline leukemia virus (FeLV). We derived a transmission network using FIV whole genome sequences, and used exponential random graph models to determine drivers structuring this network. We used the identified drivers to predict transmission pathways among panthers; simulated FeLV transmission using these pathways and three alternate modeling approaches; and compared predictions against empirical data collected during a historical FeLV outbreak in panthers. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. Prospective FIV-based predictions of FeLV transmission dynamics performed at least as well as simpler, often retrospective approaches, with evidence that FIV-based predictions could capture the spatial structuring of the observed FeLV outbreak. Our finding that an apathogenic agent can predict transmission of an analogously transmitted pathogen is an innovative approach that warrants testing in other host-pathogen systems to determine generalizability. Use of such apathogenic agents holds promise for improving predictions of pathogen transmission in novel host populations, and can thereby revolutionize proactive pathogen management in human and animal systems.Significance StatementPredicting infectious disease transmission dynamics is fraught with assumptions which limit our ability to proactively develop targeted control strategies. We show that transmission of non-disease causing (apathogenic) agents provides invaluable insight into drivers of transmission prior to outbreaks of more serious diseases. Integrating genomic and network approaches, we tested an apathogenic virus as a proxy for predicting transmission dynamics of a deadly virus in the Florida panther. We found that apathogenic virus-based predictions of pathogen transmission dynamics performed at least as well as simpler transmission models, and offered the advantage of prospectively identifying the underlying management-relevant drivers of transmission. Our innovative approach offers an opportunity to proactively design disease control strategies in at-risk animal and human populations.

Paradoxes and synergies: Optimizing management of a deadly virus in an endangered carnivore

“…To examine the effect of different disease management regimes on FeLV control, we used spatially explicit network simulations adapted from our research determining drivers of retrovirus transmission in panthers (Gilbertson et al, 2021). This approach involves two steps: (a) simulation of a contact network among panthers and (b) simulation of FeLV transmission through this network.…”

“…This approach involves two steps: (a) simulation of a contact network among panthers and (b) simulation of FeLV transmission through this network. In brief, we simulated panther populations of 150 individuals (McClintock et al, 2015) and used our previously described exponential random graph model for retrovirus transmission in panthers (Gilbertson et al, 2021) to simulate contact networks constrained by network density among simulated populations (see Supporting Information for further details and Table S1 for parameters). Simulations used previously collected panther data (see Supporting Information), and any associated free‐ranging panther captures used trained tracking hounds provided by Livestock Protection Company, as previously described (Florida Fish and Wildlife Conservation Commission, 2017).…”

Paradoxes and synergies: optimizing management of a deadly virus in an endangered carnivore

“…To examine the effect of different disease management regimes on FeLV control, we used spatially-explicit, network simulations adapted from our research determining drivers of retrovirus transmission in panthers (26). This approach involves two steps: (1) simulation of a contact network among panthers, and (2) simulation of FeLV transmission on this network.…”

“…To examine the effect of different disease management regimes on FeLV control, we used spatially explicit network simulations adapted from our research determining drivers of retrovirus transmission in panthers (Gilbertson et al, 2021). This approach involves two steps:…”

“…(1) simulation of a contact network among panthers, and (2) simulation of FeLV transmission through this network. In brief, we simulated panther populations of 150 individuals (McClintock et al, 2015) and used our previously described exponential random graph model for retrovirus transmission in panthers (Gilbertson et al, 2021) to simulate contact networks constrained by 6 network density among simulated populations (see supplementary methods for further details and Table S1 for parameters).…”